Deep learning-based classification methods of hyperspectral images (HSIs) have made significant progress recently, catching the attention of academia and industry; however, the existing studies of classification of HSIs mainly focus on the closed-set environment with the assumption that ground classes are fixed and known, ignoring the complexity and diversity of ground objects in the real world. As a result, the unknown classes will be forced into known classes. To solve this problem, we propose a novel spectral-spatial evidential learning (SSEL) network that combines an improved generative adversarial network (GAN) and evidential theory for open-set classification of HSIs. First, a domain adaptation (DA) strategy is embedded into GAN to generate high-quality samples by reducing the distribution discrepancy between generated and real samples. Second, the discriminator is devised to extract spectral-spatial features and output multiclass evidence for closed-set classification and uncertainty estimation. A new classification function called evidence-based loss is designed for the discriminator to guide the evidence-collection process. Additionally, a novel adversarial objective function is defined, where the discriminator loss is devised to predict real samples belonging to the true class and generated samples belonging to “none of the classes. The generator loss is developed to generate samples consistent with the label category. Finally, the class and corresponding uncertainty can be calculated based on the collected evidence and the appropriate open-set classification of HSIs. Extensive experiments on three benchmark HSIs show that our proposed method achieves competitive performance on closed-set and open-set classification of HSIs compared with existing state-of-the-art methods.

This paper proposes a hierarchical double Dirichlet process mixture model (DDPMM) for multilook polarimetric synthetic aperture radar (PolSAR) data unsupervised classification. Specifically, within the framework of product model (PM), an observed PolSAR data point can be factorized as the multiplication representation of a positive-scalar texture variable and a complex-Wishart-distributed speckle component. Based on this assumption, the polarization DPMM and texture DPMM in the proposed model are hierarchically established to characterize the polarimetric matrix and texture variable, respectively, thus yielding the generation procedure of the observation data to be learned sufficiently. Meanwhile, instead of sharing the same texture vector in many existing PM-based methods, each data point in DDPMM is associated with its own texture vector, which can be characterized as the weighted summation of several densities via texture DPMM rather than following a single distribution, such that the texture information can be fully and flexibly captured. In particular, dual local spatial constraints based on the statistical representations of polarization and texture spaces are also explored on the two DPMMs, allowing the local correlation to be adequately and dynamically incorporated. Moreover, all closed-form updates are derived with the variational Bayesian inference algorithm and the cluster number of the proposed model can be determined automatically. Experimental results on four real PolSAR datasets demonstrate the superiority of the proposed DDPMM to some state-of-the-art methods.

Deep learning models, such as convolutional neural networks (CNNs), have made significant progress in hyperspectral image (HSI) classification. However, these models require a large number of parameters, which occupy a lot of storage space and suffer from overfitting, thus resulting in performance loss. To solve the above problems, in this article, we propose a new compression network [namely, a Hybrid Fully Connected Tensorized Compression Network (HybridFCTCN)] by considering the high dimensionality of HSI data. First, using the low-rank fully connected tensor network decomposition (FCTND), three novel units, i.e., FCTN-FC, FCTNConv2D, and FCTNConv3D, are designed to compress the weight tensor of standard fully connected (FC) layer and kernel tensor of convolutional layer, reducing their parameters. In the novel units, the intrinsic correlation of the decomposed factors is adequately exploited by the FC structures, which enhances their feature extraction and classification abilities. Then, benefiting from the hybrid network backbone composed of the FCTNConv3D and FCTNConv2D units, HybridFCTCN can extract more discriminative features with fewer parameters, while it has great generalization capability and robustness, enabling better HSI classification. Finally, the rank of above-designed units is defined, and its determination is discussed to facilitate the application of the proposed model. Extensive experiments on three widely used HSI datasets reveal that the proposed model achieves state-of-the-art classification performance for different training sample sizes with a very small number of parameters.

The rapid development of hyperspectral remote sensing technology, has led to an explosion in the number of available hyperspectral images (HSI). The fast and accurate characterization of HSI poses a significant challenge for remote sensing scientists. Currently, deep learning strategies with various neural networks have been successfully applied for HSI classification using the concept of the “dataset-model”. Still, there is a need to develop universal deep learning models for HSI classification using a continual updating strategy. This paper presents a life-long learning strategy to continually update model weights with the help of continual spectral-spatial feature distillation. Specifically, the proposed method introduces a spectral-spatial distillation strategy to retain knowledge of the previous well-trained model. Meanwhile, the learning metric term is integrated into a multi-level feature extraction to minimize the spectral-spatial feature discrepancy between the previous model and the new one. The experimental results indicate that our method achieves superior performance for continual HSI classification tasks without suffering from the persistent loss of characterization memory.

Coastal marine debris poses a serious threat to marine life and impacts fisheries and coastal tourism. This study describes a method that uses high-resolution satellite imagery to locate marine debris in a coastal area. We use the combination of in situ data from local coastal debris cleanup efforts together with nearly coincident high-resolution satellite imagery analyzed using a segmentation-based approach to isolate debris from other materials. We applied Shannon's entropy method, which represents the uncertainties of the segmentation results to obtain spectral signatures for individual semantic features. Then, we demonstrated the robustness of these features by designing a simple classification model to estimate the density of marine debris directly from a satellite image.

In recent years, deep learning models have shown great advantages for hyperspectral images (HSIs) classification, in which long short-term memory (LSTM) has attracted plenty of attentions for its characteristic of modeling long-range dependencies. However, for the 2-D extended architecture of it (namely 2-D convolutional LSTM, ConvLSTM2D), it is the special gate structures of ConvLSTM2D that leads to a large number of training parameters and high requirements for device storage. To address this shortcoming, in this paper, a lightweight ConvLSTM2D cell is developed by using tensor-train decomposition (TTD) for the compression of training parameters, which is named TT-ConvLSTM2D and further applied to two state-of-the-art ConvLSTM2D-based HSI classification models for verifying its superiority. Experiments on a widely-used Indian Pines HSI data set are conducted, whose results demonstrate that the proposed TT-ConvLSTM2D cell can effectively reduce the number of the parameters and memory requirements of the whole models within a small range of accuracy degradation.

Change detection by comparing two bitemporal images is one of the most fundamental challenges for dynamic monitoring of the Earth surface. In this article, we propose a metric learning-based generative adversarial network (GAN) (MeGAN) to automatically explore seasonal invariant features for pseudochange suppressing and real change detection. To achieve this purpose, a seasonal invariant term is introduced to maximally suppress pseudochanges, whereas the MeGAN explores the transition patterns between adjacent images in a self-learning fashion. Different from the previous works on bitemporal imagery change detection, the proposed MeGAN have the following contributions: 1) it automatically explores change patterns from the complex bitemporal background without human intervention and 2) it aims to maximally exclude pseudochanges from the seasonal transition term and map out real changes efficiently. To our best knowledge, this is the first time we incorporate the seasonal transition term and GAN for change detection between bitemporal images. At last, to demonstrate the robustness of the proposed method, we included two data sets which are the Google Earth data and the Landsat data, for bitemporal change detection and evaluation. The experimental results indicated that the proposed method is able to perform change detection with precision can be as high as 81% and 88% for the Google Earth and Landsat data set, respectively.

Passive microwave remote sensing of the Earth is facing a number of current and future threats. They come from planned uses of the Radio Frequency (RF) spectrum by telecommunication and other services that, if approved by national and international regulatory commissions on frequency allocation, will greatly increase the possibility of Radio Frequency Interference (RFI) affecting bands that are critical for the remote sensing of the Earth surface and of its atmosphere. CORF is a body of the U.S. National Academy of Sciences able to file position statements with regulatory agencies. Thse filings are often successful in gaining some adjustments to reduce the potential for RFI in frequency bands critical for microwave Earth remote sensing. In this paper we discuss some of the present threats to passive microwave remote sensing of the Earth and how CORF is filing position statements to protect the use of these frequencies for Earth remote sensing and also for the operation of radio telescopes.

