Arbitrary-resolution hyperspectral (ARHS) pansharpening seeks to enhance low-resolution hyperspectral (LRHS) images to any target spatial resolutions by collaborating with the connected panchromatic (PAN) images. The ARHS pansharpening task is significantly different from standard hyperspectral (HS) pansharpening, where ARHS pansharpening has to deal with the challenge that how to pansharpen upcoming LRHS images to any desired scales far beyond the training scales. Neural operators (NOs) form mapping between functional spaces and build continuous function relations, offering an alternative potential path to deal with ARHS pansharpening task. In this article, we are dedicated to the design of pansharpening NOs (PNOs) by the postulation that LRHS and PAN are observations of the input functional space while target high-resolution HS (HRHS) images are observed from the output functional space. Our PNOs are built with a paradigm comprising prepansharpening encoding, inverse discretization, and functional mapping subnetwork. More specifically, by use of kernel projection of frequency domain-related design and Galerkin design, we develop Fourier PNO (Fourier-PNO) and Galerkin PNO (Galerkin-PNO). In addition, we propose the concept of dynamic spectral-spatial Green’s function and accordingly develop an enhanced Fourier-PNO (eFourier-PNO), and further introduce a PAN-guided test space tactic to develop an enhanced Galerkin-PNO (eGalerkin-PNO). Finally, we discuss and explain some details such as the scale generalization ability of our PNOs. Our four PNOs are tested on three datasets, and the experimental results verify their excellent performances.

Arbitrary-resolution hyperspectral (ARHS) pansharpening seeks to enhance hyperspectral (HS) images to any desired spatial resolutions by integrating HS images with their connected panchromatic (PAN) images, which is obviously different from standard HS pansharpening where HS data are merely pansharpened to the spatial resolutions of PANs. Therefore, ARHS pansharpening has a great potential to broaden the applications of traditional pansharpening technique. However, ARHS pansharpening is faced with some inherent obstacles, including how to produce HS images with arbitrary resolutions beyond the training scales and how to maintain high spatial-spectral fidelities at any pansharpening scales. Implicit neural representations (INRs) is able to parametrically tune neural networks to accommodate unanalytical continuous characteristics of real-world scenes, and thus offer natural representations for ARHS pansharpening. The core of pansharpening INR design is to construct suitable implicit spectral-spatial functions (ISSFs). In this work, we propose two innovatively novel transformer-ensemble based ISSFs, i.e. pre-integrated ISSF (preISSF) and post-integrated ISSF (postISSF), which use suitable coordinate operation, novel ensemble feature extraction and coordinate query mapping to build strong spectral-spatial reconstruction capabilities, where some new tactics such as mode-1 transformer decomposition and explicit-implicit positional encoding system are involved. We test our preISSF and postISSF on three datasets. The experimental results verify the excellent performance of our methods.

Active learning (AL) has achieved great success in remotely sensed hyperspectral image classification due to its ability to select highly informative training samples. An appropriate query strategy is the core of a successful AL method. In this paper, we develop a new graph signal sampling (GS)-based AL strategy which aims to query the most globally optimal and informative pixels for hyperspectral image classification. We first demonstrate the significance of smooth graph structures for GS. Then, we propose a multi-view fused graph-based GS method (GS-FusG) for active learning. Our method combines connection information from multiple perspectives to improve the sampling effectiveness and the performance of graph signal reconstruction. Besides, we present a new reconstruction method for multidimensional graph signals, which integrates the results of multi-view graph signal reconstruction. Last but not least, we develop three variants of GS-FusG based on different multi-view compositions and signal reconstruction methods. Our experimental results with real hyperspectral images demonstrate that our proposed GS-FusG can greatly enhance AL effectiveness and classification performance.

