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The hyperspectral subpixel mapping (SPM) technique can generate a land-cover map at the subpixel scale by modeling the relationship between the abundance map and the spatial distribution image of the subpixels. However, this is an inverse ill-posed problem. The most widely used way to resolve the problem is to introduce additional information as a regularization term and acquire the unique optimal solution. However, the regularization parameter either needs to be determined manually or it cannot be determined in a fully adaptive manner. Thus, in this paper, the multiobjective subpixel landcover mapping (MOSM) framework for hyperspectral remote sensing imagery is proposed, in which the two function terms [the fidelity term and the prior term (i.e., the regularization term)] can be optimized simultaneously, and there is no need to determine the regularization parameter explicitly. In order to achieve this goal, two strategies are designed in MOSM: 1) a high-resolution distribution image-based individual encoding strategy is designed in order to calculate the prior term accurately and 2) a subfitness-based individual comparison strategy is designed in order to generate subpixel land-cover mapping solutions with a high quality to update the population. Four data sets (one simulated, two synthetic, and one real hyperspectral image) were used to test the proposed method. The experimental results show that MOSM can perform better than the other subpixel land-cover mapping methods, demonstrating the effectiveness of MOSM in balancing the fidelity term and prior term in the SPM model. [ABSTRACT FROM AUTHOR]

As the acquisition of very high resolution (VHR) satellite images becomes easier owing to technological advancements, ever more stringent requirements are being imposed on automatic image interpretation. Moreover, per-pixel classification has become the focus of research interests in this regard. However, the efficient and effective processing and the interpretation of VHR satellite images remain a critical task. Convolutional neural networks (CNNs) have recently been applied to VHR satellite images with considerable success. However, the prevalent CNN models accept input data of fixed sizes and train the classifier using features extracted directly from the convolutional stages or the fully connected layers, which cannot yield pixel-to-pixel classifications. Moreover, training a CNN model requires large amounts of labeled reference data. These are challenging to obtain because per-pixel labeled VHR satellite images are not open access. In this paper, we propose a framework called the unsupervised-restricted deconvolutional neural network (URDNN). It can solve these problems by learning an end-to-end and pixel-to-pixel classification and handling a VHR classification using a fully convolutional network and a small number of labeled pixels. In URDNN, supervised learning is always under the restriction of unsupervised learning, which serves to constrain and aid supervised training in learning more generalized and abstract feature. To some degree, it will try to reduce the problems of overfitting and undertraining, which arise from the scarcity of labeled training data, and to gain better classification results using fewer training samples. It improves the generality of the classification model. We tested the proposed URDNN on images from the Geoeye and Quickbird sensors and obtained satisfactory results with the highest overall accuracy (OA) achieved as 0.977 and 0.989, respectively. Experiments showed that the combined effects of additional kernels and stages may have produced better results, and two-stage URDNN consistently produced a more stable result. We compared URDNN with four methods and found that with a small ratio of selected labeled data items, it yielded the highest and most stable results, whereas the accuracy values of the other methods quickly decreased. For some categories with fewer training pixels, accuracy for categories from other methods was considerably worse than that in URDNN, with the largest difference reaching almost 10%. Hence, the proposed URDNN can successfully handle the VHR image classification using a small number of labeled pixels. Furthermore, it is more effective than state-of-the-art methods. [ABSTRACT FROM PUBLISHER]

In the field of high spatial resolution (HSR) remote sensing imagery classification, object-oriented classification and conditional random field (CRF) approaches are widely used due to their ability to incorporate the spatial contextual information. However, the selection of the optimal segmentation scale in object- oriented classification is not an easy task, and some pairwise CRF models always show an oversmooth performance. In this paper, a detail-preserving smoothing classifier based on conditional random fields (DPSCRF) for HSR imagery is proposed to apply the object-oriented strategy in the CRF classification framework, thus integrating the merits of both approaches to consider the spatial contextual information and preserve the detail information in the classification. The DPSCRF model defines suitable potential functions based on the CRF model for HSR image classification, which comprise the spatial smoothing and local class label cost terms. Both terms favor spatial smoothing in a local neighborhood to consider the spatial information. In addition, the local class label cost also considers the different label information of neighboring pixels at each iterative step in the classification to preserve the detail information. In order to deal with the spectral variability of HSR imagery, a segmentation prior is used by the object-oriented processing strategy. This models the probability of each pixel based on the segmentation regions obtained by the connected-component labeling algorithm. The experimental results with three HSR images demonstrate that the proposed classification algorithm shows a competitive performance in both the quantitative and the qualitative evaluation when compared to the other state-of-the-art classification algorithms. [ABSTRACT FROM AUTHOR]

Due to the rapid technological development of various different satellite sensors, a huge volume of high-resolution image data sets can now be acquired. How to efficiently represent and recognize the scenes from such high-resolution image data has become a critical task. In this paper, we propose an unsupervised feature learning framework for scene classification. By using the saliency detection algorithm, we extract a representative set of patches from the salient regions in the image data set. These unlabeled data patches are exploited by an unsupervised feature learning method to learn a set of feature extractors which are robust and efficient and do not need elaborately designed descriptors such as the scale-invariant-feature-transform-based algorithm. We show that the statistics generated from the learned feature extractors can characterize a complex scene very well and can produce excellent classification accuracy. In order to reduce overfitting in the feature learning step, we further employ a recently developed regularization method called “dropout,” which has proved to be very effective in image classification. In the experiments, the proposed method was applied to two challenging high-resolution data sets: the UC Merced data set containing 21 different aerial scene categories with a submeter resolution and the Sydney data set containing seven land-use categories with a 60-cm spatial resolution. The proposed method obtained results that were equal to or even better than the previous best results with the UC Merced data set, and it also obtained the highest accuracy with the Sydney data set, demonstrating that the proposed unsupervised-feature-learning-based scene classification method provides more accurate classification results than the other latent-Dirichlet-allocation-based methods and the sparse coding method. [ABSTRACT FROM PUBLISHER]