In January 2019 a Project Authorization Request was submitted to the IEEE standards association with the title "Standard for Calibration of Microwave Radiometers in the 300 MHz to 1 THz Frequency Range for Geoscience Applications". An open committee is being assembled to draft this standard with the purpose of unifying and documenting calibration procedures for a wide range of microwave radiometers. The committee includes members, collaborators, and contributors from academia, international government and private industry. We include ground-based, air-borne, and space-borne systems. The standard will also define standardized terminology, and address procedures required to obtain traceability to fundamental units or constants. The scope of the standard encompasses various radiometer geometries, Dicke switching, total power, and differential, as well as different polarization configurations including fully polarized (full Stoke's) radiometers. The standard will also separately address free-space and single-mode (e.g. transmission-line) radiometer calibration techniques.

In this letter, we propose an unsupervised change detection method for synthetic aperture radar (SAR) images based on variational multivariate Gaussian mixture model (MGMM) and Shannon entropy. First, the difference features are generated from the Gabor wavelet transform of two SAR images. In variational inference framework, the variational MGMM is first introduced to implement accurate modeling for the data distribution of difference features and to output responsibilities. Subsequently, spatial information is explored on the responsibilities to yield thecontextual responsibilitiesfor improving the accuracy and reliability of change detection. Then,a posterioriprobabilities of the changed and unchanged classes are derived from thecontextual responsibilities, and Shannon entropy, being directly related to the classification error rate, is proposed to determine the optimal index integer. Finally, the binary change mask is achieved by separating the pixels into the changed and unchanged classes. The experiments on three pairs of SAR images for describing urban sprawl and water bodies demonstrate the effectiveness of the proposed method.

In this paper, we propose a novel unsupervised change detection method of remote sensing (RS) images based on a unified framework for weighted collaborative representation (WCR) with robust deep dictionary learning (RDDL) and fuzzy clustering. Specifically, WCR is employed to collaboratively represent neighborhood features with lower computational complexity, for which the RDDL model is built to learn more effective and representative overcomplete dictionary and enhance the robustness against the noise and outliers. Meanwhile, in order to make the resulting collaborative coefficients more beneficial for clustering, the unified framework for WCR with RDDL and fuzzy clustering is designed. By doing so, our framework not only precludes the utilization of third-party clustering algorithm, but also achieves better detection performance. Subsequently, the spatial constraint is enforced on the membership matrix to yield the updated one for further improving the accuracy of change detection. Finally, a binary change mask (CM) is achieved by assigning the pixels into the changed and unchanged classes. Experiments are performed on five pairs of RS images, and experimental results demonstrate the effectiveness of the proposed method.

This paper proposes an unsupervised classification method for multilook polarimetric synthetic aperture radar (Pol-SAR) data. The proposed method simultaneously deals with the heterogeneity and incorporates the local correlation in PolSAR images. Specifically, within the probabilistic framework of the Dirichlet process mixture model (DPMM), an observed PolSAR data point is described by the multiplication of a Wishartdistributed component and a class-dependent random variable (i.e., the textual variable). This modeling scheme leads to the proposed textured DPMM (tDPMM), which possesses more flexibility in characterizing PolSAR data in heterogeneous areas and from high-resolution images due to the introduction of the classdependent texture variable. The proposed tDPMM is learned by solving an optimization problem to achieve its Bayesian inference. With the knowledge of this optimization-based learning, the local correlation is incorporated through the pairwise constraint, which integrates an appropriate penalty term into the objective function so as to encourage the neighboring pixels to fall into the same category and to alleviate the "salt-and-pepper" classification appearance.We develop the learning algorithm with all the closed-form updates. The performance of the proposed method is evaluated with both low-resolution and high-resolution PolSAR images, which involve homogeneous, heterogeneous, and extremely heterogeneous areas. The experimental results reveal that the class-dependent texture variable is beneficial to PolSAR image classification and the pairwise constraint can effectively incorporate the local correlation in PolSAR images.

In the paper, an unsupervised change detection method for remote sensing (RS) images based on deep semi-nonnegative matrix factorization (semi-NMF) is proposed. Firstly, the difference image is generated in different ways, depending on the types of input images. Then principal component analysis (PCA) is applied on the difference image to form the feature matrix X for improving the capability against various noise. In order to exploit more useful information from the resulting feature matrix, deep semi-NMF is introduced to factorize X into L+1 factors consisting of L nonrestricted matrices {Fl}l=1Land nonnegative cluster indicator matrix GL. Finally, the binary change mask (CM) is generated by assigning the pixels into changed and unchanged classes according to maximum criterion. The experimental results on two pairs of multitemporal RS images demonstrate the effectiveness of the proposed method.

By considering the cubic nature of hyperspectral image (HSI) to address the issue of the curse of dimensionality, we have introduced a tensor locality preserving projection (TLPP) algorithm for HSI dimensionality reduction and classification. The TLPP algorithm reveals the local structure of the original data through constructing an adjacency graph. However, the hyperspectral data are often susceptible to noise, which may lead to inaccurate graph construction. To resolve this issue, we propose a modified TLPP (MTLPP) via building an adjacency graph on a dual feature space rather than the original space. To this end, the region covariance descriptor is exploited to characterize a region of interest around each hyperspectral pixel. The resulting covariances are the symmetric positive definite matrices lying on a Riemannian manifold such that the Log-Euclidean metric is utilized as the similarity measure for the search of the nearest neighbors. Since the defined covariance feature is more robust against noise, the constructed graph can preserve the intrinsic geometric structure of data and enhance the discriminative ability of features in the low-dimensional space. The experimental results on two real HSI data sets validate the effectiveness of our proposed MTLPP method.

Recently, low-rank embedding (LRE) has yielded satisfactory results in dimensionality reduction (DR), for which low-rank representation and projection learning are integrated into one model to generate robust low-dimensional features. However, LRE requires to convert samples into vectors even if the data naturally appear in high-order form. Furthermore, LRE fails to take the label information into consideration. To address these problems, this paper proposes a novel supervised DR method based on multilinear algebra, i.e., the algebra of tensors. By the motivation of extending LRE into tensor space and simultaneously combining the tensor discriminant analysis, we establish tensor low-rank discriminant embedding (TLRDE) model for hyperspectral image (HSI) DR. The model of TLRDE is solved by an alternative iteration algorithm, whose convergence is also mathematically proven. The proposed TLRDE method employs the tensor representation to preserve the intrinsic geometrical structure, uses low-rank reconstruction to uncover the potential relationship among the data points, and combines label information to enhance the discriminability of features. Moreover, the proposed TLRDE does not suffer from the small sample size problem. The experimental results on three real HSI data sets validate the effectiveness of our proposed TLRDE method.

Hyperspectral unmixing is an important processing step for many hyperspectral applications, mainly including: 1) estimation of pure spectral signatures (endmembers) and 2) estimation of the abundance of each endmember in each pixel of the image. In recent years, nonnegative matrix factorization (NMF) has been highly attractive for this purpose due to the nonnegativity constraint that is often imposed in the abundance estimation step. However, most of the existing NMF-based methods only consider the information in a single layer while neglecting the hierarchical features with hidden information. To alleviate such limitation, in this paper, we propose a new sparsity-constrained deep NMF with total variation (SDNMF-TV) technique for hyperspectral unmixing. First, by adopting the concept of deep learning, the NMF algorithm is extended to deep NMF model. The proposed model consists ofpretraining stageandfine-tuning stage, where the former pretrains all factors layer by layer and the latter is used to reduce the total reconstruction error. Second, in order to exploit adequately the spectral and spatial information included in the original hyperspectral image, we enforce two constraints on the abundance matrix. Specifically, the$L_{1/2}$constraint is adopted, since the distribution of each endmember is sparse in the 2-D space. The TV regularizer is further introduced to promote piecewise smoothness in abundance maps. For the optimization of the proposed model, multiplicative update rules are derived using the gradient descent method. The effectiveness and superiority of the SDNMF-TV algorithm are demonstrated by comparing with other unmixing methods on both synthetic and real data sets.