Standard hyperspectral (HS) pansharpening utilizes panchromatic (PAN) images to improve the connected low-resolution HS (LRHS) images to the spatial resolutions of PANs; while arbitrary-resolution hyperspectral (ARHS) pansharpening aims to use PANs to enhance LRHS images to any desired spatial resolutions. For the challenging task of ARHS pansharpening, one of major obstacles is how to generalize the single pansharpening model learned under predetermined training scales to any pansharpening scales for future data. As implicit neural representations (INRs) have a potential to approximate continuous functions, they offer a possible alternative way to naturally resolve ARHS pansharpening. In this paper, we develop two spectral-spatial INRs for ARHS pansharpening: one is a naive pansharpening INR (NaivePINR); the other is a dynamic pansharpening INR (DynamicPINR). The former builds a novel spectral-spatial encoding to produce spectral-spatial priors of observed scenes and uses a spectral-spatial query mapping to reconstruct fine spectral details and spatial details. The latter establishes a innovative two-fold tuning mechanism to dynamically adjust both the spectral-spatial encoding and the spectral-spatial query mapping. Experimental results on serval datasets verify the excellent performances of the proposed pansharpening INRs.

Supervised hyperspectral image classification suffers from the overfitting problem when limited labels are available. Graph-based semisupervised classifiers can tackle this problem by building connections between labeled and unlabeled samples. In this work, we prove the following two propositions for an optimal graph: 1) the interclass connection weights must be 0 and 2) for a given class, a subset must contain labeled samples or be connected to the remaining subset. In a semisupervised scenario, it is very difficult to ensure that the aforementioned propositions hold. Here, we introduce a new discriminant superpixel graph (dSPG) to build a suboptimal graph, which combines a newly proposed within-superpixel graph, aimed at disconnecting pixels belonging to different classes in a superpixel (so as to decrease interclass connection weights) and a between-superpixel graph that connects spectral adjacent superpixels (to increase the intraclass subset connections). We further propose a dSPG regularizer for hyperspectral image classification and a dSPG-guided graph convolutional network (dSPGCN) to extract discriminant features. Experimental results on real hyperspectral datasets demonstrate the good performance of our newly proposed dSPG for semisupervised hyperspectral image classification. The source codes for this study are available athttps://github.com/yulong112/dSPG.

Image stitching aims to mosaic individual images together to build a broader panorama, wherein image registration is the most critical step. In this step, all subimages are normally brought into alignment by warping functions, each of which is often parameterized by rotation, scale, and translation factors. Existing methods usually have to estimate such parameters simultaneously, which incur the problems of unstable and time-consuming computations, especially for stitching large number of unmanned aerial vehicle (UAV) images. In this article, a novel stitching method using vector shape preserving (VSP)-based warping is proposed, which is especially suitable for stitching many UAV images. We innovatively construct warping vectors and involve them to measure registration error to preserve vector shapes, which allows separately dealing with a re-plane stage and a translation stage. Additionally, a novel scale regularization processing connected to the warping is designed for tractable computation. Our method is able to achieve low computational cost, high alignment accuracy, and meanwhile valid real-world interpretation. Experimental results on four real-world UAV image datasets validate the excellent performance of our method.

Hyperspectral (HS) pansharpening seeks to integrate low spatial resolution HS (LRHS) images with connected panchromatic (PAN) images to produce high spatial resolution HS (HRHS) images. Traditional pansharpening convolutional neural networks (CNNs) directly map LRHS and PAN images into HRHS images under fixed network parameters, which imply static pansharpening rules. However, real-world HS data are often characterized by spatial variations, and intuitively, the pansharpening rules should be dynamic. To deal with the dilemma, in this article, we develop dynamic HS pansharpening CNNs. We first specify the concepts of dynamic pansharpening and static pansharpening. Then, we propose a learn-to-learn-oriented pansharpening CNN paradigm, which aims to learn a how-to-learn rule to produce spatially adaptive pansharpening rules and comprises three stages of preliminary fusion, scene-sensitive modulation, and spectral reconstruction. Finally, following the paradigm, we design two groups of dynamic pansharpening CNNs (DyPNNs), i.e., internal-connection-based and external-connection-based. They involve various spatial modulations, including spatial affine transform (AT), spatial dynamic convolution (DC), or improved spatial attention (SA), and, thus, consist of six specific DyPNNs: IC-AT-DyPNN, IC-DC-DyPNN, IC-SA-DyPNN, EC-AT-DyPNN, EC-DC-DyPNN, and EC-SA-DyPNN. Experimental results on several HS datasets verify the effectiveness of the proposed DyPNNs in terms of both the spatial reconstruction and spectral fidelity.

