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Image super-resolution (SR) methods can generate remote sensing images with high spatial resolution without increasing the cost of acquisition equipment, thereby providing a feasible way to improve the quality of remote sensing images. Clearly, image SR is a severe ill-posed problem. With the development of deep learning, the powerful fitting ability of deep neural networks has solved this problem to some extent. Since the texture information of various remote sensing images are totally different from each other, in this article, we proposed a network based on generative adversarial network (GAN) to achieve high-resolution remote sensing images, named multiattention GAN (MA-GAN). The main body of the generator in MA-GAN contains three blocks: pyramid convolutional residual dense (PCRD) block, attention-based upsampling (AUP) block, and attention-based fusion (AF) block. Specifically, the developed attention pyramid convolutional (AttPConv) operator in the PCRD block combines multiscale convolution and channel attention (CA) to automatically learn and adjust the scale of residuals for better representation. The established AUP block uses pixel attention (PA) to perform arbitrary scales of upsampling. The AF block uses branch attention (BA) to integrate upsampled low-resolution images with high-level features. Besides, the loss function takes both adversarial loss and feature loss into consideration to guide the learning procedure of the generator. We have compared our MA-GAN approach with several state-of-the-art methods on a number of remote sensing scenes, and the experimental results consistently demonstrate the effectiveness of the proposed MA-GAN. For study replication, the source code will be released at: https://github.com/ZhihaoWang1997/MA-GAN. [ABSTRACT FROM AUTHOR]

The rapid development of remote sensing sensors makes the acquisition, analysis, and application of hyperspectral images (HSIs) more and more extensive. However, the limited sample sets, high-dimensional features, highly correlated bands, and mixing spectral information make the classification of HSIs a great challenge. In this article, an unsupervised multiscale and diverse feature learning (UMsDFL) method is proposed for HSI classification, which deeply considers the spatial–spectral features via convolutional neural networks (CNNs). Specifically, after employing the simple noniterative clustering (SNIC) algorithm with the heuristic calculation of superpixel size, the HSIs are segmented into superpixels for feature learning. The unsupervised network is designed with the convolutional encoder and decoder, the additional clustering branch, and the multilayer feature fusion to enhance the distinguishability of feature learning and the reusability of feature maps. Then, the spatial relationships and object attributes in large- and small-scale contexts are learned collaboratively through the unsupervised network to utilize the complementary multiscale characteristics. Moreover, the diverse features of hyperspectral information and nonsubsampled contourlet transform (NSCT) textures are learned simultaneously via the unsupervised network to alleviate the insufficiency of geometric representation. Finally, the random forest (RF) is adopted as the comprehensive classifier for land cover mapping based on the UMsDFL, and superpixel regularization is adopted to optimize the classification results. A series of experiments are performed on three real-world HSI datasets to demonstrate the effectiveness of our UMsDFL approach. The experimental results show that the proposed UMsDFL can achieve the overall accuracy of 79.23%, 96.49%, and 77.26% for Houston, Pavia, and Dioni datasets, respectively, when there are only five samples per class for training. [ABSTRACT FROM AUTHOR]

Due to the detailed spectral information through hundreds of narrow spectral bands provided by hyperspectral image (HSI) data, it can be employed to accurately classify diverse materials of interest, which is one of the core applications of hyperspectral remote sensing technology. In recent years, with the rapid development of deep learning, convolutional neural networks (CNNs) have been successfully applied in many fields, including HSI classification. However, the random gradient descent-based parameter updating scheme is too general and leading to the inefficiency of CNN models. Moreover, the high dimensionality and limited training samples of HSI data also exacerbate the overfitting problem. To tackle these issues, in this article, a novel deep network with multilayer and multibranch architecture, named 3-D Gabor CNN (3DG-CNN), is proposed for HSI classification. More precisely, since the predefined 3-D Gabor filters in multiple scales and orientations could well characterize the internal spatial–spectral structure of HSI data from various perspectives, the 3-D Gabor-modulated kernels (3-D GMKs) are employed to replace the random initialization kernels. Moreover, the specially designed multibranch architecture enables the network to better integrating the scalable property of 3-D Gabor filters; thus, the representative ability and robustness of the extracted features can be greatly improved. Alternatively, the number of network parameters is substantially reduced due to the incorporation of 3-D Gabor modulation, relieving the training complexity and also alleviating the training process from overfitting. Experimental results on four real HSI datasets (including two newly released ones in the literature) have demonstrated that the proposed 3DG-CNN model can achieve better performance than several widely used machine-learning-based and deep-learning-based approaches. For the sake of reproducibility, the codes of the proposed 3DG-CNN model are available at http://jiasen.tech/papers/. [ABSTRACT FROM AUTHOR]