We discuss the analytical derivation of the absolute brightness temperature uncertainty of a hollow conical blackbody source for radiometer calibration. We introduce a Monte Carlo uncertainty propagation analysis method to quantify uncertainty contributions from nonideal emissivity, physical temperature, and antenna pattern, which are the three major factors contributing to the uncertainty of the brightness temperature radiation from the source. The low reflectance of the hollow conical geometry depends on multiple bounces on the absorber surface. To quantify total brightness temperature over a nonuniform temperature surface, each individual bounce must be considered. We derive a recursive analytical relationship to quantify this multiple-bounce effect as a function of a view angle. The resulting effective blackbody brightness temperature uncertainty is a function of frequency, temperature, antenna pattern, measurement distance, and the measurement environment. We also propagate the uncertainty to the antenna flange of a radiometer viewing the conical blackbody. This includes additional effects, such as spillover, illumination efficiency, and antenna efficiency. We demonstrate the propagation method with an example case. We use fictional input values to investigate the response of the uncertainty to input variables and distance. We find that as the distance between antenna and blackbody increases, the uncertainty due to spillover dominates, but at close distances, the dominant uncertainty contributor is linked to the physical temperature of the absorber.

The high-frequency hybrid sky-surface wave radar (HF HSSWR) has recently been used to monitor large-area sea states. However, most of the HF HSSWR detection methods are based on the assumption of a no-tilt and constant height ionospheric model, and the influences caused by uneven electron density are ignored. This paper proposes a new surface current inversion scheme for the HF distributed HSSWR system, which considers the unknown ionospheric state as a black box and extracts the key parameters to compute the surface current based on a scattering model. The computational formula of the component of the current vector is explored using spatial scattering theory instead of an approximate bistatic model. In addition, the Fourier series expansion method is applied to the HF data to extract the real first-order Bragg frequency. Subsequently, the grazing angle and the bistatic angle can be found by inversion using the first-order Bragg frequency formula after searching out the common scattering patch of two receiving stations. Simultaneously, the coordinate registration of the currents can also be determined. The feasibility and effectiveness of this new algorithm are verified with field experimental results by comparing the current vectors derived from HSSWR and traditional HF SWR. The RMS differences of the magnitude and direction of the current vectors within the core common area of the two detection systems are about 10.2 cm/s and 9.5°, respectively.

Different spectral, spatial, and temporal features have been widely used in the remote-sensing image analysis. The further development of multiple sensor remote-sensing technologies has made it necessary to explore new methods of remote-sensing image fusion using different optical image data sets which provide complementary image properties and a tradeoff among spatial, spectral, and temporal resolutions. However, due to problems in assessing correlations between different types of satellite data with different resolutions, a few efforts have been made to explore spatio-spectral–temporal features. For this purpose, we propose a novel sparse representation model to generate synthesized frequent high-spectral and high-spatial resolution data by blending multiple types: spatio-temporal data fusion, spectral–temporal data fusion, spatio-spectral data fusion, and spatio-spectral–temporal data fusion. The proposed method exploits high-spectral correlation across spectral domains and high self-similarity across spatial domains to learn the spatio-spectral fusion basis. Then, it associates temporal changes using a local constraint sparse representation. The integrated spatio-spectral–temporal sparse representation model based on the learned spectral–spatial and temporal change features strengthens the model’s ability to provide high-resolution data needed to address demanding work in real-world applications. Finally, the proposed method is not restricted to a certain type of data, but it can associate any type of remote-sensing data and be applied to dynamic changes in heterogeneous landscapes. The experimental results illustrate the effectiveness and efficiency of the proposed method.

By considering the cubic nature of hyperspectral image (HSI) and to address the issue of the curse of dimensionality, we introduce a tensor locality preserving projection (TLPP) algorithm for HSI classification. TLPP has been proved to be effective in preserving the geometrical structure of data for dimensionality reduction. More importantly, data can be taken directly in the form of a tensor of arbitrary order as input, such that the damage to sample's geometrical structure is avoided during vectorizing. For the HSI classification, TLPP can effectively embed both spatial structure and spectral information into low-dimensional space simultaneously by a series of projection matrices trained for each mode of input samples. The experimental results on the AVIRIS hyperspectral image confirm the effectiveness of TLPP.

Polarimetric synthetic aperture radar (PolSAR) is an advanced imaging radar system, for which the acquired data provide not only the information of each channel but also the correlation between channels. To fully utilize and accurately model the multilook PolSAR data, a novel compound distribution, named the H distribution, is proposed based on the generalized Fisher distribution (GFD). Specifically, the GFD introduces a power parameter to the ordinary Fisher distribution. With one more free parameter, the GFD is flexible and versatile enough to characterize different kinds of texture. Then, by assuming the generalized-Fisher-distributed texture and the Wishart-distributed speckle, the H distribution is derived, whose closed-form expression is obtained with the help of Fox's H-function. As such, the H distribution has a compact form and is conveniently applied to practical problems, such as modeling and classification of PolSAR data. The effectiveness of this method is tested by modeling the multilook PolSAR data and performing image classification. The experimental results demonstrate that the H distribution is a flexible and effective way to model multilook PolSAR data.

In this letter, a low-rank and sparse representation classifier with a spectral consistency constraint (LRSRC-SCC) is proposed. Different from the SRC that represents samples individually, LRSRC-SCC reconstructs samples jointly and is able to capture the local and global structures simultaneously. In this proposed classifier, an adaptive spectral constraint is imposed on both the low-rank and sparse terms so as to better reveal the data structure and enhance its discriminative power. In addition, the alternating direction method is introduced to solve the underlying minimization problem, in which, more importantly, the subobjective function associated with the low-rank term is optimized based on the rank equivalence between a matrix and its Gram matrix, resulting in a closed-form solution. Finally, LRSRC-SCC is extended to LRSRC-SCCE for fully exploiting the spatial information. Experimental results on two hyperspectral data sets demonstrate that the proposed LRSRC-SCC and LRSRC-SCCE methods outperform some state-of-the-art methods.

Spectral unmixing is an important technique in hyperspectral image applications. Recently, sparse regression has been widely used in hyperspectral unmixing, but its performance is limited by the high mutual coherence of spectral libraries. To address this issue, a new sparse unmixing algorithm, called double reweighted sparse unmixing and total variation (TV), is proposed in this letter. Specifically, the proposed algorithm enhances the sparsity of fractional abundances in both spectral and spatial domains through the use of double weights, where one is used to enhance the sparsity of endmembers in spectral library, and the other is introduced to improve the sparsity of fractional abundances. Moreover, a TV-based regularization is further adopted to explore the spatial-contextual information. As such, the simultaneous utilization of both double reweighted l1minimization and TV regularizer can significantly improve the sparse unmixing performance. Experimental results on both synthetic and real hyperspectral data sets demonstrate the effectiveness of the proposed algorithm both visually and quantitatively.

We consider estimating the direction-of-arrival (DOA) in the presence of sensor array error. In the proposed method, a blind signal separation method, the joint approximation and diagonalization of eigenmatrices algorithm, is implemented to separate the signal vector and the mixing matrix consisting of the array manifold matrix and the sensor array error matrix. Based on a new mixing matrix and the reconstruction of the array output vector of each individual signal, we propose a novel DOA estimation and sensor array error calibration procedure. This method is independent of array phase errors and performs well against difference of SNR of signals. Numerical simulations verify the effectiveness of the proposed method.

