Medium spatial resolution remote sensing images are widely used for cropland mapping. In areas where cropland is fragmented, however, the limitation of the spatial resolution may lead to inaccurate or even impossible mapping of small croplands. Super-resolution mapping is an effective method to address this issue by transforming coarse-resolution fraction images, derived from spectral unmixing, into fine-resolution land cover maps. In practical applications, a crucial obstacle of this approach is the difficulty in collecting training samples for spectral unmixing and super-resolution mapping. To address this problem, this article proposed a novel super-resolution cropland mapping approach by simulating spectral and spatial training samples. Specially, a mixture spectral simulation method was used to generate training samples for the regression unmixing model to estimate cropland fraction images. A multilevel feature fusion U-NET model was proposed for super-resolution cropland mapping and was trained with simulated training samples considering fraction errors. The proposed method was tested in the Jianghan Plain, China, by generating 2.5-m cropland maps from the 10-m Sentinel-2 images. The results show that the proposed method can accurately extract more smaller and linear land cover features, preserve the spatial structure of the boundaries, and achieve higher accuracy than other cropland mapping methods. This method overcomes the dependency on actual sample collection in traditional methods, better utilizes spectral and spatial features in remote sensing data, and reduces the impact of spectral unmixing errors on the final fine-resolution cropland maps.

Water color is an intuitive parameter for indicating the condition of natural waters. It can be captured by satellite images on a large scale and over a long period of time. However, it can be challenging to capture this information for small bodies of water due to the trade-off between spatial and spectral resolution of imaging sensors. We propose a deep learning-based method, called Spectral Angle (SA) Consistent Unsupervised Generative Adversarial Network (SA-UGAN), to fuse Sentinel-2 multispectral data and China's Gaofen-1 panchromatic images. The loss function is optimized to emphasize spectral preservation through an SA-based loss. SA-UGAN outperforms three traditional approaches in terms of quality assessment. The hue angle, which is used to describe water color, was monitored based on the fused image. The results showed that the spatial distribution of hue angle obtained from the SA-UGAN fused images was consistent with the original results.

The reconstruction of the Normalized Difference Vegetation Index (NDVI) is a crucial prerequisite for numerous spatiotemporal continuous studies. To address the limitations posed by satellite temporal resolution and challenging atmospheric conditions, the combination of Synthetic Aperture Radar (SAR) and optical images from diverse sources has proven to be effective and widely employed. In this study, we employ the Spatial-Temporal Savitzky-Golay algorithm to rectify MODIS NDVI maps and eliminate interruptions caused by noise. Random Forest and Gradient Boosting Decision Trees serve as a dual filter to select SAR indices with the highest impact on NDVI reconstruction, ensuring that the chosen indices encapsulate the most valuable information. Subsequently, we conduct a series of ablation experiments and develop a deep learning network named Residual-in-Residual Dense Block NDVI Reconstruction net (RDNRnet). This network effectively mitigates the impacts of MODIS coarse resolution and speckle noises in SAR data. We also evaluate the network performance in reconstructing NDVI across all seasons and land cover types. Our findings highlight that the modified dual-polarimetric SAR vegetation index and the standard deviation of the VV band are the most crucial SAR indices. The predictions for summer exhibit the highest performance, with a coefficient of determination (R2) reaching 0.9757. Optimal performances by land cover type are observed in forests, paddy fields, and dry farming fields, all with R2values exceeding 0.9580. Our adaptive NDVI reconstruction solution demonstrates robust performance across different data availability scenarios, effectively catering to all seasons and land cover types.