Hyperspectral images encompass abundant information and provide unique characteristics for material classification. However, the labeling of training samples can be challenging in hyperspectral image classification. To address this problem, this study proposes a framework named flexible Gabor-based superpixel-level unsupervised linear discriminant analysis (FG-Su ULDA) to extract the most informative and discriminating features for classification. First, a number of 3-D flexible Gabor filters are rigorously designed using an asymmetric sinusoidal wave to sufficiently characterize the spatial–spectral structure in hyperspectral images. Then, an unsupervised linear discriminant analysis strategy guided by the entropy rate superpixel (ERS) segmentation algorithm, called Su ULDA, is skillfully introduced to reduce the extracted large amount of FG features. The Su ULDA method not only boosts the classification capability but also increases the peculiarity of features, with the aid of superpixel information. Finally, the achieved features are imported to the popular support vector machine classifier. The proposed FG-Su ULDA framework is applied to four real hyperspectral image data sets, and the experiments constantly prove that our FG-Su ULDA is superior to several state-of-the-art methods in both classification performance and computational efficiency, especially with scarce training samples. The codes of this work are available at http://jiasen.tech/papers/ for the sake of reproducibility. [ABSTRACT FROM AUTHOR]

Due to the importance in many military and civilian applications, hyperspectral anomaly detection has attracted remarkable interest. Low-rank representation (LRR)-based anomaly detectors use the low-rank property to represent background pixels, and pixels that cannot be well represented are detected as anomalies. The ability of an LRR-based detector to separate background pixels and anomalous pixels depends on the dictionary representation ability, which usually can be enhanced by designing a proper prior for dictionary representation coefficients and constructing a better dictionary. However, it is not easy to handcraft effective and meaningful regularizers for dictionary coefficients. In this article, we propose a novel anomaly detection algorithm that uses a plug-and-play prior for representation coefficients and constructs a new dictionary based on clustering. Instead of cumbersomely handcrafting a regularizer for representation coefficients, we propose solving the anomaly detection problem using the plug-and-play framework, which enables us to plug state-of-the-art priors for representation coefficients. An effective convolutional neural network (CNN) denoiser is plugged into our framework to fully exploit the spatial correlation of representation coefficients. We also propose a modified background dictionary construction method, which carefully includes background pixels and excludes anomalous pixels from clustering results. We refer to the proposed anomaly detection method as plug-and-play denoising CNN regularized anomaly detection (DeCNN-AD) method. Extensive experiments were performed on five data sets in a comparison with eight state-of-the-art anomaly detection methods. The experimental results suggest that the proposed method is effective in anomaly detection and can produce better anomaly detection results than that of the comparison methods. The codes of this work will be available at https://github.com/FxyPd for the sake of reproducibility. [ABSTRACT FROM AUTHOR]

In the hyperspectral image, each pixel corresponds to a small area on the Earth’s surface and represents the intrinsic characteristic of objects, which can be applied for recognition of land covers. Nevertheless, hyperspectral image processing should face some critical issues, and a small sample set problem may be the most challenging one in the research. Deep learning (DL), which has successfully been applied in many fields, has also been introduced for hyperspectral image classification. However, the large gap between the massive parameters to be tuned and limited labeled samples can lead to overfitting scenario, inevitably deteriorating the generalization ability of the DL model. In this article, a lightweight convolutional neural network (LWCNN) is proposed for hyperspectral image classification to mainly tackle the small sample set problem. Especially, spatial–spectral Schroedinger eigenmaps (SSSE) feature extraction is first adopted to obtain the joint spatial–spectral information, and the compressed dimensionality could significantly reduce the number of parameters in the following DL model. Second, a dual-scale convolution (DSC) module is carefully designed to address the SSSE features from a 1-D vector viewpoint (the number of parameters is further decreased), and the DSC procedure is successively employed to obtain the hierarchical structure description that could represent data distribution from different aspects. Subsequently, the feature vectors from all DSC layers are separately filtered by a new bichannel fusion (BCF) module, which could well encode both the intrinsic and contextual information inside DSC features. Finally, the filtered features are concatenated together and imported into a global average pooling classifier to achieve the predicted probability of each category. Experimental results on three famous hyperspectral image data sets illustrate that the developed LWCNN approach is advantageous in both the efficiency and robustness sides for hyperspectral image classification tasks and outperforms other state-of-the-art methods (both traditional-based and DL-based) with very limited labeled samples. [ABSTRACT FROM AUTHOR]