Hyperspectral image classification has been an active topic of research in recent years. In the past, many different types of features have been extracted (using both linear and nonlinear strategies) for classification problems. On the one hand, some approaches have exploited the original spectral information or other features linearly derived from such information in order to have classes which are linearly separable. On the other hand, other techniques have exploited features obtained through nonlinear transformations intended to reduce data dimensionality, to better model the inherent nonlinearity of the original data (e.g., kernels) or to adequately exploit the spatial information contained in the scene (e.g., using morphological analysis). Special attention has been given to techniques able to exploit a single kind of features, such as composite kernel learning or multiple kernel learning, developed in order to deal with multiple kernels. However, few approaches have been designed to integrate multiple types of features extracted from both linear and nonlinear transformations. In this paper, we develop a new framework for the classification of hyperspectral scenes that pursues the combination of multiple features. The ultimate goal of the proposed framework is to be able to cope with linear and nonlinear class boundaries present in the data, thus following the two main mixing models considered for hyperspectral data interpretation. An important characteristic of the presented approach is that it does not require any regularization parameters to control the weights of considered features so that different types of features can be efficiently exploited and integrated in a collaborative and flexible way. Our experimental results, conducted using a variety of input features and hyperspectral scenes, indicate that the proposed framework for multiple feature learning provides state-of-the-art classification results without significantly increasing computational complexity. [ABSTRACT FROM PUBLISHER]

In this paper, a new sparse representation-based binary hypothesis (SRBBH) model for hyperspectral target detection is proposed. The proposed approach relies on the binary hypothesis model of an unknown sample induced by sparse representation. The sample can be sparsely represented by the training samples from the background-only dictionary under the null hypothesis and the training samples from the target and background dictionary under the alternative hypothesis. The sparse vectors in the model can be recovered by a greedy algorithm, and the same sparsity levels are employed for both hypotheses. Thus, the recovery process leads to a competition between the background-only subspace and the target and background subspace, which are directly represented by the different hypotheses. The detection decision can be made by comparing the reconstruction residuals under the different hypotheses. Extensive experiments were carried out on hyperspectral images, which reveal that the SRBBH model shows an outstanding detection performance. [ABSTRACT FROM PUBLISHER]

The subpixel mapping technique can specify the spatial distribution of different categories at the subpixel scale by converting the abundance map into a higher resolution image, based on the assumption of spatial dependence. Traditional subpixel mapping algorithms only utilize the low-resolution image obtained by the classification image downsampling and do not consider the spectral unmixing error, which is difficult to account for in real applications. In this paper, to improve the accuracy of the subpixel mapping, an adaptive subpixel mapping method based on a maximum a posteriori (MAP) model and a winner-take-all class determination strategy, namely, AMCDSM, is proposed for hyperspectral remote sensing imagery. In AMCDSM, to better simulate a real remote sensing scene, the low-resolution abundance images are obtained by the spectral unmixing method from the downsampled original image or real low-resolution images. The MAP model is extended by considering the spatial prior models (Laplacian, total variation (TV), and bilateral TV) to obtain the high-resolution subpixel distribution map. To avoid the setting of the regularization parameter, an adaptive parameter selection method is designed to acquire the optimal subpixel mapping results. In addition, in AMCDSM, to take into account the spectral unmixing error in real applications, a winner-take-all strategy is proposed to achieve a better subpixel mapping result. The proposed method was tested on simulated, synthetic, and real hyperspectral images, and the experimental results demonstrate that the AMCDSM algorithm outperforms the traditional subpixel mapping methods and provides a simple and efficient algorithm to regularize the ill-posed subpixel mapping problem. [ABSTRACT FROM PUBLISHER]

Independent component analysis (ICA) has been recently applied into hyperspectral unmixing as a result of its low computation time and its ability to perform without prior information. However, when applying ICA for hyperspectral unmixing, the independence assumption in the ICA model conflicts with the abundance sum-to-one constraint and the abundance nonnegative constraint in the linear mixture model, which affects the hyperspectral unmixing accuracy. In this paper, we consider an abundance matrix composed of Np-dimensional variables, and we propose a new hyperspectral unmixing approach with an abundance characteristic-based ICA model. Two characteristics of the abundance variables are explored, and the model is constructed by these characteristics. A corresponding gradient descent algorithm is also proposed to solve the proposed objective function. Both the synthetic and real experimental results demonstrate that the proposed method performs better than the other state-of-the-art methods in abundance and endmember extraction. [ABSTRACT FROM PUBLISHER]