Mapping surface water bodies from fine spatial resolution optical remote sensing imagery is essential for the understanding of the global hydrologic cycle. Although satellite data are useful for mapping, the limited spectral information captured by some satellite systems can be suboptimal for the task. For example, the very high-resolution images of Google Earth (GE) only contain RGB bands, which often means many water bodies and land objects are confused. Sentinel-2 (S2) imagery has a spectral resolution more suitable for mapping water bodies, but its medium spatial resolution limits the ability for detailed mapping of water-land boundaries. This letter proposes a deep feature and domain knowledge fusion network (DFDKFNet) for mapping surface water bodies by fusing GE and S2 images while incorporating domain knowledge. DFDKFNet uses the remote sensing indices of normalized difference water index (NDWI) and normalized difference vegetation index (NDVI) derived from the S2 image as the representative domain knowledge to better extract water bodies from terrestrial features. A similar pixel-based approach is used to downscale the NDWI and NDVI maps to match the spatial resolution between the GE and S2 images. The DFDKFNet uses the GE and downscaled NDWI and NDVI images to extract the deep semantic features of water bodies, which are fused with the domain knowledge extracted from the NDWI and NDVI images. DFDKFNet was compared with several state-of-the-art algorithms, and the results show that DFDKFNet can enhance water body mapping accuracy.

Super-resolution mapping (SRM) is an effective way to alleviate the mixed pixel problem of remotely sensed imagery, by transforming the coarse-resolution fraction image originated from spectral unmixing into a fine-resolution land-cover map. Deep learning has been widely used in SRM since it has a powerful ability to represent the complex heterogeneous spatial distribution patterns of land-cover patches. However, the accuracy of existing deep learning-based SRM models is compromised by the fact that the fraction images used in SRM always contain errors. In this paper, we propose an end-to-end convolutional neural network (CNN)-based fraction error eliminating SRM (DeepNESRM) method to overcome the negative effect of fraction errors in SRM. In DeepNESRM, to better learn the complex nonlinear relationship between the actual coarse-resolution fraction image and the fine-resolution land-cover map by the CNN, a practical error simulation method that considers the characteristics of fraction errors is introduced to produce training samples. In addition, a multi-level feature fusion CNN is adopted to eliminate fraction errors and simultaneously implement SRM. Experiments using Sentinel-2 and Landsat 8 images were conducted to test the performance of the proposed method. Two conventional SRM methods, namely the pixel swapping method and the spatial dependence and L2 norm combined SRM (L2_SRM) method, and also a stacked very deep CNN based SRM (VDSRM) method, were used as the comparison methods. The results show that DeepNESRM can deal with fraction errors and preserve the spatial detail information, and achieve higher average overall accuracies than the other methods.

Superresolution mapping (SRM) is a commonly used method to cope with the problem of mixed pixels when predicting the spatial distribution within low-resolution pixels. Central to the popular SRM method is the spatial pattern model, which is utilized to represent the land cover spatial distribution within mixed pixels. The use of an inappropriate spatial pattern model limits such SRM analyses. Alternative approaches, such as deep-learning-based algorithms, which learn the spatial pattern from training data through a convolutional neural network, have been shown to have considerable potential. Deep learning methods, however, are limited by issues such as the way the fraction images are utilized. Here, a novel SRM model based on a generative adversarial network (GAN), GAN-SRM, is proposed that uses an end-to-end network to address the main limitations of existing SRM methods. The potential of the proposed GAN-SRM model was assessed using four land cover subsets and compared to hard classification and several popular SRM methods. The experimental results show that of the set of methods explored, the GAN-SRM model was able to generate the most accurate high-resolution land cover maps.

The spatiotemporal reflectance fusion method is used to blend high-temporal and low-spatial resolution images with their low-temporal and high-spatial resolution counterparts that were previously acquired by various satellite sensors. Recently, a wide variety of learning-based solutions have been developed, but challenges remain. These solutions usually require two sets of data acquired before and after the prediction time, making them unsuitable for near-real-time predicting. The solutions are always trained band by band and thus do not consider the spectral correlation. High-resolution temporal changes are difficult to reconstruct accurately with the network structure used, which lowers the accuracy of the fusion result. To address these problems, this study proposes a novel spatiotemporal adaptive reflectance fusion model using a generative adversarial network (GASTFN). In GASTFN, an end-to-end network, including a generative and discriminative network, is simultaneously trained for all spectral bands. The proposed model can be applied to the one-pair case, consider the spectral correlation of each band, and improve the process of producing super-resolution imagery by adopting the discriminative network for image reflectance values rather than temporal changes in reflectance. The proposed model has been verified with two actual satellite data sets acquired in heterogeneous landscapes and areas with abrupt changes, with a comparison of the state-of-art methods. The results show that GASTFN can generate the most accurate fusion images with more detailed textures, more realistic spatial shapes, and higher accuracy, demonstrating that the GASTFN is effective for predicting near-real-time changes in image reflectance and preserves the most valuable spatial information.