A conical cavity has been designed and fabricated for use as a broadband passive microwave calibration source, or blackbody, at the National Institute of Standards and Technology. The blackbody will be used as a national primary standard for brightness temperature and will allow for the prelaunch calibration of spaceborne radiometers and calibration of ground-based systems to provide traceability among radiometric data. The conical geometry provides performance independent of polarization, minimizing reflections, and standing waves, thus having a high microwave emissivity. The conical blackbody has advantages over typical pyramidal array geometries, including reduced temperature gradients and excellent broadband electromagnetic performance over more than a frequency decade. The blackbody is designed for use between 18 and 230 GHz, at temperatures between 80 and 350 K, and is vacuum compatible. To approximate theoretical blackbody behavior, the design maximizes emissivity and thus minimizes reflectivity. A newly developed microwave absorber is demonstrated that uses cryogenically compatible, thermally conductive two-part epoxy with magnetic carbonyl iron (CBI) powder loading. We measured the complex permittivity and permeability properties for different CBI-loading percentages; the conical absorber is then designed and optimized with geometric optics and finite-element modeling, and finally, the reflectivity of the resulting fabricated structure is measured. We demonstrated normal incidence reflectivity considerably below -40 dB at all relevant remote sensing frequencies.

One of the significant challenges in physical oceanography is getting an adequate space/time description of the ocean surface currents. One possible solution is the maximum cross-correlation (MCC) method that we apply to hourly ocean color images from the Geostationary Ocean Color Imager (GOCI) over five years. Since GOCI provided a large number of image pairs, we introduce a new MCC search strategy to improve the computational efficiency of the MCC method saving 95% of the processing time. We also use an MCC current merging method to increase the total spatial coverage of the currents, proving a 25% increase. Five-year mean and seasonal time-average flows are computed to capture the major currents in the area of interest. The mean flows investigate the Kuroshio path, support the triple-branch pattern of the Tsushima Warm Current (TC), and reveal the origin of the TC. The evolution of a warm core ring shed by the Kuroshio near the northeast coast of Honshu, Japan, is clearly depicted by a sequence of three monthly MCC composites. We capture the evolution of the Kuroshio meander over seasonal, monthly, and weekly time scales. Three successive weekly MCC composite maps demonstrate how a large anticyclonic eddy, to the south of the Kuroshio meander, influences its formation and evolution in time and space. The unique ability to view short space/time scale changes in these strong current systems is a major benefit of the application of the MCC method to the high spatial resolution and rapid refresh GOCI data.

Image reranking is a popular postprocessing method for remote sensing image retrieval (RSIR), which aims at enhancing the initial retrieval performance. In general, it takes either users' opinions or the relationships between images into consideration to find an optimal reranked list based on the initial retrieved results. In this paper, we present a reranking method for improving RSIR, which is named two-stage reranking (TSR). Suppose the k-nearest neighbors of a query RS image have been obtained by the initial retrieval. The first step of our TSR is to edit these neighbors using the editing scheme. A handful of informative and representative RS images are selected by the active learning algorithm, and their binary labels are provided by the users relative to the query image. Then, a binary classifier is trained using the selected RS images and their labels to classify the rest of the neighbors. Finally, both classification results and rank information in the initial retrieval results are considered to decide which neighbor should be excluded. In the next step, the remaining RS images are reranked by the proposed reranking scheme, i.e., multisimilarity fusion reranking. Both the user's experience and image relationships are taken into account in TSR to ensure the performance of the reranking. The efficiency and the robustness of our method are validated by experiments conducted on two different types of RS images. Compared with the existing visual reranking approaches, our method achieves improved performance.

The accurate statistical modeling of synthetic aperture radar (SAR) images is a crucial problem in the context of effective SAR image processing, interpretation, and application. In this paper, a semi-parametric approach is designed within the framework of finite mixture models based on the generalized Gamma distribution in view of its flexibility and compact form. Specifically, we develop a generalized Gamma mixture model to implement an effective statistical analysis of high-resolution SAR images and prove the identifiability of such mixtures. A low-complexity unsupervised estimation method is derived by combining the proposed histogram-based expectation-conditional maximization algorithm and the Figueiredo-Jain algorithm. This results in a numerical maximum-likelihood (ML) estimator that can simultaneously determine the ML estimates of component parameters and the optimal number of mixture components. Finally, the state-of-the-art performance of this proposed method is verified by experiments with a wide range of high-resolution SAR images.

In this letter, we propose a simple yet effective unsupervised change detection approach for multitemporal synthetic aperture radar images from the perspective of clustering. This approach jointly exploits the robust Gabor wavelet representation and the advanced cascade clustering. First, a log-ratio image is generated from the multitemporal images. Then, to integrate contextual information in the feature extraction process, Gabor wavelets are employed to yield the representation of the log-ratio image at multiple scales and orientations, whose maximum magnitude over all orientations in each scale is concatenated to form the Gabor feature vector. Next, a cascade clustering algorithm is designed in this discriminative feature space by successively combining the first-level fuzzy c-means clustering with the second-level nearest neighbor rule. Finally, the two-level combination of the changed and unchanged results generates the final change map. Experimental results are presented to demonstrate the effectiveness of the proposed approach.

In this paper, we propose an unsupervised change detection approach for the multitemporal remote sensing images based on semi-nonnegative matrix factorization (semi-NMF). Specifically, the multitemporal source images, acquired at the same geographical area but at two different time instances, are first utilized to generate the difference image. Then, feature vector is created for each pixel of the difference image in such a way that its corresponding h × h block data is projected on the generated eigenvector space by principal component analysis (PCA), which is further arranged as a column vector to form a feature-by-item data matrix X. Next, we implement semi-NMF to factorize X into two nonnegative factors (i.e., the basis matrix F and the coefficient matrix G). Finally, the change detection is achieved by discriminating each column of GTaccording to the maximum criterion. Experimental results verify the feasibility and effectiveness of the proposed approach.

The quantity and the updating time of the archives of very high spatial resolution visible and near-infrared remote sensing images for commercial use improved during the last years. This led to the detection of changes on the Earth surface through remote sensing images to become a key analytical tool for many public and private organizations, which can take advantage of the information carried out to help and improve their decision making processes. This paper proposes an unsupervised method for detecting multiple changes in the application to damage assessment after a flood. It is composed of five steps, and is based on a change vector analysis approach. After a case-specific feature extraction stage, through a process called normalized difference indexing, the change detection task is carried out by modeling the classes of changed and not changed pixels with a Gaussian finite mixture model, using the expectation-maximization algorithm to estimate the statistical parameters involved. Then, the mean shift clustering algorithm is used to discriminate among different types of change. The method has been tested on a pair of images acquired by WorldView-2 and associated with the 2013 flood in Colorado.

A calibrated radiometer has been developed to enable the collection of accurate infrared measurements of sea surface temperature (SST) from unmanned aerial vehicles (UAVs). A key feature of this instrument is that in situ calibration is achieved with two built-in blackbodies (BBs). The instrument is designed so that the 2-D microbolometer array produces infrared images incremented as the aircraft travels, resulting in a well-calibrated strip of SST. Designed to be carried by medium-class UAVs, the Ball Experimental SST (BESST) instrument has been also successfully flown on manned aircraft. A recent intercalibration of BESST was carried out at the University of Miami using their National Institute of Standards and Technology traceable water-bath BB and a Fourier transform interferometer, the Marine-Atmospheric Emitted Radiance Interferometer (M-AERI). The characterization of the BESST instrument with the Miami BB demonstrates the linearity and precision of the response of the microbolometer-based radiometer. Coincident measurements of SST from a nearby pier clearly demonstrated the excellent performance of the BESST instrument with a mean SST equal to that of the M-AERI and an RMS of 0.14 K very close to the microbolometer's advertised precision of 0.1 K. Cold calibration was not possible in Miami due to condensation, but a Ball BB was characterized relative to the Miami water-bath BB, and calibrations were made in Boulder at lower temperatures than were possible in Miami. The BESST instrument's performance remained linear, and the mean and RMS values did not change. UAV flights were conducted in summer/fall of 2013 over the Alaskan Arctic.