Due to the high spectral resolution, anomaly detection from hyperspectral images provides a new way to locate potential targets in a scene, especially those targets that are spectrally different from the majority of the data set. Conventional Mahalanobis-distance-based anomaly detection methods depend on the background statistics to construct the anomaly detection metric. One of the main problems with these methods is that the Gaussian distribution assumption of the background may not be reasonable. Furthermore, these methods are also susceptible to contamination of the conventional background covariance matrix by anomaly pixels. This paper proposes a new anomaly detection method by effectively exploiting a robust anomaly degree metric for increasing the separability between anomaly pixels and other background pixels, using discriminative information. First, the manifold feature is used so as to divide the pixels into the potential anomaly part and the potential background part. This procedure is called discriminative information learning. A metric learning method is then performed to obtain the robust anomaly degree measurements. Experiments with three hyperspectral data sets reveal that the proposed method outperforms other current anomaly detection methods. The sensitivity of the method to several important parameters is also investigated. [ABSTRACT FROM PUBLISHER]

With regard to quantitative remote sensing products in the visible and infrared ranges, thick clouds and accompanying shadows are an inevitable source of noise. Due to the absence of adequate supporting information from the data themselves, it is a formidable challenge to accurately restore the surficial information underlying large-scale clouds. In this paper, dictionary learning is expanded into the multitemporal recovery of quantitative data contaminated by thick clouds and shadows. This paper proposes two multitemporal dictionary learning algorithms, expanding on their KSVD and Bayesian counterparts. In order to make better use of the temporal correlations, the expanded KSVD algorithm seeks an optimized temporal path, and the expanded Bayesian method adaptively weights the temporal correlations. In the experiments, the proposed algorithms are applied to a reflectance product and a land surface temperature product, and the respective advantages of the two algorithms are investigated. The results show that, from both the qualitative visual effect and the quantitative objective evaluation, the proposed methods are effective. [ABSTRACT FROM PUBLISHER]

This paper develops several new strategies for remote sensing image classification postprocessing (CPP) and conducts a systematic study in this area. CPP is defined as a refinement of the labeling in a classified image in order to enhance its original classification accuracy. The current mainstream classification methods (preprocessing) extract additional spatial features in order to complement spectral information and enhance classification using spectral responses alone. On the other hand, however, the CPP methods, providing a new solution to improve classification accuracy by refining the initial result, have not received sufficient attention. They have potential for achieving comparable accuracy to the preprocessing methods but in a more direct and succinct way. In this paper, we consider four groups of CPP strategies: filtering; random field; object-based voting; and relearning. In addition to the state-of-the-art CPP algorithms, we also propose a series of new ones, e.g., anisotropic probability diffusion and primitive cooccurrence matrix. In experiments, a number of multisource remote sensing data sets are used for evaluation of the considered CPP algorithms. It is shown that all the CPP strategies are capable of providing more accurate results than the raw classification. Among them, the relearning approaches achieve the best results. In addition, our relearning algorithms are compared with the state-of-the-art spectral-spatial classification. The results obtained further verify the effectiveness of CPP in different remote sensing applications. [ABSTRACT FROM PUBLISHER]

High spatial resolution (HSR) remote sensing imagery provides abundant geometric and detailed information, which is important for classification. In order to make full use of the spatial contextual information, object-oriented classification and pairwise conditional random fields (CRFs) are widely used. However, the segmentation scale choice is a challenging problem in object-oriented classification, and the classification result of pairwise CRF always has an oversmooth appearance. In this paper, a hybrid object-oriented CRF classification framework for HSR imagery, namely, CRF + OO, is proposed to address these problems by integrating object-oriented classification and CRF classification. In CRF + OO, a probabilistic pixel classification is first performed, and then, the classification results of two CRF models with different potential functions are used to obtain the segmentation map by a connected-component labeling algorithm. As a result, an object-level classification fusion scheme can be used, which integrates the object-oriented classifications using a majority voting strategy at the object level to obtain the final classification result. The experimental results using two multispectral HSR images (QuickBird and IKONOS) and a hyperspectral HSR image (HYDICE) demonstrate that the proposed classification framework has a competitive quantitative and qualitative performance for HSR image classification when compared with other state-of-the-art classification algorithms. [ABSTRACT FROM PUBLISHER]

Multitemporal imagery analysis has attracted widespread interest in recent years due to the large number of applications. Multitemporal remote sensing imagery analysis is very important for Earth observation, in order to allow an understanding of the relationships and interactions between human and natural phenomena. Radiometric variance of the same targets due to differences in environmental conditions is one of the most important issues. In this paper, we propose an automatic radiometric normalization method with iterative slow feature analysis (ISFA) to reduce the radiometric variance. Slow feature analysis extracts invariant features from the quickly varying input signals. It is first reformulated for the multitemporal imagery problem and then improved to an iterative version. In the iteration, high weights are assigned to unchanged pixels. After convergence, the linear function of the radiometric normalization is directly obtained with all the pixels and their weights. If the ISFA is negatively affected by the changed pixels in some special cases and cannot find the correct regression line, initial seeds are selected as the initial weights in the iteration, to improve the performance, which is called S-ISFA. Two pairs of multitemporal ETM images from different seasons and years were used to test the effectiveness of our proposed method. The quantitative evaluation showed that our proposed method performs better, with smaller differences in the statistical distributions and radiometric values than the other state-of-the-art methods. The robustness with regard to the selection of initial seeds was also proved in the experiment. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a joint collaborative representation (CR) classification method with multitask learning for hyperspectral imagery. The proposed approach consists of the following aspects. First, several complementary features are extracted from the hyperspectral image. We next apply these features into the unified multitask-learning-based CR framework to acquire a representation vector and adaptive weight for each feature. Finally, the contextual neighborhood information of the image is incorporated into each feature to further improve the classification performance. The experimental results suggest that the proposed algorithm obtains a competitive performance and outperforms other state-of-the-art regression-based classifiers and the classical support vector machine classifier. [ABSTRACT FROM PUBLISHER]