Rivers are important elements of the earth’s ecosystem, and their spatial distribution information is critical for the study of hydrological and biogeochemical processes. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery has been widely used for river mapping due to its high temporal resolution and long-term observation records, which are essential to capture the rapid fluctuation of rivers. However, when the conventional hard classification methods are used, the accuracy of the river maps produced from MODIS data (and especially for those rivers with narrow widths) is often limited because mixed pixels are common in MODIS imagery, due to coarse spatial resolution. In this article, a cascaded spectral–spatial information combined deep convolutional neural network (CNN) model for super-resolution river mapping (DeepRivSRM) is proposed to produce Landsat-like fine-resolution river maps from MODIS images. In DeepRivSRM, a CNN made up of a spectral unmixing module and a super-resolution mapping (SRM) module is introduced to handle the spectral and spatial information simultaneously. Moreover, for training the DeepRivSRM model, an adaptive cross-entropy loss function incorporating the fraction information of the rivers is designed to improve the performance of the DeepRivSRM model for small rivers. The proposed method was evaluated with MODIS images from three test sites and was compared with hard classification, a conventional SRM method, and a CNN-based SRM method. The results show that DeepRivSRM can generate more accurate river maps by effectively learning the subpixel-scale spatial–spectral information in MODIS imagery.

Due to the tradeoff between spatial and temporal resolutions commonly encountered in remote sensing, no single satellite sensor can provide fine spatial resolution land surface temperature (LST) products with frequent coverage. This situation greatly limits applications that require LST data with fine spatiotemporal resolution. Here, a deep learning-based spatiotemporal temperature fusion network (STTFN) method for the generation of fine spatiotemporal resolution LST products is proposed. In STTFN, a multiscale fusion convolutional neural network is employed to build the complex nonlinear relationship between input and output LSTs. Thus, unlike other LST spatiotemporal fusion approaches, STTFN is able to form the potentially complicated relationships through the use of training data without manually designed mathematical rules making it is more flexible and intelligent than other methods. In addition, two target fine spatial resolution LST images are predicted and then integrated by a spatiotemporal-consistency (STC)-weighting function to take advantage of STC of LST data. A set of analyses using two real LST data sets obtained from Landsat and moderate resolution imaging spectroradiometer (MODIS) were undertaken to evaluate the ability of STTFN to generate fine spatiotemporal resolution LST products. The results show that, compared with three classic fusion methods [the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the spatiotemporal integrated temperature fusion model (STITFM), and the two-stream convolutional neural network for spatiotemporal image fusion (StfNet)], the proposed network produced the most accurate outputs [average root mean square error (RMSE)0.971].

Optical earth observation satellite sensors often provide a coarse spatial resolution (CR) multispectral (MS) image together with a fine spatial resolution (FR) panchromatic (PAN) image. Pansharpening is a technique applied to such satellite sensor images to generate an FR MS image by injecting spatial detail taken from the FR PAN image while simultaneously preserving the spectral information of MS image. Pansharpening methods are mostly applied on a per-pixel basis and use the PAN image to extract spatial detail. However, many land cover objects in FR satellite sensor images are not illustrated as independent pixels, but as many spatially aggregated pixels that contain important semantic information. In this article, an object-based pansharpening approach, termed object-based area-to-point regression kriging (OATPRK), is proposed. OATPRK aims to fuse the MS and PAN images at the object-based scale and, thus, takes advantage of both the unified spectral information within the CR MS images and the spatial detail of the FR PAN image. OATPRK is composed of three stages: image segmentation, object-based regression, and residual downscaling. Three data sets acquired from IKONOS and Worldview-2 and 11 benchmark pansharpening algorithms were used to provide a comprehensive assessment of the proposed OATPRK approach. In both the synthetic and real experiments, OATPRK produced the most superior pan-sharpened results in terms of visual and quantitative assessment. OATPRK is a new conceptual method that advances the pixel-level geostatistical pansharpening approach to the object level and provides more accurate pan-sharpened MS images.