The analysis of multitemporal very high spatial resolution imagery is too often limited to the sole use of pixel digital numbers which do not accurately describe the observed targets between the various collections due to the effects of changing illumination, viewing geometries, and atmospheric conditions. This paper demonstrates both qualitatively and quantitatively that not only physically based quantities are necessary to consistently and efficiently analyze these data sets but also the angular information of the acquisitions should not be neglected as it can provide unique features on the scenes being analyzed. The data set used is composed of 21 images acquired between 2002 and 2009 by QuickBird over the city of Denver, Colorado. The images were collected near the downtown area and include single family houses, skyscrapers, apartment complexes, industrial buildings, roads/highways, urban parks, and bodies of water. Experiments show that atmospheric and geometric properties of the acquisitions substantially affect the pixel values and, more specifically, that the raw counts are significantly correlated to the atmospheric visibility. Results of a 22-class urban land cover experiment show that an improvement of 0.374 in terms of Kappa coefficient can be achieved over the base case of raw pixels when surface reflectance values are combined to the angular decomposition of the time series.

In the last few years, active learning has been gaining growing interest in the remote sensing community in optimizing the process of training sample collection for supervised image classification. Current strategies formulate the active learning problem in the spectral domain only. However, remote sensing images are intrinsically defined both in the spectral and spatial domains. In this paper, we explore this fact by proposing a new active learning approach for support vector machine classification. In particular, we suggest combining spectral and spatial information directly in the iterative process of sample selection. For this purpose, three criteria are proposed to favor the selection of samples distant from the samples already composing the current training set. In the first strategy, the Euclidean distances in the spatial domain from the training samples are explicitly computed, whereas the second one is based on the Parzen window method in the spatial domain. Finally, the last criterion involves the concept of spatial entropy. Experiments on two very high resolution images show the effectiveness of regularization in spatial domain for active learning purposes.

As compared with conventional methods for measuring ocean surface currents, spaceborne synthetic aperture radar (SAR) offers cloud-penetrating ocean-current observation capability at high spatial resolution. While some studies have shown the potential of SAR for studying ocean surface currents through feature tracking, they have only analyzed a few images to demonstrate the basic measurement technique, and no concise general technique has been developed. This paper shows the application of the maximum cross correlation (MCC) method to generate ocean surface currents from nearly two years of available sequential spaceborne C-band SAR imagery from the Envisat ASAR and ERS-2 Advanced Microwave Instrument SAR sensors over the coastal California Current System. The data processing strategies are discussed in detail, and results are compared with HF radar measured currents. One-dimensional wavenumber spectra of the SAR-derived surface currents agree with the k-2power law, as predicted by sub mesoscale resolution models. Comparisons with HF radar currents show encouraging results with MCC SAR vectors oriented slightly counterclockwise relative to HF radar vectors. MCC SAR surface currents are found to have larger magnitudes than HF radar currents (≈11 cm/s), which may be due to the fact that SAR penetrates only a few centimetres into the ocean surface whereas HF radar currents are averaged over the top 1 m of the ocean surface. The larger part of this magnitude difference is contained in the along-shore component, which can be attributed to higher HF radar accuracy in the direct radial cross-shore measurements as compared with along-shore components derived from multiple cross-shore radial measurements.

Measuring sea surface height (SSH) using satellite altimetry in coastal ( from coasts) and shallow water region has long been a challenge since the radar altimeter waveforms are often contaminated by complex coastal topography and do not conform to theoretical Brown waveform shapes. The land contamination or surface variation due to ocean dynamics induce spurious peaks in altimeter waveforms that deviate from Brown's theoretical model as the altimeter footprint approaches or leaves the shoreline. These spurious peaks should be mitigated to minimize the error in the determination of the leading edge and associated track offset in the waveform retracking process. Here, we introduce a novel algorithm to modify coastal waveforms (0.5-7 km from coasts, using 20 Hz altimetry data), thus improving coastal data coverage and accuracy. We apply our processing algorithm and use various retrackers to compare retrieved coastal SSHs in four study regions in North America, using both Envisat and Jason-2 altimetry. The retrieved altimetry data in the 1-7 km coastal zone indicate that the 20% Threshold retracker with modified waveform has a RMSE of 21 cm as compared with in situ tide gauge data, which corresponds to a 63% improvement in accuracy compared to the use of the original deep-ocean waveform retracker.

Multi-temporal multi-angle data provides multiple images, collected over both time and satellite view-angle, of a single target area. In the presented research, these data are used to explore the fundamental physical distortions present in very-high spatial resolution optical data as applied to land-use/land-cover classification. This is done by creating a land-use/land-cover model in one image of the multi-temporal multi-angle data and directly applying it to the remaining images. This direct measure of model portability provides unique insight into the dependence of very-high spatial resolution land-use/land-cover classification on the atmosphere, solar illumination, and model training location.

Despite the fact that commercial optical very high spatial resolution satellite imagery has been available for more than 10 years, very little research has been done to take advantage of its multi-temporal and multi-angular information. In this paper, the benefits of using surface reflectance for the analysis of multi-temporal and multi-angular images are discussed using a 23 image time-series acquired between 2002 and 2010 by QuickBird and WorldView-2 over the city of Denver, Colorado. Results show that it is possible to extract useful information from multi-angular data regarding the structure of specific objects.

In the framework of the monitoring of structures and infrastructures from environmental disasters, the COSMO-SkyMed constellation has a huge potential, thanks to up to metric spatial resolution, short revisit time, and the day/night all-weather acquisition capability ensured by SAR. This paper focuses on the scientific results of the project “Development and validation of multitemporal image analysis methodologies for multirisk monitoring of critical structures and infrastructures,” funded by the Italian Space Agency. Several change-detection, data-fusion, and feature-extraction techniques, which were developed and experimentally validated in the project for COSMO-SkyMed imagery and for their integration with other data sources (including very high resolution optical data), are described and examples of processing results are discussed.

A suite of sensors has been assembled to map surface elevation and topography with fine resolution from small unmanned aircraft systems. The sensor package consists of a light detecting and ranging (LIDAR) instrument, an inertial measurement unit (IMU), a Global Positioning System (GPS) module, and digital still and video cameras. The system has been utilized to map ice sheet topography in Greenland and to measure sea ice freeboard and roughness in Fram Strait off the coast of Svalbard and in the Southern Ocean near McMurdo, Antarctica. The elevation measurement accuracy is found to be <; 10 cm (1σ) when short-baseline differential GPS processing is used to position the aircraft, and IMU data are used to correct for off-nadir pointing of the LIDAR. The system is optimized to provide coincident surface topography measurements and imagery of ice sheets, glaciers, and sea ice, and it has the potential to become a widely distributed observational resource to complement manned-aircraft and satellite missions.

The high-performance camera control systems carried aboard the DigitalGlobe WorldView satellites, WorldView-1 and WorldView-2, are capable of rapid retargeting and high off-nadir imagery collection. This provides the capability to collect dozens of multiangle very high spatial resolution images over a large target area during a single overflight. In addition, WorldView-2 collects eight bands of multispectral data. This paper discusses the improvements in urban classification accuracy available through utilization of the spatial and spectral information from a WorldView-2 multiangle image sequence collected over Atlanta, GA, in December 2009. Specifically, the implications of adding height data and multiangle multispectral reflectance, both derived from the multiangle sequence, to the textural, morphological, and spectral information of a single WorldView-2 image are investigated. The results show an improvement in classification accuracy of 27% and 14% for the spatial and spectral experiments, respectively. Additionally, the multiangle data set allows the differentiation of classes not typically well identified by a single image, such as skyscrapers and bridges as well as flat and pitched roofs.

The eight papers in this special section focus on space technology.

Active learning process represents an interesting solution to the problem of training sample collection for the classification of remote sensing images. In this work, we propose a criterion based on the spatial information that can be used in combination with a spectral criterion in order to improve the selection of training samples. Experimental results obtained on a very high resolution image show the effectiveness of regularization in spatial domain and open challenging perspectives for terrain campaigns planning.