Convolutional neural network (CNN)-based object detection methods have aroused widespread interest in the remote sensing images field, which usually achieve satisfactory results. However, there still exist some factors that cause the detection performance to degrade, such as scales variability, back-ground complexity and objects tininess. In this work, we propose a CNN module that combines semantic information with fine-grained information and can replace the basic block in the backbone of object detection methods to enhance performance. More specifically, our module include a double branches for extracting semantic information and fine-grained information, and an Efficient channel attention (ECA) module for adjusting weights in channel-wise. Experimental results on DIOR dataset suggest the superiority of our module.

Hyperspectral (HS) pansharpening convolutional neural networks (CNNs) usually pre-interpolate the low spatial resolution (LR) HS image before pansharpening, which incurs heavy computation burden and insufficient fusion. Therefore, we propose a novel two-stage fusion based CNN (TSF-CNN) in this work. In particular, the HS pansharpening is split into two fusion stages, i.e. the low-resolution (LRF) fusion stage and the high-resolution fusion (HRF) stage. At the LRF stage, we subsapmle the panchromatic (PAN) image and fuse it with the LRHS image, which not only reduces the computation cost but also make use of the low-frequency information of the PAN. Then, the high-frequency information of the PAN is extracted and fused via a residual dense channel attention block (RDCAB) in the subsequent HRF stage. Moreover, a new spectral similarity auxiliary L1 loss is designed for better spectral fidelity. Experimental results from the Chikusei dataset demonstrate the effectiveness and efficiency of the proposed method.

Traditional hyperspectral (HS) pansharpening aims at fusing a HS image with its panchromatic (PAN) counterpart, to bring the spatial resolution of the HS image to that of the PAN image. However, in many practical applications, arbitrary resolution HS (ARHS) pansharpening is required, where the HS and PAN images need to be integrated to generate a pansharpened HS image with arbitrary resolution (usually higher than that of the PAN image). Such an innovative task brings forth new challenges for the pansharpening technique, mainly including how to reconstruct HS images beyond the training scale and how to guarantee spectral fidelity at any spatial resolutions. To tackle the challenges, we present a novel convolutional neural network (CNN)-based method for ARHS pansharpening called ARHS-CNN. It is based on a two-step relay optimization process, which is associated with a multilevel enhancement subnetwork and a rescaling subnetwork. With a careful design following the thread, our ARHS-CNN is able to pansharpen HS images to any spatial resolutions using just a single CNN model trained on a limited number of scales while meantime to keep spectral fidelity at those resolutions, which wins an obvious advantage over traditional pansharpening methods. Experimental results obtained on several datasets verify the excellent performance of our ARHS-CNN method.

Standard hyperspectral (HS) pansharpening rely on fusion to enhance low-resolution HS (LRHS) images to the resolution of their matching panchromatic (PAN) images, whose practical implementation is normally under a stipulation of scale invariance of model across the training phase and the pansharpening phase. By contrast, arbitrary resolution HS (ARHS) pansharpening seeks to pansharpen LRHS images to any user-customized resolutions. For such a new HS pansharpening task, it is not feasible to train and store CNN models for all possible candidate scales, which implies the single model acquired from the training phase should be capable of being generalized to yield HS images with any resolutions in the pansharpening phase. To address the challenge, a novel variable sub-pixel convolution (VSPC)-based CNN (VSPC-CNN) method following our arbitrary upsampling CNN (AU-CNN) framework is developed for ARHS pansharpening. The VSPC-CNN method comprises a two-stage elevating thread. The first stage is to improve the spatial resolution of input HS image to that of the PAN image through a pre-pansharpening module and then a VSPC-encapsulated arbitrary scale attention upsampling (ASAU) module is cascaded for arbitrary resolution adjustment. After training with given scales, it can be generalized to pansharpen HS image to arbitrary scales under the spatial patterns invariance across the training and pansharpening phases. Experimental results from several specific VSPC-CNNs on both simulated and real HS datasets show the superiority of the proposed method.