The brick-making industry of the `Brick Belt' (spanning mostly parts of Bangladesh, Nepal, India and Pakistan) is a major employer, providing work for tens of millions people. Unfortunately, it is known via locally-based human rights groups that many of those working in the brick kilns are modern-day slaves. However, reliable and timely, spatially explicit and scalable data on the full extent of this slavery is currently unavailable. Since brick kilns leave a visible impact on the Earth's land cover that can be detected from space, and given that satellite sensor images are readily available globally for the past >40 years, using these data could afford a better understanding of the spatio-temporal dynamics of this slavery activity, affording optimal intervention. This research focuses on aging of brick kilns in the `Brick Belt' based on Landsat time series data, in order to reflect the evolution of brick kilns. Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM+) and Landsat 8 Operational Land Imager (OLI) time series data from 1984 to 2018 were downloaded from Google Earth Engine according to the coordinates of a collection of kilns identified in the `Brick Belt'. A break detection method applied to the time series of data and random forest classifications were used to age the kilns. Results showed that the accuracy of kiln aging, determined using Google Earth as ground data, was approximately 83%, and the root-mean-square-error (RMSE) between the Landsat prediction date and Google Earth date was about 3.60 years, both highlighting the potential to age kilns across the broader region. The results also show that many kilns were younger than 10 years, indicating that the brick making industry has been highly active in recent years, being driven by the demand for bricks as the region's economy grows.

Super-resolution mapping (SRM) aims to determine the spatial distribution of the land cover classes contained in the area represented by mixed pixels to obtain a more appropriate and accurate map at a finer spatial resolution than the input remotely sensed image. The image-based SRM models directly use the observed images as input and can mitigate the uncertainty caused by class fraction errors. However, existing image-based SRM models always adopt a fixed set of endmembers used in the entire image, ignoring the spatial variability and spectral uncertainty of endmembers. To address this problem, this letter proposed an optimal endmember-based SRM (OESRM) model, which considers the spatial variations in endmembers, and determines the best-fit one for each coarse resolution pixel using the spectral angle and the spectral distance as the spectral similarity indexes. A Sentinel-2A and a Landsat-8 multispectral images were used to analyze the performance of OESRM, by comparing with three other SRM methods which adopt a fixed endmember set or multiple endmember sets. The results showed that OESRM generated resultant land cover maps with more spatial detail, and reduced the confusion between land cover classes with similar spectral features. The proposed OESRM model produced the results with the highest overall accuracy in both experiments, showing its effectiveness in reducing the effect of endmember uncertainty on SRM.

The mixed pixel problem is common in remote sensing. A soft classification can generate land cover class fraction images that illustrate the areal proportions of the various land cover classes within pixels. The spatial distribution of land cover classes within each mixed pixel is, however, not represented. Super-resolution land cover mapping (SRM) is a technique to predict the spatial distribution of land cover classes within the mixed pixel using fraction images as input. Spatial–temporal SRM (STSRM) extends the basic SRM to include a temporal dimension by using a finer-spatial resolution land cover map that pre- or postdates the image acquisition time as ancillary data. Traditional STSRM methods often use one land cover map as the constraint, but neglect the majority of available land cover maps acquired at different dates and of the same scene in reconstructing a full state trajectory of land cover changes when applying STSRM to time-series data. In addition, the STSRM methods define the temporal dependence globally, and neglect the spatial variation of land cover temporal dependence intensity within images. A novel local STSRM (LSTSRM) is proposed in this paper. LSTSRM incorporates more than one available land cover map to constrain the solution, and develops a local temporal dependence model, in which the temporal dependence intensity may vary spatially. The results show that LSTSRM can eliminate speckle-like artifacts and reconstruct the spatial patterns of land cover patches in the resulting maps, and increase the overall accuracy compared with other STSRM methods.