This paper illustrates a procedure for the retrieval of tropospheric profiles (temperature, pressure, and humidity) using only refractivity profiles coming from Global Positioning System (GPS)-low-Earth-orbit radio occultation, without the constraint of independent knowledge of atmospheric parameters at each GPS occultation. In order to achieve this goal, we have used an approach based on neural networks (NNs), exploiting a data set of 1106 occultations collected over the Arctic region during the winter season of 2007 and 2008. Total refractivity (N) profiles from Formosa Satellite 3 (FORMOSAT-3)/Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) satellites have been used as input for training the NNs, whereas the target profiles of dry and wet components (Ndand Nw) were derived using prior information on dry and wet fractions of the total refractivity provided by the analysis of the European Centre for Medium-Range Weather Forecast (ECMWF). Once we have retrieved Ndand Nwby the trained networks, the other atmospheric parameters (pressure, temperature, and vapor) can be computed, and we have done so relative to colocated ECMWF data, which we have assumed as atmospheric truth. Finally, some comparisons with radiosonde observations (RAOBs) are shown, and performances and potential of the proposed approach are discussed. Profiles computed using 1-D variational retrieval by the COSMIC Data Analysis and Archive Center have also been considered as a benchmark in the RAOB comparison.

In this paper, we investigate the performance of pulse-coupled neural networks (PCNNs) to detect the damage caused by an earthquake. PCNN is an unsupervised model in the sense that it does not need to be trained, which makes it an operational tool during crisis events when it is crucial to produce damage maps as soon as the post-event images are available. The damage map resulting from PCNN was validated at a block scale of 120×120m using ground truth obtained by a combination of ground survey and visual inspection of the before- and after-event images. The comparison showed agreement between the change measured by PCNN on block scale and the damage occurred.

In the above titled paper (ibid., vol. 47, no. 7, pp. 2218-2232, Jul. 09), three lines from Algorithm 2 were inadvertently omitted during the paper's typesetting. The corrected algorithm is presented here.

This study provides an assessment of the potential for using contextual information with TerraSAR-X backscattering images in classifying urban land-use. Due to the lack of multi-frequency data, a contextual analysis was carried out to extract geometrical information of objects/classes within the images. Anisotropic morphological filters were applied to the backscattering image using a multi-scale approach. A range of different spatial domains were investigated by neural network pruning. The final map of land-use composed of seven different classes of interest was obtained using a Multi-Layer Perceptron neural network with an accuracy of 0.91 in terms of K-coefficient.

Using satellite sensors to detect urban damage and other surface changes due to earthquakes is gaining increasing interest. Optical images at different resolutions and radar images represent useful tools for this application, particularly when more frequent revisit times will be available with the implementation of new missions and future possible constellations of satellites. Very high resolution (VHR) images (on the order of 1 m or less) may provide information at the scale of a single building, whereas images at resolutions on the order of tens of meters may give indications of damage levels at a district scale. Both types of information may be extremely important if provided with sufficient timeliness to rescue teams. The earthquake that hit the city of Bam, Iran, has been taken as a test case, where QuickBird VHR optical images and advanced synthetic aperture radar data were available both before and after the event. Methods to process these data in order to detect damage and to extract features used to estimate damage levels are investigated in this paper, pointing out the significant potential of these satellite data and their possible synergy.

We investigate the relevance of morphological operators for the classification of land use in urban scenes using sub-metric panchromatic imagery. A support vector machine is used for the classification. Six types of filters have been employed: opening and closing, opening and closing by reconstruction, and opening and closing top hat. The type and scale of the filters are discussed, and a feature selection algorithm called recursive feature elimination is applied to decrease the dimensionality of the input data. The analysis performed on two QuickBird panchromatic images showed that simple opening and closing operators are the most relevant for classification at such a high spatial resolution. Moreover, mixed sets combining simple and reconstruction filters provided the best performance. Tests performed on both images, having areas characterized by different architectural styles, yielded similar results for both feature selection and classification accuracy, suggesting the generalization of the feature sets highlighted.

In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.

Remote sensing sensors for detecting urban damage and other surface changes due to earthquakes is gaining increasing interest. To this aim optical images can represent useful tools for this application thanks to their very high ground geometric resolution, especially when more frequent revisit times will be feasible to the implementation of new missions and future possible constellations of satellites. Sub-meter resolution images at visible frequencies are able to provide information at the single building scale. This kind of information is extremely important if provided with sufficient timeliness to rescue teams. In this work, the December 26th, 2003, earthquake that hit the ancient city of Bam (Iran) has been investigated. The urban area was very close to the epicenter of the seism thus causing strong damage to the urban structures. Pre- and post-earthquake QuickBird panchromatic images have been used to show the capability of this data to map damage at building scale by means of segmentation approach based on the application of morphological operators. A validation process has been performed by comparing the map of damage levels at single building scale with a detailed ground-based damage map provided byinsitusurvey.

Urban areas are composed of numerous materials arranged by humans in complex ways. A simple building may appear as a complex structure with many architectural details surrounded by gardens, trees, buildings, roads, social and technical infrastructure and many temporary objects, such as cars, buses or daily markets. In this paper, we analyze the effectiveness of 8 textural features (resulting from the Grey Level Co-occurrence Matrix) derived from a 50 cm WorldVieW-1 image of Washington D.C. (U.S.A.). The information extracted from the panchromatic and textural features are fused and processed by a Multi-Layer Perceptron (MPL) neural network producing a land-use map with accuracy above 0.90 in term of K-coefficient.

This study analyzes different wavenumber spectra produced by both uni-dimensional and bi-dimensional Fast Fourier Transform (FFT) of Very High Resolution (VHR) QuickBird (QB) images to quantitatively describe significant features in the images of different urban environments. The variation of spectra in the wavenumber domain has been assessed by ancillary data comparisons in the spatial domain. Some spectral properties of QB images have been investigated in both spatial and temporal domains..

An extensive analysis based on the use of different morphological filters for the classification of very-high resolution panchromatic images is presented. Feature selection on high-dimensional input space is performed using recursive feature elimination, a support vector machines specific method performing backward elimination based on margin-estimation criterion. Experimental results on an eight-classes image of Las Vegas (USA) confirmed the effectiveness of the analysis pointing out the relevancy of the most contributing morphological features which resulted in high classification accuracy using panchromatic imagery.

An active learning method is proposed for the semi-automatic selection of training sets in remote sensing image classification. The method adds iteratively to the current training set the unlabeled pixels for which the prediction of an ensemble of classifiers based on bagged training sets show maximum entropy. This way, the algorithm selects the pixels that are the most uncertain and that will improve the model if added in the training set. The user is asked to label such pixels at each iteration. Experiments using support vector machines (SVM) on an 8 classes QuickBird image show the excellent performances of the methods, that equals accuracies of both a model trained with ten times more pixels and a model whose training set has been built using a state-of-the-art SVM specific active learning method.

The 2007 Data Fusion Contest that was organized by the IEEE Geoscience and Remote Sensing Data Fusion Technical Committee was dealing with the extraction of a land use/land cover maps in and around an urban area, exploiting multitemporal and multisource coarse-resolution data sets. In particular, synthetic aperture radar and optical data from satellite sensors were considered. Excellent indicators for mapping accuracy were obtained by the top teams. The best algorithm is based on a neural classification enhanced by preprocessing and postprocessing steps.

Parametric and nonparametric approaches to evaluate land-cover change detection using very high resolution (VHR) satellite imagery are applied to the analysis of the demolition of the Rocky Flats nuclear weapons facility located near Denver, CO. Both maximum-likelihood and neural network classifiers are used to validate a new parallel architecture which improves the accuracy when applied to VHR satellite imagery for the study of land-cover change between sequential satellite acquisitions. An enhancement of about 14% was found between the single-step classification and the new parallel architecture, confirming the advantage and the robust improvement obtained with this architecture regardless of the classification algorithm used. In this paper, we demonstrate and document the demolition and removal of hundreds of buildings taken down to bare soil between 2003 and 2005 at the Rocky Flats site.