Spatio-temporal fusion is a feasible way to provide synthetic satellite images with high spatial and high temporal resolution simultaneously. Due to its practicability, spatio-temporal fusion has gotten increasing attention, for which many spatio-temporal fusion approaches have been developed. Most spatio-temporal fusion methods follow the “base fine image guided” (BFIG) fusion mode, resulting in the fact that their fusion results are similar to the base fine images. Therefore, these methods can perform well in areas with limited surface changes due to high similarity between the base and the predicted fine images. However, they might not be applicable in areas with intense surface changes. In this article, we develop a pansharpening-based spatio-temporal fusion model (PSTFM) by introducing the pansharpening fusion mode, which is “coarse image guided” (CIG), into spatio-temporal fusion. PSTFM first trains a pansharpening convolutional neural network (CNN), which then fuses the coarse images and reconstructed panchromatic (Pan) images of the predicted time to recover the missing fine images. The newly proposed PSTFM is compared with three representative BFIG spatio-temporal fusion methods on two Landsat–Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, both of which contain intense surface changes. After that, the experimental results are analyzed and discussed in detail. The experiments and the analysis demonstrate that the newly proposed PSTFM has remarkably qualitative and quantitative performance in predicting the intense surface changes while it is mediocre in areas with low surface change intensity.

Spatiotemporal fusion (STF) aims at generating remote-sensing data with both high spatial and temporal resolution. In the literature, one of the most widely used strategies to accomplish this goal is to fuse high temporal resolution images collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) with images with finer spatial resolution than those provided by MODIS (e.g., those collected by other satellite instruments such as Landsat or Sentinel-2). Current STF methods generally fuse an upsampled MODIS image with finer spatial resolution images. This leads to two main problems. First of all, the model uncertainty errors (resulting from the ill-posed upsampling problem) will be propagated into the fusion results, leading to spatial and spectral distortion. Furthermore, the spatial details of the upsampled MODIS image may be significantly different from those of the finer spatial resolution images, making the STF problem even more challenging. In order to tackle these issues, in this work, we develop a new linear regression-based STF strategy (LiSTF), which performs the reconstruction from a MODIS-like image (instead of from an upsampled MODIS image), thus reducing the model uncertainty errors and preserving better the spatial information. The MODIS-like images are built from the finer spatial resolution images via downsampling. Our experimental results, conducted using two publicly available datasets of Landsat–MODIS image pairs and one publicly available dataset of Sentinel–MODIS image pairs, reveal that our newly proposed LiSTF approach can significantly enhance the quantitative and qualitative performance of STF, particularly in terms of preserving the spatial information.

Recently, many convolutional neural network (CNN) methods have been designed for hyperspectral image (HSI) classification since CNNs are able to produce good representations of data, which greatly benefits from a huge number of parameters. However, solving such a high-dimensional optimization problem often requires a large number of training samples in order to avoid overfitting. In addition, it is a typical nonconvex problem affected by many local minima and flat regions. To address these problems, in this article, we introduce the naive Gabor networks or Gabor-Nets that, for the first time in the literature, design and learn CNN kernels strictly in the form of Gabor filters, aiming to reduce the number of involved parameters and constrain the solution space and, hence, improve the performances of CNNs. Specifically, we develop an innovative phase-induced Gabor kernel, which is trickily designed to perform the Gabor feature learning via a linear combination of local low-frequency and high-frequency components of data controlled by the kernel phase. With the phase-induced Gabor kernel, the proposed Gabor-Nets gains the ability to automatically adapt to the local harmonic characteristics of the HSI data and, thus, yields more representative harmonic features. Also, this kernel can fulfill the traditional complex-valued Gabor filtering in a real-valued manner, hence making Gabor-Nets easily perform in a usual CNN thread. We evaluated our newly developed Gabor-Nets on three well-known HSIs, suggesting that our proposed Gabor-Nets can significantly improve the performance of CNNs, particularly with a small training set.