The rapid increase in the number of remote sensing sensors makes it possible to develop multisource feature extraction and fusion techniques to improve the classification accuracy of surface materials. It has been reported that light detection and ranging (LiDAR) data can contribute complementary information to hyperspectral images (HSIs). In this article, a multiple feature-based superpixel-level decision fusion (MFSuDF) method is proposed for HSIs and LiDAR data classification. Specifically, superpixel-guided kernel principal component analysis (KPCA) is first designed and applied to HSIs to both reduce the dimensions and compress the noise impact. Next, 2-D and 3-D Gabor filters are, respectively, employed on the KPCA-reduced HSIs and LiDAR data to obtain discriminative Gabor features, and the magnitude and phase information are both taken into account. Three different modules, including the raw data-based feature cube (concatenated KPCA-reduced HSIs and LiDAR data), the Gabor magnitude feature cube, and the Gabor phase feature cube (concatenation of the corresponding Gabor features extracted from the KPCA-reduced HSIs and LiDAR data), can be, thus, achieved. After that, random forest (RF) classifier and quadrant bit coding (QBC) are introduced to separately accomplish the classification task on the aforementioned three extracted feature cubes. Alternatively, two superpixel maps are generated by utilizing the multichannel simple noniterative clustering (SNIC) and entropy rate superpixel segmentation (ERS) algorithms on the combined HSIs and LiDAR data, which are then used to regularize the three classification maps. Finally, a weighted majority voting-based decision fusion strategy is incorporated to effectively enhance the joint use of the multisource data. The proposed approach is, thus, named MFSuDF. A series of experiments are conducted on three real-world data sets to demonstrate the effectiveness of the proposed MFSuDF approach. The experimental results show that our MFSuDF can achieve the overall accuracy of 73.64%, 93.88%, and 74.11% for Houston, Trento, and Missouri University and University of Florida (MUUFL) Gulport data sets, respectively, when there are only three samples per class for training. [ABSTRACT FROM AUTHOR]

As a typical graph-based semisupervised learning technique, the label propagation (LP) approach has gained much attention in recent years. The key to LP algorithms is the propagation capability and efficiency of the similarity matrix, which describes the similarity between two data points. Concerning hyperspectral image which often contains hundreds of thousands of pixels, the corresponding similarity matrix is particularly huge and thus the LP procedure is intractable. Fortunately, superpixel, which can effectively characterize the spatial semantic information of surface objects, provides a reasonable way to solve this problem. In this article, we propose an elaborate superpixel-based weighted LP approach, abbreviated as SuWLP, for hyperspectral image classification. First, the hyperspectral image is oversegmented by the entropy rate segmentation (ERS) method, and the internal consistency of each superpixel can be achieved. Second, a new similarity measure is designed to estimate the similarity between two superpixels, and a superpixel-based similarity matrix can be thus established. Third, after the training samples have been expanded based on the superpixel distribution, a weighted LP technique is designed to propagate the sample label at the superpixel level without any parameter tuning. Finally, the label of each superpixel maps back to the contained pixels. We compared our proposed SuWLP method with several state-of-the-art ones, and experimental results on three real hyperspectral data sets certify the effectiveness and efficiency of the superpixel-level LP strategy. [ABSTRACT FROM AUTHOR]