We present two different change detection techniques to monitor surface changes that occurred at the Rocky Flats nuclear weapons facility located immediately to the North West of the city of Denver, Colorado, USA. The site started being cleaned up and dismantled in 1998 and was completed in 2005. The first Change Detection method is based on a Maximum Likelihood classifier, while the other is an approach based on a Neural Network architecture called NAHIRI (Neural Architecture for High-Resolution Imagery) to produce change detection maps from very high-resolution satellite imagery. NAHIRI simultaneously exploits spectral and temporal information by adding a filter, directly stemming from the multi- temporal information, to the classification changes derived from the multi-spectral data. In fact, the distinctive feature of this method is that the NNs exploit both the multi-spectral and the multi-temporal information in parallel that are associated with the changed values of the pixel spectral reflectances. The quantitative results are analyzed in order to single out advantages and shortcomings of the two different approaches.

The advent of very high spatial resolution optical satellite imagery has greatly increased our ability to monitor land cover changes in urban environments where the spatial resolution plays a key role related to the detection of fine-scale objects such as a single house or small structures. At the same time, very high spatial resolution imagery presents a new challenge over other satellite systems, in that a relatively large amount of data must be analyzed and corrected for registration and classification errors to identify the land cover changes, commonly resulting in a very extensive manual work. To improve on this situation we have developed a new method for land surface change detection that greatly reduces the human effort needed to remove the errors that occur with many methods applied to very high spatial resolution imagery. This change detection algorithm is based on Neural Networks and it is able to exploit in parallel both the multi-band and the multi-temporal data to discriminate between real changes and false alarms. In general the classification errors are reduced by a factor of 2–3 using this new method over a simple Post Classification Comparison based on a neural network classification of the same images.

Many previous studies have demonstrated the viability of estimating advective ocean surface currents from sequential infrared satellite imagery using the maximum cross-correlation (MCC) technique when applied to 1.1-km-resolution Advanced Very High Resolution Radiometer (AVHRR) thermal infrared imagery. Applied only to infrared imagery, cloud cover and undesirable viewing conditions (gaps in satellite data and edge-of-scan distortions) limit the spatial and temporal coverage of the resulting velocity fields. In addition, MCC currents are limited to those represented by the displacements of thermal surface patterns, and hence, isothermal flow is not detected by the MCC method. The possibility of supplementing MCC currents derived from thermal AVHRR imagery was examined, with currents calculated from 1.1-km-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) ocean color imagery, which often have spatial patterns complementary to the thermal infrared patterns. Statistical comparisons are carried out between yearlong collections of thermal and ocean color derived MCC velocities for the central California Current. It is found that the image surface patterns and resulting MCC velocities complement one another to reduce the effects of poor viewing conditions and isothermal flow. The two velocity products are found to agree quite well with a mean correlation of 0.74, a mean rms difference of 7.4 cm/s, and a mean bias less than 2 cm/s which is considerably smaller than the established absolute error of the MCC method. Merging the thermal and ocean color MCC velocity fields increases the spatial coverage by approximately 25% for this specific case study

The 26 papers in this special issue were selected from the conference papers of the 2006 International Geoscience and Remote Sensing Symposium (IGARSS), which was held in Denver, Co, from July 31 to August 4.

The advent of new high spatial resolution optical satellite imagery has greatly increased our ability to monitor land cover changes from space. Satellite observations are carried out regularly and continuously, and provide a great deal of insight into the temporal changes of land cover use. High spatial resolution imagery better resolves the details of these changes and makes it possible to overcome the "mixed-pixel" problem that is inherent with more moderate resolution satellite sensors. At the same time, high-resolution imagery presents a new challenge over other satellite systems, in that a relatively large amount of data must be analyzed and corrected for registration and classification errors to identify the land cover changes. To obtain the accuracies that are required by many applications to large areas, very extensive manual work is commonly required to remove the classification errors that are introduced by most methods. To improve on this situation, we have developed a new method for land surface change detection that greatly reduces the human effort that is needed to remove the errors that occur with many classification methods that are applied to high-resolution imagery. This change detection algorithm is based on neural networks, and it is able to exploit in parallel both the multiband and the multitemporal data to discriminate between real changes and false alarms. In general, the classification errors are reduced by a factor of 2-3 using our new method over a simple postclassification comparison based on a neural-network classification of the same images.

Coastal surface currents have been computed for years from sequential infrared and more recently ocean color imagery using the Maximum Cross Correlation (MCC) technique. Preliminary results suggest that this MCC method may be applied to sequential Synthetic Aperture Radar (SAR) imagery yielding surface currents with a much higher spatial resolution which are independent of the presence of cloud cover which makes it impossible to use infrared or ocean color imagery. A requirement for the application of the MCC to SAR imagery is the presence of surface slicks, which are often related to ocean color patterns. Test applications are made to ENVISAT ASAR images.

Abstract—The application of satellite remote sensing to the detection and study of wildfires has grown rapidly in recent years as new tools have become available and are put into use. Space borne imagery can provide a unique perspective to viewing the fire giving space/time coverage not available with any other observational system. One aspect of fires that can both be detected with satellite imagery and modeled numerically is the smoke plume produced by the fire. Surprisingly, most models designed to study smoke plumes were created to study controlled burns and not wildfires. We use one such model to compare model simulations with a suite of different types of satellite imagery to study a major wildfire. The 2003 Aspen Fire in the mountains north of Tucson, Arizona is used as a case study for the analysis of satellite imagery of a wildfire smoke plume in conjunction with model simulations of this plume. We clearly demonstrate that this plume model can be used to adequately simulate the fire plume as depicted in the satellite imagery when the plume achieves a sufficient altitude. For weak fires and low wind conditions the plumes often follow the local surface topography. Index Terms—AVHRR, fires, plume models, QuickBird

Infrared and microwave SST retrievals are highly complementary but are found to have significant differences that must be addressed if the products are to be combined. Individual products are evaluated using buoy observations to identify any dependence of the retrieval uncertainty on atmospheric forcing. The infrared products are seen to be affected by aerosols, water vapor, and SST while the microwave product is affected by atmospheric stability, wind speed, SST, and water vapor. Applying bias adjustments based on these results reduces the differences between the products

The Maximum Cross Correlation (MCC) method is used with infrared and passive microwave images of sea surface temperature (SST) along with ocean color images to compute sea surface currents from sequential imagery. These surface currents are then merged with geostrophic surface currents computed from coincident satellite altimetry observations to yield a high spatial resolution map of the surface mesoscale currents of the ocean. These methods are shown to be of particular value in the coastal ocean where the mesoscale dominates the surface flow field. The passive microwave data is best for mapping the larger basin scale currents due to their all-weather sensing capability.

Combining gridded ice motions with daily ice extent maps, it is possible to "track" the evolution of sea ice in the Arctic region. Classifying the ice by ice age, this evolution reveals that the area of the oldest (>4 years) ice is decreasing in the Arctic Basin and is being replaced by younger, first-year ice. As a result, the extent of the oldest ice retreats to a relatively small area north of the Canadian Archipelago, with narrow bands that spread out across the central Arctic. This new approach reinforces the work done by others showing the changes in the Arctic sea ice cover over the past two decades.

Ten-day composites of maximum cross-correlation (MCC) ocean surface current vectors from 1-km spatial resolution Advanced Very High Resolution Radiometer (AVHRR) 11-/spl mu/m thermal infrared images are used to simulate the ocean surface current retrieval capabilities of three satellite altimeter configurations over a large California coastal region. Ground track positions of the nadir sampling TOPEX/Poseidon (TP; now the Jason-1) satellite altimeters are used to compute the cross-track velocity components from the corresponding optimally interpolated MCC vectors for a ten-day period. Next, the Jason-1-only and the TP plus Jason-1 "tandem mission" sampling are simulated as well as the combination of all available satellite altimeters including European Remote Sensing Satellite 2 and Geosat Follow-On. Finally, we simulate surface current retrievals from the proposed Wide Swath Ocean Altimeter (WSOA), which will have both along- and cross-track velocity components over a spatial swath. Comparisons of vector current fields, their differences, and wavenumber spectra from optimally interpolated maps of the "simulated altimetry velocities" with the corresponding MCC field indicates that (1) the combined coverage from Jason-1 plus TP as well as the combination of all available satellite altimeters results in a better representation of the currents than that of Jason-1 alone and (2) the retrieved currents from the WSOA provide an even greater improvement over the tandem mapping and multiple satellites. It will be possible in the future to regularly map the mesoscale surface currents of the ocean with wide-swath ocean altimeters.