Spectral-spatial Gabor filtering, which is based on 3-D local harmonic analysis, has been a powerful spectral-spatial feature extraction tool for hyperspectral image (HSI) classification. However, existing spectral-spatial Gabor approaches are prone to oversmoothing, neglecting the existences of edges and negatively affecting the classification. In this article, we propose a new HSI Gabor filtering concept, called spectral-spatial-range Gabor filtering, which intends to restrain edge interference from disturbing local spectral-spatial harmonic components. Contributions and novelties of our work can be identified as follows: 1) an HSI filtering framework is created, which can accommodate various Gabor filtering procedures and hence offer the potential to guide the design of new Gabor filters; 2) following such a unified filtering framework and taking into consideration both local spectral-spatial harmonic characteristics and range domain variations, we develop a new concept of spectral-spatial-range Gabor filtering; and 3) utilizing this proposed Gabor prototype and elaborating mathematical derivations, we achieve a novel discriminative spectral-spatial-range Gabor filtering method, which can deal with discriminative local harmonics and edge interference simultaneously along the spectral-spatial-range domain, obtaining highly discriminative Gabor features while yielding linear computational complexity. Our novel method is evaluated on four real HSI data sets and achieves excellent performances.

Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in the last four decades from being a sparse research tool into a commodity product available to a broad user community. Specially, in the last 10 years, a large number of new techniques able to take into account the special properties of hyperspectral data have been introduced for hyperspectral data processing, where hyperspectral image classification, as one of the most active topics, has drawn massive attentions. Spectral-spatial hyperspectral image classification can achieve better classification performance than its pixel-wise counterpart, since the former utilizes not only the information of spectral signature but also that from spatial domain. In this paper, we provide a comprehensive overview on the methods belonging to the category of spectral-spatial classification in a relatively unified context. First, we develop a concept of spatial dependency system that involves pixel dependency and label dependency, with two main factors: neighborhood covering and neighborhood importance. In terms of the way that the neighborhood information is used, the spatial dependency systems can be classified into fixed, adaptive, and global systems, which can accommodate various kinds of existing spectral-spatial methods. Based on such, the categorizations of single-dependency, bilayer-dependency, and multiple-dependency systems are further introduced. Second, we categorize the performings of existing spectral-spatial methods into four paradigms according to the different fusion stages wherein spatial information takes effect, i.e., preprocessing-based, integrated, postprocessing-based, and hybrid classifications. Then, typical methodologies are outlined. Finally, several representative spectral-spatial classification methods are applied on real-world hyperspectral data in our experiments.

Active learning (AL) has obtained a great success in supervised remotely sensed hyperspectral image classification, since it is able to select highly informative training samples. As an intrinsically biased sampling approach, AL generally favors the selection of samples following discriminative distributions, which are located in low-density areas. However, hyperspectral data are often highly class-mixed, i.e., most samples fluctuate in the overlapping regions of distributions of different classes. In this case, the potential of AL to select effective training samples is more limited. As AL strongly depends on the features, a possibility to increase its capabilities is to transfer the data into a highly discriminative feature space, in which the mixture of distributions that different classes of data follow tends to reduce. Based on this observation, in this paper, we introduce the concept of feature-driven AL, namely, the sample selection is going to be conducted in a given optimized feature space whose superiority is measured by an overall error probability. For illustrative purposes, we used Gabor filtering and morphological profiles for instantiation. Our experimental results, obtained on three real hyperspectral data sets, indicate that the proposed approach can significantly improve the potential of AL for hyperspectral image classification.