Forest mapping from satellite sensor imagery provides important information for the timely monitoring of forest growth and deforestation, bioenergy potential assessment, and modeling of carbon flux, among others. Due to the daily global revisit rate and wide swath width, MODerate-resolution Imaging Spectroradiometer (MODIS) images are used commonly for satellite-derived forest mapping at both regional and global scales. However, the spatial resolution of MODIS images is too coarse to observe fine spatial variation in forest cover. The last few decades have seen the production of several fine-spatial-resolution satellite-derived global forest cover maps, such as Hansen’s global tree canopy cover map of 2000, which includes abundant spectral, temporal, and spatial prior information about forest cover at a fine spatial resolution. In this paper, a novel learning-based spatial–temporal superresolution mapping approach is proposed to integrate both current MODIS images and prior maps of Hansen’s tree canopy cover, to map present forest cover with a fine spatial resolution. The novel approach is composed of three main stages: 1) automatic generation of 240-m forest proportion images from both 240- and 480-m MODIS images using a nonlinear learning-based spectral unmixing method; 2) downscaling the 240-m forest proportion images to 30 m to predict the class possibilities at the subpixel scale using a temporal-example learning-based downscaling method; and 3) final production of the fine-spatial-resolution forest map by solving a regularization-based optimization problem. The novel approach produced more accurate fine-spatial-resolution forest cover maps in terms of both visual and quantitative evaluation than traditional pixel-based classification and the latest subpixel based superresolution mapping methods. The results show the great efficiency and potential of the novel approach for producing fine-spatial-resolution forest maps from MODIS images.

The development of remote sensing has enabled the acquisition of land cover classes and their changes at different scales. The high-spatial-resolution images are usually acquired infrequently, whereas the low-spatial-resolution images which have high repetition rates cannot capture the land cover spatial detail information. A novel spatial-temporal land cover blending method (STLCB) is proposed to produce land cover maps at both high spatial and temporal resolutions, using a single or a series of low-spatial-resolution images and two high-spatial-resolution land cover maps which pre-date and post-date the low-spatial-resolution images as input. A spatial-temporal Markov-random-field based method, which integrates spatial and temporal links of pixels, is proposed in STLCB. The proposed STLCB method is validated based on synthetic and Landsat multi-spectral images. Results show that the overall accuracies of STLCB were higher than 90% in both experiments.

The development of remote sensing has enabled the acquisition of information on land-cover change at different spatial scales. However, a tradeoff between spatial and temporal resolutions normally exists. Fine-spatial-resolution images have low temporal resolutions, whereas coarse-spatial-resolution images have high temporal repetition rates. A novel superresolution change detection method (SRCD) is proposed to detect land-cover changes at both fine spatial and temporal resolutions with the use of a coarse-resolution image and a fine-resolution land-cover map acquired at different times. SRCD is an iterative method that involves endmember estimation, spectral unmixing, land-cover fraction change detection, and superresolution land-cover mapping. Both the land-cover change/no-change map and from-to change map at fine spatial resolution can be generated by SRCD. In this paper, SRCD was applied to a synthetic multispectral image, a Moderate-Resolution Imaging Spectroradiometer multispectral image, and a Landsat-8 Operational Land Imager multispectral image. The land-cover from-to change maps are found to have the highest overall accuracy (higher than 85%) in all of the three experiments. Most of the changed land-cover patches, which were larger than the coarse-resolution pixel, were correctly detected.