The detection and identification of target pixels such as certain minerals and man-made objects from hyperspectral remote sensing images is of great interest for both civilian and military applications. However, due to the restriction in the spatial resolution of most airborne or satellite hyperspectral sensors, the targets often appear as subpixels in the hyperspectral image (HSI). The observed spectral feature of the desired target pixel (positive sample) is therefore a mixed signature of the reference target spectrum and the background pixels spectra (negative samples), which belong to various land cover classes. In this paper, we propose a novel supervised metric learning (SML) algorithm, which can effectively learn a distance metric for hyperspectral target detection, by which target pixels are easily detected in positive space while the background pixels are pushed into negative space as far as possible. The proposed SML algorithm first maximizes the distance between the positive and negative samples by an objective function of the supervised distance maximization. Then, by considering the variety of the background spectral features, we put a similarity propagation constraint into the SML to simultaneously link the target pixels with positive samples, as well as the background pixels with negative samples, which helps to reject false alarms in the target detection. Finally, a manifold smoothness regularization is imposed on the positive samples to preserve their local geometry in the obtained metric. Based on the public data sets of mineral detection in an Airborne Visible/Infrared Imaging Spectrometer image and fabric and vehicle detection in a Hyperspectral Mapper image, quantitative comparisons of several HSI target detection methods, as well as some state-of-the-art metric learning algorithms, were performed. All the experimental results demonstrate the effectiveness of the proposed SML algorithm for hyperspectral target detection. [ABSTRACT FROM PUBLISHER]

Hyperspectral remote sensing image clustering, with the large volume, high dimensions, and temporal-spatial spectral diversity, is a challenging task due to finding interesting clusters in the sparse feature space. In this paper, a novel hyperspectral clustering algorithm, namely, an unsupervised spectral matching classifier based on artificial DNA computing (UADSM), is proposed to perform the task of clustering different ground objects in specific spectral DNA feature encoding subspaces. UADSM builds up the clustering framework with the spectral encoding, optimizing, and matching mechanism by introducing the basic notions and operators of artificial DNA computing. By discretized spectral DNA feature encoding processing, the spectral shape, amplitude, and slope features of the hyperspectral data are extracted. Furthermore, the optimal clustering centers in the form of DNA strands can be found by recombining the DNA strands in the spectral DNA encoding subspace. Finally, a reasonable category for each spectral signature is automatically identified by the normalized spectral DNA similarity norm. The traditional clustering methods of k-means, ISODATA, fuzzy c-means classifier, and FCM and MoDEFC after principal component analysis transformation are provided to compare with the UADSM classifier, using Hyperspectral Digital Imagery Collection Experiment and Reflective Optics System Imaging Spectrometer hyperspectral images. The experimental results show that the UADSM classifier can achieve the best classification accuracy; hence, it is considered that the UADSM classifier is an effective clustering method for hyperspectral remote sensing imagery. [ABSTRACT FROM PUBLISHER]

Hyperspectral images (HSIs) are often degraded by a mixture of various kinds of noise in the acquisition process, which can include Gaussian noise, impulse noise, dead lines, stripes, and so on. This paper introduces a new HSI restoration method based on low-rank matrix recovery (LRMR), which can simultaneously remove the Gaussian noise, impulse noise, dead lines, and stripes. By lexicographically ordering a patch of the HSI into a 2-D matrix, the low-rank property of the hyperspectral imagery is explored, which suggests that a clean HSI patch can be regarded as a low-rank matrix. We then formulate the HSI restoration problem into an LRMR framework. To further remove the mixed noise, the “Go Decomposition” algorithm is applied to solve the LRMR problem. Several experiments were conducted in both simulated and real data conditions to verify the performance of the proposed LRMR-based HSI restoration method. [ABSTRACT FROM PUBLISHER]

Sparse representation has been widely used in image classification. Sparsity-based algorithms are, however, known to be time consuming. Meanwhile, recent work has shown that it is the collaborative representation (CR) rather than the sparsity constraint that determines the performance of the algorithm. We therefore propose a nonlocal joint CR classification method with a locally adaptive dictionary (NJCRC-LAD) for hyperspectral image (HSI) classification. This paper focuses on the working mechanism of CR and builds the joint collaboration model (JCM). The joint-signal matrix is constructed with the nonlocal pixels of the test pixel. A subdictionary is utilized, which is adaptive to the nonlocal signal matrix instead of the entire dictionary. The proposed NJCRC-LAD method is tested on three HSIs, and the experimental results suggest that the proposed algorithm outperforms the corresponding sparsity-based algorithms and the classical support vector machine hyperspectral classifier. [ABSTRACT FROM PUBLISHER]