Convolutional neural networks (CNNs) have shown great potential for remote sensing image classification. As the features obtained from a deep CNN generally exhibit high generalization capacity, the subsequent classifier is normally able to provide good results without the need for careful optimization. However it is well-known that, in the pursuit of high classification results, it is generally difficult to acquire a large number of training samples for the learning stage of the CNN. Therefore, it is important to exploit not only the feature generalization ability, but also the machine generalization ability for accurate classification with limited training samples. In this work, we introduce a new two-fold sparse regularization method (based on the deep CNN learning framework) for remote sensing image classification. Our proposed method, called TFCNN (for two-fold CNN), exploits the fact that the sparseness of features leads to increased linear class-separability. It also relies on the fact that the sparseness of the activation function collaborates with those features in a straightforward manner. As a result, the proposed TFCNN naturally achieves (for the first time in the literature) both feature and machine generalization, in complementary fashion. Our experimental results are conducted with three types of remote sensing data: hyperspectral images, multispectral images and synthetic aperture radar (SAR), suggesting that the proposed framework achieves excellent classification performance in all cases.

In order to deal with facial occlusion effectively, the authors propose a powerful but simple face representation method, called adaptive Weberfaces (AdapWeber), based on human visual perception change model and the Weber ratio R implied in Weber's law. Specifically, human perception is naturally highly selective and robust to occlusions, and the Weber ratio R is very important to enhance feature redundancy. As feature redundancy and locality are two guiding principles against facial occlusion, they further develop eight variants of AdapWeber, collectively referred to as single‐scale and single‐orientation (SSSO) AdapWeber, by shrinking the kernel locality and varying the kernel orientation of the original AdapWeber, and integrate them to formulate a multi‐scale and multi‐orientation (MSMO) AdapWeber. A natural by‐product of MSMO AdapWeber is MSMO Weberfaces. Experiments on four benchmark databases, including Extended Yale B, AR, UMB‐DB, and LFW, showed that MSMO AdapWeber/Weberfaces, rather than any variant of SSSO AdapWeber/Weberfaces, outperformed several popular feature extraction approaches in many scenarios, especially when the occlusion level is very high or the image dimension is very low. This result demonstrates that several occlusion‐weak features can be combined together to construct an occlusion‐robust feature.

Spectral-spatial classification of remotely sensed hyperspectral images has attracted a lot of attention in recent years. Although Gabor filtering has been used for feature extraction from hyperspectral images, its capacity to extract relevant information from both the spectral and the spatial domains of the image has not been fully explored yet. In this paper, we present a new discriminative low-rank Gabor filtering (DLRGF) method for spectral-spatial hyperspectral image classification. A main innovation of the proposed approach is that our implementation is accomplished by decomposing the standard 3-D spectral-spatial Gabor filter into eight subfilters, which correspond to different combinations of low-pass and bandpass single-rank filters. Then, we show that only one of the subfilters (i.e., the one that performs low-pass spatial filtering and bandpass spectral filtering) is actually appropriate to extract suitable features based on the characteristics of hyperspectral images. This allows us to perform spectral-spatial classification in a highly discriminative and computationally efficient way, by significantly decreasing the computational complexity (from cubic to linear order) compared with the 3-D spectral-spatial Gabor filter. In order to theoretically prove the discriminative ability of the selected subfilter, we derive an overall classification risk bound to evaluate the discriminating abilities of the features provided by the different subfilters. Our experimental results, conducted using different hyperspectral images, indicate that the proposed DLRGF method exhibits significant improvements in terms of classification accuracy and computational performance when compared with the 3-D spectral-spatial Gabor filter and other state-of-the-art spectral-spatial classification methods.