Superresolution mapping (SRM) is a method to produce a fine-spatial-resolution land cover map from coarse-spatial-resolution remotely sensed imagery. A popular approach for SRM is a two-step algorithm, which first increases the spatial resolution of coarse fraction images by interpolation and then determines class labels of fine-resolution pixels using the maximum a posteriori (MAP) principle. By constructing a new image formation process that establishes the relationship between the observed coarse-resolution fraction images and the latent fine-resolution land cover map, it is found that the MAP principle only matches with area-to-point interpolation algorithms and should be replaced by deconvolution if an area-to-area interpolation algorithm is to be applied. A novel iterative interpolation deconvolution (IID) SRM algorithm is proposed. The IID algorithm first interpolates coarse-resolution fraction images with an area-to-area interpolation algorithm and produces an initial fine-resolution land cover map by deconvolution. The fine-spatial-resolution land cover map is then updated by reconvolution, back-projection, and deconvolution iteratively until the final result is produced. The IID algorithm was evaluated with simulated shapes, simulated multispectral images, and degraded Landsat images, including comparison against three widely used SRM algorithms: pixel swapping, bilinear interpolation, and Hopfield neural network. Results show that the IID algorithm can reduce the impact of fraction errors and can preserve the patch continuity and the patch boundary smoothness simultaneously. Moreover, the IID algorithm produced fine-resolution land cover maps with higher accuracies than those produced by other SRM algorithms.

Superresolution mapping (SRM) is a technique for generating a fine-spatial-resolution land cover map from coarse-spatial-resolution fraction images estimated by soft classification. The prior model used to describe the fine-spatial-resolution land cover pattern is a key issue in SRM. Here, a novel learning-based SRM algorithm, whose prior model is learned from other available fine-spatial-resolution land cover maps, is proposed. The approach is based on the assumption that the spatial arrangement of the land cover components for mixed pixel patches with similar fractions is often similar. The proposed SRM algorithm produces a learning database that includes a large number of patch pairs for which there is a fine- and coarse-spatial-resolution representation for the same area. From the learning database, patch pairs that have similar coarse-spatial-resolution patches as those in the input fraction images are selected. Fine-spatial-resolution patches in these selected patch pairs are then used to estimate the latent fine-spatial-resolution land cover map by solving an optimization problem. The approach is illustrated by comparison against state-of-the-art SRM methods using land cover map subsets generated from the USA's National Land Cover Database. Results show that the proposed SRM algorithm better maintains the spatial pattern of land covers for a range of different landscapes. The proposed SRM algorithm has the highest overall accuracy and kappa values in all of these SRM algorithms, by using the entire maps in the accuracy assessment.

The Moderate Resolution Imaging Spectroradiometer (MODIS) is an important data set in global burned-area mapping. The MODIS global burned-area product has a coarse spatial resolution at approximately 500 m, which often introduces errors to the mapped burned areas. In this letter, a novel subpixel mapping (SPM) approach was proposed to produce burned-area maps at the fine spatial resolution similar to Landsat imagery, by exploring the spectral and spatial information provided by the second and fifth bands of MODIS. The proposed SPM approach aims to refine the estimate of burned areas, which have been detected by the MODIS global burned-area product. The performance of the proposed SPM approach was assessed with an experiment area containing six burned areas, by comparing with the MODIS burned-area product MCD45. The result shows that the average omission error decreased from 52.26% for MCD45 to 16.74% for SPM, and the average commission error decreased from 21.76% for MCD45 to 12.54% for SPM. The kappa value increased from 0.5583 for MCD45 to 0.8756 for SPM, indicating that the proposed SPM approach is effective in reducing the influence of the coarse spatial resolution of MODIS imagery in mapping a burned area and refining existing global burned-area products.

Super-resolution mapping (SRM) is a method for generating a fine-resolution land cover map from coarse-resolution fraction images. Example-regression-based SRM algorithms can estimate a fine-resolution land cover map with detailed spatial information by learning land cover spatial patterns from available land cover maps. Existing example-regression-based SRM algorithms are sensitive to fraction errors, and the results often include many linear artifacts and speckles. To overcome these shortcomings, this study proposes an improved example-regression-based SRM algorithm. The objective function of the proposed SRM algorithm comprises three terms. The first term is used to minimize the difference between the fraction values of the estimated fine-resolution land cover map and the input fraction values. The second term is used to maximize the class membership possibility values of the fine pixels in the result. The final term is used to make the result locally smooth. The proposed SRM algorithm is compared with several popular SRM algorithms using both synthetic and real fraction images. Experimental results indicate that the proposed SRM algorithm can produce results with less speckles and linear artifacts, more spatial details, smoother boundaries, and higher accuracies than the SRM results used for comparison.