Learning from a limited number of labeled samples (pixels) remains a key challenge in the hyperspectral image (HSI) classification. To address this issue, we propose a deep metric learning-based feature embedding model, which can meet the tasks both for same- and cross-scene HSI classifications. In the first task, when only a few labeled samples are available, we employ ideas from metric learning based on deep embedding features and make a similarity learning between pairs of samples. In this case, the proposed model can learn well to compare whether two samples belong to the same class. In another task, when an HSI image (target scene) that needs to be classified is not labeled at all, the embedding model can learn from another similar HSI image (source scene) with sufficient labeled samples and then transfer to the target model by using an unsupervised domain adaptation technique, which not only employs the adversarial approach to make the embedding features from the source and target samples indistinguishable but also encourages the target scene’s embeddings to form similar clusters with the source scene one. After the domain adaptation between the HSIs of the two scenes is finished, any traditional HSI classifier can be used. In a simple manner, the nearest neighbor (NN) algorithm is selected as the classifier for the classification tasks throughout this article. The experimental results from a series of popular HSIs demonstrate the advantages of the proposed model both in the same- and cross-scene classification tasks. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing imagery provides valuable and rich information to distinguish the characteristics of materials. However, this advantage of hyperspectral imagery often encounters the problem of a limited amount of training samples, which is caused by the difficulty of manually labeling. Fortunately, the spatial distribution of surface objects can be integrated with the spectral signature to improve the discriminative ability. In this paper, a 3-D Gaussian–Gabor feature extraction and selection framework has been proposed for hyperspectral image classification. First, a bank of 3-D Gaussian–Gabor filters are convolved with the concatenated data of both extended multi-attribute profile (EMAP) features and raw hyperspectral data. Second, an improved fast density peak clustering (IFDPC) method is introduced to select the most representative features from each extracted 3-D Gaussian–Gabor feature cube. Finally, the retained features are combined together to accomplish the classification task. The proposed method is thus named as GG-IFDPC. Three real hyperspectral imagery data sets have been utilized, and the experiments demonstrate the advantages of the proposed GG-IFDPC approach over the compared ones. [ABSTRACT FROM AUTHOR]

In virtue of the spatial structural characteristic of surface materials, the performance of the hyperspectral image classification can be boosted by incorporating texture information. Normally, the spatial structure can be extracted by predefined operators, including the popular extended multiattribute profiles (EMAPs) and the Gabor filters. Recently, superpixel segmentation, which reflects the homogeneous regularity of objects, has drawn much attention in the field. In this paper, a collaborative representation-based multiscale superpixel fusion (CRMSF) approach has been proposed for the hyperspectral image classification. First, after obtaining the EMAPs from the raw hyperspectral image, a group of predesigned 3-D Gabor wavelet filters is convolved with the EMAP features, and the EMAP-Gabor features can, thus, be achieved. Second, the collaborative representation-based classification (CRC) is employed to fully and efficiently make use of the huge amount of extracted EMAP-Gabor features. Third, multiscale superpixel maps are generated from the EMAP features that are utilized to regularize the classification map obtained by CRC. A heuristic strategy has been specially devised to automatically decide the number of extracted superpixels in multiple scales, which can be perfectly compatible with hyperspectral images having various spatial sizes and spatial resolutions. This is the most important contribution of the developed CRMSF approach. Finally, the classification task is accomplished by fusing the multiple regularized classification maps. The CRMSF approach has been evaluated on four popular hyperspectral image data sets, and the experimental results show the advantages of CRMSF, particularly for a hyperspectral image with high spatial resolution. [ABSTRACT FROM AUTHOR]

Since the spatial distribution of surface materials is usually regular and locally continuous, it is reasonable to utilize the spectral and spatial information for the hyperspectral image classification. In this paper, a spectral–spatial Gabor surface feature (GSF) fusion approach has been proposed for hyperspectral image classification. First, Gabor magnitude pictures (GMPs) are extracted by applying a set of predefined 2-D Gabor filters to hyperspectral images. Second, the GSF has been extended to the spectral–spatial domains to comply with the 3-D structure of hyperspectral imagery, called 3-DGSF, which utilizes the first-order derivative of GMPs. Meanwhile, a classic superpixel segmentation method, called simple linear iterative clustering (SLIC), is adopted to divide the original hyperspectral image into disjoint superpixels. Third, principal component analysis is adopted to reduce the dimensionality of each extracted 3-DGSF feature cube. Next, a support vector machine classifier is applied on each reduced 3-DGSF features, and the majority voting strategy is used to obtain the classification results. Finally, the superpixel map obtained by SLIC is used to regularize the classification map, and thus, the proposed approach is named as S3-DGSF. Extensive experiments on three real hyperspectral data sets have demonstrated the higher performance of the proposed S3-DGSF approach over several state-of-the-art methods in the literature. [ABSTRACT FROM AUTHOR]