A suitable filtering preprocessing is beneficial to hyperspectral image (HSI) classification. In this paper, we design a three-dimensional filtering approach for spectral-spatial HSI classification. The associated three-dimensional filter is the coupling of two kinds of kernels. The former is a Gaussian kernel that collects spatial dependency of spectra, whereas the latter is the derivative of Gaussian kernel that reflects the discriminating contribution of spectrum derivative. Through convoluting with such a three-dimensional filtering, it is expected that class-specific spectra of an HSI are more congregated; thus leading to better classification performance in the subsequent classification stage. Experimental results from a real HSI validate that such a three-dimensional filtering is capable of enhancing the classification accuracy of several benchmark HSI classifiers.

The methodology of sparse representations (SRs) has being popular in hyperspectral image (HSI) classification. To boost the SR-based classification for HSIs, in this paper we present a designation of sparse representation involving random subspace. First, random band selection or random projection generates data subspaces from an original HSI. Then, the sparse representation on each subspace is solved by a linear programming. Finally, the average of the sparse representations or that of the associated local residuals is applied to integrate the contribution of all subspaces, yielding the final classification output. Four SR classifiers, including RSBS-AC, RSBS-AR, RSP-AC and RSP-AR, are therefore established. Experimental results from a real HSI suggest that such SR classifiers truly achieve significant enhanced classification performance.

Active learning has obtained a great success in supervised remotely sensed hyperspectral image classification, since it can be used to select highly informative training samples. As an intrinsically biased sampling approach, it generally favors the selection of samples following discriminative distributions, i.e., those located in low density areas in feature space. However, the hyperspectral data are often highly mixed, i.e., most samples fluctuate in a local density areas. In this case, the potential of active learning for effective training sample selection is more limited. In order to address this relevant issue, we develop a new Gabor-based active learning approach for hyperspectral image classification, which consists of two main steps. First, we use a Gabor filter for feature extraction, which aims at bringing the data into a discriminative space. Then, we perform active learning to find the most informative training samples in the low density areas prior to the final classification. Our experimental results, conducted using two real hyperspectral datasets, indicate that the proposed Gabor-based approach can greatly improve the potential of active learning for classification purposes.

For hyperspectral image (HSI) classification, it is challenging to adopt the methodology of sparse-representation-based classification. In this paper, we first propose an l1-minimization-based spectral-spatial classification method for HSIs via a spatial translation-invariant wavelet (STIW)-based sparse representation (STIW-SR), wherein both the spectrum dictionary and the analyzed signal are formed with STIW features. Due to the capability of a STIW to reduce both the observation noise and the spatial nonstationarity while maintaining the ideal spectra, which is proved with our signal-interference-noise spectrum model involved, it is expected that the pixels in the same class congregate in a lower dimensional subspace, and the separations among class-specific subspaces are enhanced, thus yielding a highly discriminative sparse representation. Then, we develop an approach to evaluate the sparsity recoverability of an l1-minimization on HSIs in a probabilistic framework. This approach takes into account not only the recovery probability under the given support length of the l0-norm solution but also the apriori probability of the support length; consequently, it overcomes the inability of traditional mutual/cumulative coherence conditions to address high-coherence HSIs. This paper reveals that the higher sparsity recoverability of a STIW-SR leads to its higher classification accuracy and that the increasing coherence does not necessarily lead to a reduced sparsity recovery probability, and this paper verifies the connection between l0and l1-minimizations on HSIs. Experimental results from realworld HSIs suggest that our classification method significantly outperforms several representative spectral-spatial classifiers and support vector machines.

P300 is one of the most studied components of event related potentials which reflects the responses of brain to events in the external environment. In this paper, we present a new method that utilizes multiresolution autoregression of multichannel time series (MAMTS) for feature extraction of P300 wave. First, it adopts multiresolution autoregression on dyadic tree to depict the characteristic of electroencephalogram (EEG) signal. Then the corresponding autoregression noise of multichannel time series is extracted as the feature. The experiment results verified the effectiveness of this new feature for P300 speller brain compute interface (BCI).