A feature selection method based on Cramer's V-test (CV-test) discretization is presented to improve the classification accuracy of remotely sensed imagery. Three possible contributions are pursued in this paper. First of all, a Cramer's V-based discretization (CVD) algorithm is proposed to optimally partition the continuous features into discrete ones. Two association-based feature selection indexes, the CVD-based association index (CVDAI) and the class-attribution interdependence maximization (CAIM)-based association index (CAIMAI), derived from the CV-test value, are then proposed to select the optimal feature subset. Finally, the benefit of using discretized features to improve the performance with the J48 decision tree (J48-DT) and naive Bayes (NB) classifiers is studied. To validate the proposed approaches, a high spatial resolution image and two hyperspectral data sets were used to evaluate the performances of CVD and the associated algorithms. The test performances of discretization using CVD and two other state-of -the-art methods, the CAIM and equal width, show that the CVD-based technique has the better ability to generate a good discretization scheme. Furthermore, the feature selection indexes, CVDAI and CAIMAI, perform better than the other used feature selection methods in terms of overall accuracies achieved by the J48-DT, NB, and support vector machine classifiers. Our tests also show that the use of discretized features benefits the J48-DT and NB classifiers. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hyperspectral image denoising algorithm with a Spatial-spectral view fusion strategy. The idea is to denoise a noisy hyperspectral 3-D cube using the hyperspectral total variation algorithm, but applied to both the spatial and spectral views. A metric Q-weighted fusion algorithm is then adopted to merge the denoising results of the two views together, so that the denoising result is improved. A number of experiments illustrate that the proposed approach can produce a better denoising result than both the individual spatial and spectral view denoising results. [ABSTRACT FROM AUTHOR]

Change detection was one of the earliest and is also one of the most important applications of remote sensing technology. For multispectral images, an effective solution for the change detection problem is to exploit all the available spectral bands to detect the spectral changes. However, in practice, the temporal spectral variance makes it difficult to separate changes and nonchanges. In this paper, we propose a novel slow feature analysis (SFA) algorithm for change detection. Compared with changed pixels, the unchanged ones should be spectrally invariant and varying slowly across the multitemporal images. SFA extracts the most temporally invariant component from the multitemporal images to transform the data into a new feature space. In this feature space, the differences in the unchanged pixels are suppressed so that the changed pixels can be better separated. Three SFA change detection approaches, comprising unsupervised SFA, supervised SFA, and iterative SFA, are constructed. Experiments on two groups of real Enhanced Thematic Mapper data sets show that our proposed method performs better in detecting changes than the other state-of-the-art change detection methods. [ABSTRACT FROM AUTHOR]

Morphological profiles are widely used for the classification of high-resolution remote sensing images. Images are degraded by morphological operators with different sizes. Profiles, such as the variation of intensities of the pixels, are extracted during the degradation. In this paper, two issues with morphological profiles are addressed. First, we propose using the topographic map of the image, which is autodual and does not require any structural element, for the degradation of the image. Second, we propose extracting other profiles that are more robust than the intensity variations. Classification experiments are carried out on two IKONOS remote sensing images with a 1-m resolution and one Pléiades 1-A image with a 2-m resolution. The efficiencies of the new profiles are validated by the experimental results. [ABSTRACT FROM AUTHOR]

Target detection is one of the most important applications in hyperspectral remote sensing image analysis. However, the state-of-the-art machine-learning-based algorithms for hyperspectral target detection cannot perform well when the training samples, especially for the target samples, are limited in number. This is because the training data and test data are drawn from different distributions in practice and given a small-size training set in a high-dimensional space, traditional learning models without the sparse constraint face the over-fitting problem. Therefore, in this paper, we introduce a novel feature extraction algorithm named sparse transfer manifold embedding (STME), which can effectively and efficiently encode the discriminative information from limited training data and the sample distribution information from unlimited test data to find a low-dimensional feature embedding by a sparse transformation. Technically speaking, STME is particularly designed for hyperspectral target detection by introducing sparse and transfer constraints. As a result of this, it can avoid over-fitting when only very few training samples are provided. The proposed feature extraction algorithm was applied to extensive experiments to detect targets of interest, and STME showed the outstanding detection performance on most of the hyperspectral datasets. [ABSTRACT FROM AUTHOR]

Because of malfunction or noise in 15 out of the 20 detectors, band 6 (1.628-1.652 μm) of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Aqua satellite contains large areas of dead pixel stripes. Therefore, the corresponding high-level products of MODIS are corrupted by this periodic phenomenon. This paper proposes an improved Bayesian dictionary learning algorithm based on the burgeoning compressed sensing theory to solve this problem. Compared with other state-of-the-art methods, the proposed method can adaptively exploit the spectral relations of band 6 and other spectra. The performance of the proposed method is demonstrated by experiments on both simulated Terra and real Aqua images. [ABSTRACT FROM AUTHOR]

Object-based classification, including object-based segmentation and classification, has been applied for the classification of high spatial resolution imagery due to the increase in the spatial resolution and the limited spectral resolution. Because of the independent design of the object-based segmentation and classification in many of the traditional object-based classification methods, additional work is required to select the appropriate segmentation algorithms to match the classification algorithms. The object-based segmentation algorithms, e.g., the fractal net evolution approach (FNEA), have been successfully utilized to provide the homogeneous regions, and are the basis of object-based classification. However, the traditional FNEA algorithm is greatly influenced by the global control strategy of the region-growing procedure. In addition, the existing object classification methods take little account of the object context information, which is important for high spatial-resolution image interpretation. To improve the accuracy of the object-based classification, in this paper, a multiagent object-based classification framework (MAOCF) for high-resolution remote sensing imagery is proposed. The proposed approach avoids the issue of segmentation algorithm selection by unifying the processing of object-based segmentation and classification through the use of a 4-tuple agent model. In the uniform framework, a multiagent object-based segmentation (MAOS) algorithm is proposed to optimally control the procedure of object merging. In addition, a MAOC is proposed to utilize the contextual information from the surrounding objects by taking advantage of the benefits of a multiagent system, e.g., strong interaction, high flexibility, and parallel global control capability. Due to the characteristics of a multiagent system, MAOCF has the potential for a parallel computing ability. Three experiments with different types of images were performed to evaluate the performance of MAOS and MAOC in comparison to other segmentation and classification algorithms: 1) mean-shift segmentation; 2) FNEA; 3) recursive hierarchical segmentation; and 4) the majority voting object-based classification method. The experimental results demonstrate that MAOS and MAOC give a stable performance with high spatial resolution remote-sensing imagery, and are competitive with the other methods. [ABSTRACT FROM AUTHOR]