Future observations of the global ocean will move beyond measurements of the ocean surface topography, winds, roughness, sea surface temperature, and surface salinity to include in situ and remotely sensed measurements of the deep ocean. These important measurements will quantify the deep ocean circulation and its implication for the oceanic transport of heat, and the relationships between ocean circulation variability and the climate change. New measurement technologies will include remote sensing measurements of ocean mass through global gravity observations, innovative measurement of oceanic vertical structure with satellite relayed data from in-situ profiling floats, and remote measurement of the ocean surface boundary layer at higher temporal resolution.

Ten-day composites of maximum cross correlation (MCC) ocean surface current vectors from 1 km spatial resolution advanced very high resolution radiometer (AVHRR) thermal infrared images are used to simulate the ocean surface current retrieval capabilities of three satellite altimeter configurations over a large California coastal region. Ground track positions of the nadir sampling TOPEX/Poseidon (TP; now the Jason-1) satellite altimeters are used to compute the cross track components of the corresponding MCC vectors for a ten-day composite period. Next, the TP and Jason-1 "tandem mission" sampling is simulated for the same region and temporal sample. Finally, a similar approach is used to simulate the current retrievals from the proposed wide swath ocean altimeter (WSOA), which will have the capability to retrieve both along and cross track components of the surface velocity over a spatial swath rather than just nadir sampling. Visual comparisons of raw vector fields and through differences and wavenumber spectra from optimally interpolated (OI) currents from the "simulated altimetry" with the corresponding MCC fields demonstrates that the combined coverage from Jason and TOPEX results in a better representation of the currents than that of Jason alone and that the retrieved currents from the WSOA altimeter provide an even greater improvement over the tandem mapping of the TP and Jason-1 altimeters.

To enable "automated" image navigation (without human intervention) a base image is defined, and the maximum cross correlation (MCC) method is used to automatically compute the satellite attitude parameters required to geometrically correct images to this base image. Several levels of filters insure that contamination from cloudy pixels is minimized. The MCC method produces displacement vectors, which are translated into satellite attitude corrections to be added to the orbital image navigation corrections. The auto attitude corrections are shown to be more accurate than the traditional linear translation methods. A further application of the attitude corrections is demonstrated whereby attitude corrections computed over land can be carried forward in the satellite's orbit to accurately navigate imagery over the open ocean where no map reference points are available. Tested for two land sites well separated in a single orbit this method is shown to be as accurate as when applied to an individual image.

An operational scheme for masking cloud-contaminated pixels in Advanced Very High Resolution Radiometer (AVHRR) daytime data over land is developed, evaluated, and presented. Dynamic thresholding is used with channel 1 reflectance data, channel 3 minus channel 4 temperature difference data, and channel 4 minus channel 5 temperature difference data to automatically create a cloud mask for a single image. The dynamic thresholds can be applied in two different ways: to each pixel individually and to classes of pixels determined by an unsupervised minimum Euclidian distance classifier. The dynamic threshold cloud-masking (DTCM) algorithm presented in this study is used to produce cloud masks based on three different configurations: two channels and individual pixels, three channels and individual pixels, and three channels and classes of pixels. These cloud masks are compared with control masks that were created by visual inspection. The results from the clouds from AVHRR (CLAVR) algorithm and the cloud and surface parameter retrieval (CASPR) algorithm are also compared with the control masks. The results of the comparisons indicate that DTCM, applied on a pixel-by-pixel basis, correctly identifies more clear pixels than CASPR or CLAVR while correctly identifying a comparable or higher number of cloud-contaminated pixels.

This paper demonstrates that a high-resolution reflectivity model used in conjunction with an instrument pointspread function (PSF) can both determine georegistration parameters of coarse resolution sensors and improve the spatial resolution by compositing noncoincident repeat satellite data. To demonstrate this ability, an ideal location is selected and several first principle assumptions are made to simplify the reflectivity model. Twenty-three 1-km advanced very high-resolution radiometer (AVHRR) images are composited by using a Bayesian statistical sampling technique to yield estimates of a simple terrain-based reflectivity model with 180-m resolution. The terrain values are determined from a 90-m resolution digital elevation model (DEM). The Bayesian technique uses the AVHRR data to iteratively determine the most likely values for the model spectral albedos contained within an AVHRR field of view. Model predicted radiances for the repeat AVHRR footprints are computed by integrating model albedo values over the AVHRR PSF. As a first-order verification, simulated AVHRR reflectivities are shown to reconstruct well a smoothly varying prescribed albedo field. Comparisons of the composited real AVHRR image result with Landsat Multi Spectral Scanner (MSS) data show that the model reconstruction resolves surface features, which are not resolved in a single AVHRR image.

Both global area coverage (GAC) and high-resolution picture transmission (HRPT) data from the Advanced Very High Resolution Radiometer (AVHRR) are made available to Internet users through an online data access system. Older GOES-7 data are also available. Created as a "testbed" data system for NASA's future Earth Observing System Data and Information System (EOSDIS), this testbed provides an opportunity to test both the technical requirements of an online data system and the different ways in which the general user community would employ such a system. Initiated in December 1991, the basic data system experienced five major evolutionary changes in response to user requests and requirements. Features added with these changes were the addition of online browse, user subsetting, dynamic image processing/navigation, a stand-alone data storage system, and movement from an X-windows graphical user interface (GUI) to a World Wide Web (WWW) interface. Over its lifetime, the system has had as many as 2500 registered users. The system on the WWW has had over 2500 hits since October 1995. Many of these hits are by casual users that only take the GIF images directly from the interface screens and do not specifically order digital data. Still, there is a consistent stream of users ordering the navigated image data and related products (maps and so forth). The authors have recently added a real-time, seven-day, northwestern United States normalized difference vegetation index (NDVI) composite that has generated considerable interest.

The sensitivity of grassland bidirectional reflectance to soil, vegetation, irradiance, and sensor parameters is assessed. Based on these results, a vegetation bidirectional reflectance distribution function (BRDF) model is inverted with ground reflectance data from the First ISLSCP Field Experiment (FIFE). Results suggest leaf area index (LAI) is most accurately retrieved from data gathered in near-infrared bands at low solar zenith angles (SZA), and leaf angle distribution is best retrieved from data gathered in near-infrared bands at SZA. Generally, leaf optical properties are more accurately estimated from data acquired at high SZA. Canopy albedo and fraction of absorbed photosynthetically active radiation (fAPAR) are also estimated and compared to measured values. Albedo estimates are accurate to about /spl plusmn/0.01 (4% relative) when model parameters are determined from reflectance data gathered under preferred conditions. Estimates of fAPAR are less accurate. These results provide a guide for efficiently sampling surface reflectance and accurately retrieving parameters for use in climate ecosystem models.

High resolution (100 m), sequential multispectral atmospheric mapping sensor (MAMS) images were used to calculate sea surface velocities from the advection of visible and thermal surface features using maximum cross correlation (MCC) and subjective techniques. The visible gradient image velocities agreed well with the subjective motion, while the infrared channel performed best without computing gradients.>

A new AVHRR image navigation software package has been developed. Both direct and indirect image navigation capabilities are provided. An approach is used in which the orbital motion model for the satellite is an independent module. The attitude of the spacecraft is also modeled and the capability to estimate the attitude from ground control points is included. Descriptions of the various orbital and attitude models are presented along with the navigation algorithms. Image navigation examples, including attitude recovery from ground control points, are also shown. Overall, it is found that the software can provide navigation accuracies to within 1 km.>