Due to the 3-D nature of hyperspectral images, as well as the spatial properties (such as regularity and continuity) of land covers, many 3-D feature extraction operators have been designed to fully exploit the joint spatial–spectral information. However, the large amount of obtained features can suffer from the “curse of dimensionality” problem, especially for the small training sample set. Moreover, various spatial–spectral features can represent the characteristics of the hyperspectral image from different aspects. In this paper, a multiple 3-D feature fusion framework (M3DF3) has been proposed for hyperspectral image classification. First, we extend the 2-D Gabor surface feature into 3-D (3DSF) domains to comply with the spatial–spectral structure of the hyperspectral image, which is directly applied on the original hyperspectral image instead of the Gabor features. Second, three 3-D feature extraction methods, including the 3-D morphological profile, the 3-D local binary pattern, and the proposed 3DSF, that, respectively, characterize the hyperspectral image from three different angles, i.e., morphology, local dependence, and shape smoothness, are fused under a multitask sparse representation framework to take full advantage of the multiple 3-D features together. The proposed M3DF3 approach was fully tested on three real-world hyperspectral image data, i.e., the widely used Indian Pines, Pavia University, and Houston University. The results show that our method can achieve as high as 68.22%, 79.44%, and 72.84% accuracies, respectively, even when only few samples, i.e., three samples per class, are used for training. [ABSTRACT FROM AUTHOR]

Due to the high spectral dimensionality of hyperspectral images as well as the difficult and time-consuming process of collecting sufficient labeled samples in practice, the small sample size scenario is one crucial problem and a challenging issue for hyperspectral image classification. Fortunately, the structure information of materials, reflecting region of homogeneity in the spatial domain, offers an invaluable complement to the spectral information. Assuming some spatial regularity and locality of surface materials, it is reasonable to segment the image into different homogeneous parts in advance, called superpixel, which can be used to improve the classification performance. In this paper, a superpixel-based multitask learning framework has been proposed for hyperspectral image classification. Specifically, a set of 2-D Gabor filters are first applied to hyperspectral images to extract discriminative features. Meanwhile, a superpixel map is generated from the hyperspectral images. Second, a superpixel-based spatial–spectral Schroedinger eigenmaps (S4E) method is adopted to effectively reduce the dimensions of each extracted Gabor cube. Finally, the classification is carried out by a support vector machine (SVM)-based multitask learning framework. The proposed approach is thus termed Gabor S4E and SVM-based multitask learning (GS4E-MTLSVM). A series of experiments is conducted on three real hyperspectral image data sets to demonstrate the effectiveness of the proposed GS4E-MTLSVM approach. The experimental results show that the performance of the proposed GS4E-MTLSVM is better than those of several state-of-the-art methods, while the computational complexity has been greatly reduced, compared with the pixel-based spatial–spectral Schroedinger eigenmaps method. [ABSTRACT FROM PUBLISHER]

The local binary pattern (LBP) is a simple and efficient texture descriptor for image processing. Recently, LBP has been introduced for feature extraction of hyperspectral imagery. Specifically, the LBP codes are extracted from the 2-D band images to capture the spatial correlation among neighboring pixels, and then the statistical histogram features from all bands, which could estimate the underlying distribution in local area, are concatenated together for pixel-wise classification. However, since hyperspectral imagery contains rich spectral and spatial information, which is actually a 3-D data cube, the 2-D LBP (2-DLBP) model cannot fully exploit the joint spectral–spatial structure. In this paper, the 2-DLBP has been extended into 3-D LBP (3-DLBP) model through forming a 3-D regular octahedral frame to characterize the spectral–spatial relationship. In order to reflect the local continuous property of hyperspectral data in both the spectral and spatial domains, while ensuring the rotational invariance of the 3-DLBP model, the code patterns of 3-DLBP model have been divided into eight groups (including seven groups of “dense” patterns and one group of “nondense” patterns) based on the consistency of spectral–spatial topology structure. Specifically, the patterns in seven “dense” groups correspond to the microstructures in the 3-D domains (such as spots, edges, and flat areas), which has a high percentage in all the 3-DLBP patterns, while the rest patterns are aggregated and treated as the “nondense” patterns. The proposed method is thus called 3-D dense LBP (3-D2LBP) model. Moreover, instead of taking zero as the hard threshold, a slack variable has been introduced to enable the difference between the central pixel and the neighboring ones varying in a small interval, which could greatly decrease the impact of spectral variability and noise, and the discriminative power of the features has been further boosted. The slack threshold-based 3-D2LBP model is named ST-3-D2LBP. A series of experiments is conducted on three real hyperspectral imageries to demonstrate the effectiveness of the proposed two 3-D2LBP-based methods. The experimental results show that the performance of the proposed ST-3-D2LBP is significantly superior to that of 2-DLBP, which is also better than the 3-D2LBP model and several state-of-the-art hyperspectral classification methods. [ABSTRACT FROM PUBLISHER]