The existence of mixed pixels is a major problem in remote-sensing image classification. Although the soft classification and spectral unmixing techniques can obtain an abundance of different classes in a pixel to solve the mixed pixel problem, the subpixel spatial attribution of the pixel will still be unknown. The subpixel mapping technique can effectively solve this problem by providing a fine-resolution map of class labels from coarser spectrally unmixed fraction images. However, most traditional subpixel mapping algorithms treat all mixed pixels as an identical type, either boundary-mixed pixel or linear subpixel, leading to incomplete and inaccurate results. To improve the subpixel mapping accuracy, this paper proposes an adaptive subpixel mapping framework based on a multiagent system for remote-sensing imagery. In the proposed multiagent subpixel mapping framework, three kinds of agents, namely, feature detection agents, subpixel mapping agents and decision agents, are designed to solve the subpixel mapping problem. Experiments with artificial images and synthetic remote-sensing images were performed to evaluate the performance of the proposed subpixel mapping algorithm in comparison with the hard classification method and other subpixel mapping algorithms: subpixel mapping based on a back-propagation neural network and the spatial attraction model. The experimental results indicate that the proposed algorithm outperforms the other two subpixel mapping algorithms in reconstructing the different structures in mixed pixels. [ABSTRACT FROM AUTHOR]

Shadows are evident in most aerial images with high resolutions, particularly in urban scenes, and their existence obstructs the image interpretation and the following application, such as classification and target detection. Most current shadow removal methods were proposed for natural images, whereas shadows in remote sensing images show distinct characteristics. We have therefore analyzed the characteristics of shadows in aerial images, and in this paper, we propose a new shadow removal method for aerial images, using nonlocal (NL) operators. In the proposed method, the soft shadow is introduced to replace the traditional binary hard shadow. NL operators are used to regularize the shadow scale and the updated shadow-free image. Furthermore, a spatially adaptive NL regularization is introduced to handle compound shadows. The combination of the soft shadow and NL operators yields satisfying shadow-free results, preserving textures and holding regular color. Different types of shadowed aerial images are employed to verify the proposed method, and the results are compared with two other methods. The experimental results confirm the validity of the proposed method and the advantage of the soft-shadow approach. [ABSTRACT FROM AUTHOR]

Filling dead pixels or removing uninteresting objects is often desired in the applications of remotely sensed images. In this paper, an effective image inpainting technology is presented to solve this task, based on multichannel nonlocal total variation. The proposed approach takes advantage of a nonlocal method, which has a superior performance in dealing with textured images and reconstructing large-scale areas. Furthermore, it makes use of the multichannel data of remotely sensed images to achieve spectral coherence for the reconstruction result. To optimize the proposed variation model, a Bregmanized-operator-splitting algorithm is employed. The proposed inpainting algorithm was tested on simulated and real images. The experimental results verify the efficacy of this algorithm. [ABSTRACT FROM AUTHOR]

In recent years, the resolution of remotely sensed imagery has become increasingly high in both the spectral and spatial domains, which simultaneously provides more plentiful spectral and spatial information. Accordingly, the accurate interpretation of high-resolution imagery depends on effective integration of the spectral, structural and semantic features contained in the images. In this paper, we propose a new multifeature model, aiming to construct a support vector machine (SVM) ensemble combining multiple spectral and spatial features at both pixel and object levels. The features employed in this study include a gray-level co-occurrence matrix, differential morphological profiles, and an urban complexity index. Subsequently, three algorithms are proposed to integrate the multifeature SVMs: certainty voting, probabilistic fusion, and an object-based semantic approach, respectively. The proposed algorithms are compared with other multifeature SVM methods including the vector stacking, feature selection, and composite kernels. Experiments are conducted on the hyperspectral digital imagery collection experiment DC Mall data set and two WorldView-2 data sets. It is found that the multifeature model with semantic-based postprocessing provides more accurate classification results (an accuracy improvement of 1-4% for the three experimental data sets) compared to the voting and probabilistic models. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for the dimensionality reduction (DR) of spectral-spatial features in hyperspectral images (HSIs), under the umbrella of multilinear algebra, i.e., the algebra of tensors. The proposed approach is a tensor extension of conventional supervised manifold-learning-based DR. In particular, we define a tensor organization scheme for representing a pixel's spectral-spatial feature and develop tensor discriminative locality alignment (TDLA) for removing redundant information for subsequent classification. The optimal solution of TDLA is obtained by alternately optimizing each mode of the input tensors. The methods are tested on three public real HSI data sets collected by hyperspectral digital imagery collection experiment, reflective optics system imaging spectrometer, and airborne visible/infrared imaging spectrometer. The classification results show significant improvements in classification accuracies while using a small number of features. [ABSTRACT FROM AUTHOR]