Superresolution mapping (SRM) based on the Hopfield neural network (HNN) is a technique that produces land cover maps with a finer spatial resolution than the input land cover fraction images. In HNN-based SRM, it is assumed that the spatial dependence of land cover classes is homogeneous. HNN-based SRM uses an isotropic spatial dependence model and gives equal weights to neighboring subpixels in the neighborhood system. However, the spatial dependence directions of different land cover classes are discarded. In this letter, a revised HNN-based SRM with anisotropic spatial dependence model (HNNA) is proposed. The Sobel operator is applied to detect the gradient magnitude and direction of each fraction image at each coarse-resolution pixel. The gradient direction is used to determine the direction of subpixel spatial dependence. The gradient magnitude is used to determine the weights of neighboring subpixels in the neighborhood system. The HNNA was examined on synthetic images with artificial shapes, a synthetic IKONOS image, and a real Landsat multispectral image. Results showed that the HNNA can generate more accurate superresolution maps than a traditional HNN model.

Subpixel mapping (SPM) is a technique to obtain a land cover map with finer spatial resolution than the original remotely sensed imagery. An image-based SPM model that directly uses the original image data as input by integrating both the spectral and spatial information has been demonstrated as a promising SPM model. However, all existing image-based SPM models are based on a supervised approach, since the spectral term in these SPM models is composed of a supervised unmixing method. The endmembers and training samples for different land cover classes must be determined before implementing these supervised SPM algorithms. In this letter, a novel unsupervised image-based SPM model based on the fuzzy c-means (FCM) clustering approach (usFCM_SPM) was proposed. By incorporating the unsupervised unmixing criterion of the FCM clustering algorithm and the maximal land cover spatial-dependence principle, the proposed usFCM_SPM can generate a subpixel land cover map without any prior endmember information. Both synthetic multispectral image and real IKONOS image experiments demonstrate that the usFCM_SPM can generate higher accuracy subpixel land cover maps than the traditional unsupervised pixel-scale classification approaches and the unsupervised pixel-swapping model.

Superresolution land cover mapping (SRM) is a technique for generating land cover maps with a finer spatial resolution than the input image. In general, either the original multispectral (MS) images or the spectral unmixing results of the MS image are used as input for SRM models. Panchromatic (PAN) images are often captured together with MS images by many remote sensors and provide more spatial information due to their higher spatial resolution compared with the MS image. In this paper, a spatially adaptive spatial–spectral managed SRM model (SA_SSMSRM) that incorporates both MS and PAN images is proposed. SA_SSMSRM aims to better smooth homogeneous regions of objects (which represent a territory within which there is a uniformity in terms of land cover class) and preserve land cover class boundaries simultaneously by using the PAN image pixel photometric distance (i.e., gray-level distance or pixel value difference). Homogeneous regions in the PAN images are usually characterized by the photometric (pixel value) similarity, whereas class boundaries are usually characterized by photometric dissimilarity. The SA_SSMSRM smoothing parameter, which controls the contribution of the prior term (which encodes prior knowledge about land cover spatial patterns), is designed to be spatially adaptive, with its value decreasing if the photometric similarity of neighboring PAN image pixels decreases. SA_SSMSRM was examined on high-spatial-resolution QuickBird images, IKONOS images, and Advanced Land Observing Satellite (ALOS) images with both MS and PAN data. Results showed that the proposed SA_SSMSRM can generate more accurate superresolution maps than other SRM models.