The large amount of spectral and spatial information contained in hyperspectral imagery has provided great opportunity to effectively characterize and identify the surface materials of interest. As a novel feature extraction technique, a series of Gabor wavelet filters with different scales and frequencies was applied on hyperspectral data to extract spectral–spatial-combined features, which produced impressive performance on pixel-oriented classification. However, the incredibly large number of Gabor features could cause too much burden for onboard computation, limiting the efficiency of the method. To make matters worse, due to the nonhomogeneous spatial distribution of materials as well as the different characteristics of the constructed Gabor filters, some Gabor features could have a smaller or even negative impact on material representation, deteriorating the classification accuracy eventually. In this paper, a Gabor cube selection based multitask joint sparse representation approach, abbreviated as GS-MTJSRC, was proposed for hyperspectral image classification. First, based on the Fisher discrimination criterion, the most representative Gabor cubes for each class were picked out. Next, under multitask joint sparse representation framework, a coefficient vector could be obtained for each test sample with the selected Gabor cube features, which could be directly used for the following residual-based classification. Experimental results on three real hyperspectral data sets with different characteristics and spatial resolutions demonstrated the feasibility and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Through imaging the same spatial area by hyperspectral sensors at different spectral wavelengths simultaneously, the acquired hyperspectral imagery often contains hundreds of band images, which provide the possibility to accurately analyze and identify a ground object. However, due to the difficulty of obtaining sufficient labeled training samples in practice, the high number of spectral bands unavoidably leads to the problem of a “dimensionality disaster” (also called the Hughes phenomenon), and dimensionality reduction should be applied. Concerning band (or feature) selection, conventional methods choose the representative bands by ranking the bands with defined metrics (such as non-Gaussianity) or by formulating the band selection problem as a clustering procedure. Because of the different but complementary advantages of the two kinds of methods, it can be beneficial to use both methods together to accomplish the band selection task. Recently, a fast density-peak-based clustering (FDPC) algorithm has been proposed. Based on the computation of the local density and the intracluster distance of each point, the product of the two factors is sorted in decreasing order, and cluster centers are recognized as points with anomalously large values; hence, the FDPC algorithm can be considered a ranking-based clustering method. In this paper, the FDPC algorithm has been enhanced to make it suitable for hyperspectral band selection. First, the ranking score of each band is computed by weighting the normalized local density and the intracluster distance rather than equally taking them into account. Second, an exponential-based learning rule is employed to adjust the cutoff threshold for a different number of selected bands, where it is fixed in the FDPC. The proposed approach is thus named the enhanced FDPC (E-FDPC). Furthermore, an effective strategy, which is called the isolated-point-stopping criterion, is developed to automatically determine the appropriate number of bands to be selected. That is, the clustering process will be stopped by the emergence of an isolated point (the only point in one cluster). Experimental results on three real hyperspectral data demonstrate that the bands selected by our E-FDPC approach could achieve higher classification accuracy than the FDPC and other state-of-the-art band selection techniques, whereas the isolated-point-stopping criterion is a reasonable way to determine the preferable number of bands to be selected. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing is a crucial preprocessing step for material classification and recognition. In the last decade, nonnegative matrix factorization (NMF) and its extensions have been intensively studied to unmix hyperspectral imagery and recover the material end-members. As an important constraint for NMF, sparsity has been modeled making use of the L1 regularizer. Unfortunately, the L1 regularizer cannot enforce further sparsity when the full additivity constraint of material abundances is used, hence limiting the practical efficacy of NMF methods in hyperspectral unmixing. In this paper, we extend the NMF method by incorporating the L1/2 sparsity constraint, which we name L1/2-NMF. The L1/2 regularizer not only induces sparsity but is also a better choice among Lq(0 < q < 1) regularizers. We propose an iterative estimation algorithm for L1/2-NMF, which provides sparser and more accurate results than those delivered using the L1 norm. We illustrate the utility of our method on synthetic and real hyperspectral data and compare our results to those yielded by other state-of-the-art methods. [ABSTRACT FROM AUTHOR]