Hyperspectral imagery, which contains hundreds of spectral bands, has the potential to better describe the biological and chemical attributes on the plants than multispectral imagery and has been evaluated in this paper for the purpose of crop yield estimation. The spectrum of each pixel in a hyperspectral image is considered as a linear combinations of the spectra of the vegetation and the bare soil. Recently developed linear unmixing approaches are evaluated in this paper, which automatically extracts the spectra of the vegetation and bare soil from the images. The vegetation abundances are then computed based on the extracted spectra. In order to reduce the influences of this uncertainty and obtain a robust estimation results, the vegetation abundances extracted on two different dates on the same fields are then combined. The experiments are carried on the multidate hyperspectral images taken from two grain sorghum fields. The results show that the correlation coefficients between the vegetation abundances obtained by unsupervised linear unmixing approaches are as good as the results obtained by supervised methods, where the spectra of the vegetation and bare soil are measured in the laboratory. In addition, the combination of vegetation abundances extracted on different dates can improve the correlations (from 0.6 to 0.7). [ABSTRACT FROM AUTHOR]

In hyperspectral remote sensing image classification, multiple features, e.g., spectral, texture, and shape features, are employed to represent pixels from different perspectives. It has been widely acknowledged that properly combining multiple features always results in good classification performance. In this paper, we introduce the patch alignment framework to linearly combine multiple features in the optimal way and obtain a unified low-dimensional representation of these multiple features for subsequent classification. Each feature has its particular contribution to the unified representation determined by simultaneously optimizing the weights in the objective function. This scheme considers the specific statistical properties of each feature to achieve a physically meaningful unified low-dimensional representation of multiple features. Experiments on the classification of the hyperspectral digital imagery collection experiment and reflective optics system imaging spectrometer hyperspectral data sets suggest that this scheme is effective. [ABSTRACT FROM AUTHOR]

The artificial immune network (AIN), a computational intelligence model based on artificial immune systems inspired by the vertebrate immune system, has been widely utilized for pattern recognition and data analysis. However, due to the inherent complexity of current AIN models, their application to multi-/hyperspectral remote sensing image classification has been severely restricted. This paper presents a novel supervised AIN-namely, the artificial antibody network (ABNet), based on immune network theory-aimed at performing multi-/hyperspectral image classification. To construct the ABNet, the artificial antibody population (AB) model was utilized. AB is the set of antibodies where each antibody has two attributes-its center vector and recognizing radius-thus each can recognize all antigens within its recognizing radius. In contrast to the traditional AIN model, ABNet can adaptively obtain these two parameters by evolving the antigens without relying on user-defined parameters in the training step. During the process of training, to enlarge the recognizing range, the immune operators (such as clone, mutation, and selection) were used to enhance the AB model to find better antibody in the feature space, which may recognize as much antigen as possible. After the training process, the trained ABNet was utilized to classify the remote sensing image, exhibiting superior learning abilities. Three experiments with different types of images were performed to evaluate the performance of the proposed algorithm in comparison to other supervised classification algorithms: minimum distance, Gaussian maximum likelihood, back-propagation neural network, and our previously developed artificial immune classifiers-resource-limited classification of remote sensing image and multiple-valued immune network classifier. The experimental results demonstrate that ABNet has remarkable recognizing accuracy and ability to provide effective classification for multi-/hyperspectral remote sensing imagery, superior to other methods. [ABSTRACT FROM AUTHOR]

Subpixel target detection is a challenge in hyperspectral image analysis. As the spatial resolution of hyperspectral imagery is usually limited, subpixel targets only occupy part of the pixel area. In such cases, the spatial characteristics of the targets are hard to acquire, and the only information we can use comes from spectral characteristics. Several kinds of method based on spectral characteristics have been proposed in the past. One is the linear unmixing method, which can provide the abundances of different endmembers in the hyperspectral imagery, including the target abundance. Another focuses on providing statistically reliable rules to separate subpixel targets from their backgrounds. Recently, hybrid detectors combining the aforementioned two methods were put forward, which cannot only figure out the quantitative information of the endmembers but also put this quantitative information into an adaptive matched subspace detector or adaptive cosine/coherent estimate detector to separate the target pixels from the background with statistically reliable rules. However, in these methods, all the endmembers are used to construct the statistical rule, while in most cases only some of the endmembers are actually contained in the pixels. This paper proposes hybrid endmembers selective detectors in which different kinds of endmembers are used according to different pixels to ensure that the true composition of endmembers in each pixel is applied in the detection procedure. Three different types of hyperspectral data were used in our experiments, and our proposed hybrid endmember selective detectors showed better performances than the current hybrid detectors in all the experiments. [ABSTRACT FROM AUTHOR]