Superresolution mapping (SRM) is a technique for translating original coarse-resolution fractions into a fine-resolution land cover map by dividing a coarse-resolution pixel into a few finer resolution pixels and determining their class labels. SRM can be solved by considering it a maximum a posteriori principle-based classification problem and by assigning each fine pixel as the class with the highest probability. Fine-pixel class membership probabilities (CMPs) can be calculated at two different scales: at the fine scale, in which the target fine pixel is compared with other fine pixels, and at the coarse scale, in which coarse fractions are downscaled into fine-pixel probabilities. The fine-scale CMP is suitable for representing local land cover features but not for maintaining global features. The coarse-scale CMP is the opposite of the fine-scale CMP. This paper proposes a novel multiscale approach to overcome this shortcoming by fusing the CMP calculated at both fine and coarse scales with the tau model. With the fused CMP, a simulated-annealing algorithm is applied to produce a fine-resolution land cover map. The land cover maps generated from QuickBird and IKONOS images and the National Land Cover Database were used to validate the effectiveness of the proposed SRM algorithms. The proposed SRM algorithms were evaluated visually and quantitatively by comparing them with several existing SRM algorithms. The results indicate that the accuracy of land cover maps at fine spatial resolution increased significantly compared with that obtained from all existing SRM algorithms.

Superresolution mapping (SRM) is a method of predicting the spatial locations of land cover classes within mixed pixels in remotely sensed images. This paper proposes a novel SRM framework that is operated from the perspective of spatial regularization. Within the proposed framework, SRM aims to generate final superresolution land cover maps that conform to inputted fraction images, with spatial regularization intended for exploiting a priori knowledge about the land cover maps. Two SRM models are constructed by using maximal spatial dependence as the spatial regularization term and the L1 or L2 norm as the data fidelity term. The proposed models are evaluated by using synthetic Landsat, real IKONOS, and real Airborne Visible/Infrared Imaging Spectrometer images and compared with hard classification technologies, as well as pixel-swapping, Hopfield neural network, and Markov random field SRM models. We perform linear spectral mixture analysis (LSMA) and multiple endmember spectral mixture analysis (MESMA) to estimate fraction images. Results show that the accuracy of inputted fraction images plays an important role in the final superresolution land cover maps and that using MESMA fraction images results in higher accuracy than using LSMA fraction images. Moreover, the L-curve criterion is suitable for choosing the optimal regularization parameter in both SRM models. Compared with hard classification technologies and other SRM models, the proposed model derives the highest Kappa coefficients and lowest class area proportion errors when MESMA fraction images are used as input.

Subpixel mapping (SPM) is a technique to predict spatial locations of land cover classes within mixed pixels in remotely sensed imagery. The two-step approach first estimates fraction images by spectral unmixing and then inputs fraction images into an SPM algorithm to generate the final subpixel land cover map. A shortcoming of this approach is that the information about the credibility of fraction images is not considered. In this letter, we proposed a general framework of SPM which is directly applied to original coarse resolution remotely sensed imagery by integrating spectral and spatial information. Based on the proposed framework, the linear unmixing model and the maximal spatial dependence model were combined to construct a novel SPM model aiming to minimize the least squares error of spectral signature and make the subpixel land cover map spatially smooth, simultaneously. By applying to an Airborne Visible/Infrared Imaging Spectrometer hyperspectral image, the proposed model was evaluated both visually and quantitatively by comparing it with hard classification and the two-step SPM approach. The results showed that the regularization parameter, which balances the influence of spectral and spatial terms, plays an important role on the solution. The L-curve approach was a reasonable method to select the regularization parameter, with which an increased accuracy of the proposed model was obtained.

Extracting land cover change (LCC) information at the subpixel scale is important when coarse-resolution remotely sensed images are used for change detection. Although fraction images derived from soft-classification technologies can be used for subpixel LCC detection, the spatial distribution of changed subpixels within each coarse-resolution pixel cannot be provided. This letter presents a subpixel LCC mapping (SLCCM) algorithm, aiming to predict the spatial pattern of LCC at the subpixel scale between bitemporal images through comparing the former high-resolution land cover map and the latter fraction images derived from the coarse-resolution image. The resulting subpixel LCC map is determined by the spatial dependence principle and an LCC rule in each mixed pixel. The proposed algorithm was evaluated with simulated and real images, and the results showed the effectiveness of the proposed method for SLCCM.