Remotely sensed images often suffer from the common problems of stripe noise and random dead pixels. The techniques to recover a good image from the contaminated one are called image destriping (for stripes) and image inpainting (for dead pixels). This paper presents a maximum a posteriori (MAP)-based algorithm for both destriping and inpainting problems. The main advantage of this algorithm is that it can constrain the solution space according to a priori knowledge during the destriping and inpainting processes. In the MAP framework, the likelihood probability density function (PDF) is constructed based on a linear image observation model, and a robust Huber-Markov model is used as the prior PDF. The gradient descent optimization method is employed to produce the desired image. The proposed algorithm has been tested using moderate resolution imaging spectrometer images for destriping and China—Brazil Earth Resource Satellite and QuickBird images for simulated inpainting. The experiment results and quantitative analyses verify the efficacy of this algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive mean-shift (MS) analysis framework is proposed for object extraction and classification of hyperspectral imagery over urban areas. The basic idea is to apply an MS to obtain an object-oriented representation of hyperspectral data and then use support vector machine to interpret the feature set. In order to employ MS for hyperspectral data effectively, a feature-extraction algorithm, nonnegative matrix factorization, is utilized to reduce the high-dimensional feature space. Furthermore, two bandwidth-selection algorithms are proposed for the MS procedure. One is based on the local structures, and the other exploits separability analysis. Experiments are conducted on two hyperspectral data sets, the DC Mall hyperspectral digital-imagery collection experiment and the Purdue campus hyperspectral mapper images. We evaluate and compare the proposed approach with the well-known commercial software eCognition (object-based analysis approach) and an effective spectral/spatial classifier for hyperspectral data, namely, the derivative of the morphological profile. Experimental results show that the proposed MS-based analysis system is robust and obviously outperforms the other methods. [ABSTRACT FROM AUTHOR]

The artificial immune network (AIN), which is a new computational intelligence model based on artificial immune systems inspired by the vertebrate immune system, has been widely utilized for pattern recognition and data analysis. However, due to the inherent complexity of current AIN models, their application to remote-sensing image classification has been rather limited. This paper presents a novel supervised classification algorithm based on a multiple-valued immune network, which is a novel AIN model, to perform remote-sensing image classification. The proposed method trains the immune network using the samples of regions of interest and obtains an immune network with memory to classify the remote-sensing imagery. Two experiments with different types of images are performed to evaluate the performance of the proposed algorithm in comparison with other traditional image classification algorithms: Parallelepiped, Minimum Distance, Maximum Likelihood, and Back-Propagation Neural Network. The results evince that the proposed algorithm consistently outperforms the traditional algorithms in all the experiments and, hence, provides an effective option for processing remote-sensing imagery. [ABSTRACT FROM AUTHOR]

Gaussian Markov random fields (GMRFs) are used to analyze textures. GMRFs measure the interdependence of neighboring pixels within a texture to produce features. In this paper, neighboring pixels are taken into account in a priority sequence according to their distance from the center pixel, and a step-by-step least squares method is proposed to extract a novel set of GMRF texture features, named as PS-GMRF. A complete procedure is first designed to classify texture samples of QuickBird imagery. After texture feature extraction, a sub-set of PS-GMRF features is obtained by the sequential floating forward-selection method. Then, the maximum a posterior iterated conditional mode classification algorithm is used, involving the selected PS-GMRF texture features in combination with spectral features. The experimental results show that the performance of classifying texture samples on high spatial resolution QuickBird satellite imagery is improved when texture features and spectral features are used jointly, and PS-GMRF features have a higher discrimination power compared to the classical GMRF features, making a notable improvement in classification accuracy from 71.84% to 94.01%. On the other hand, it is found that one of the PS-GMRF texture features—the lowest order variance—is effective for residential-area detection. Some results for IKONOS and SPOT-5 images show that the integration of the lowest order variance with spectral features improves the classification accuracy compared to classification with purely spectral features. [ABSTRACT FROM AUTHOR]

Shape and spectra are both important features of high spatial resolution remotely sensed (HSRRS) imagery, and they are concrete manifestation of textures on such imagery. This paper presents a spatial feature index, pixel shape index (PSI), to describe the shape feature in a local area surrounding a pixel. PSI is a pixel-based feature which measures the gray similarity distance in every direction. As merely the shape feature is inadequate for classifying HSRRS imagery, a transformed spectral feature extracted by independent component analysis is added to the input vectors of our classifier, and this replaces the original multispectral bands. Meanwhile, a fast fusion algorithm that integrates both shape and spectral features using the support vector machine has been developed to interpret the complex input vectors. The results by PSI are compared with some spatial features extracted using wavelet transform, gray level co-occurrence matrix, and the length-width extraction algorithm to test its effectiveness. The experiments demonstrate that PSI is capable of describing shape features effectively and result in more accurate classifications than other methods. While it is found that spectral and shape features can complement each other and their integration can improve classification accuracy, the transformed spectral components are also found to be more suitable for classification. [ABSTRACT FROM AUTHOR]

A new method in computational intelligence namely artificial immune systems (AIS), which draw inspiration from the vertebrate immune system, have strong capabilities of pattern recognition. Even though AIS have been successfully utilized in several fields, few applications have been reported in remote sensing. Modern commercial imaging satellites, owing to their large volume of high-resolution imagery, offer greater opportunities for automated image analysis. Hence, we propose a novel unsupervised machine-learning algorithm namely unsupervised artificial immune classifier (UAIC) to perform remote sensing image classification. In addition to their nonlinear classification properties, UAIC possesses biological properties such as clonal selection, immune network, and immune memory. The implementation of UAIC comprises two steps: initially, the first clustering centers are acquired by randomly choosing from the input remote sensing image. Then, the classification task is carried out. This assigns each pixel to the class that maximizes stimulation between the antigen and the antibody. Subsequently, based on the class, the antibody population is evolved and the memory cell pool is updated by immune algorithms until the stopping criterion is met. The classification results are evaluated by comparing with four known algorithms: K-means, ISODATA, fuzzy K-means, and self-organizing map. It is shown that UAIC is an adaptive clustering algorithm, which outperforms other algorithms in all the three experiments we carried out. [ABSTRACT FROM AUTHOR]