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In this paper, a new joint spectral–spatial subpixel mapping model is proposed for hyperspectral remotely sensed imagery. Conventional approaches generally use an intermediate step based on the derivation of fractional abundance maps obtained after a spectral unmixing process, and thus the rich spectral information contained in the original hyperspectral data set may not be utilized fully. In this paper, a concept of subpixel abundance map, which calculates the abundance fraction of each subpixel to belong to a given class, was introduced. This allows us to directly connect the original (coarser) hyperspectral image with the final subpixel result. Furthermore, the proposed approach incorporates the spectral information contained in the original hyperspectral imagery and the concept of spatial dependence to generate a final subpixel mapping result. The proposed approach has been experimentally evaluated using both synthetic and real hyperspectral images, and the obtained results demonstrate that the method achieves better results when compared to other seven subpixel mapping methods. The numerical comparisons are based on different indexes such as the overall accuracy and the CPU time. Moreover, the obtained results are statistically significant at 95% confidence. [ABSTRACT FROM AUTHOR]

Recently, researchers have shown the powerful ability of deep methods with multilayers to extract high-level features and to obtain better performance for hyperspectral image classification. However, a common problem of traditional deep models is that the learned deep models might be suboptimal because of the limited number of training samples, especially for the image with large intraclass variance and low interclass variance. In this paper, novel convolutional neural networks (CNNs) with multiscale convolution (MS-CNNs) are proposed to address this problem by extracting deep multiscale features from the hyperspectral image. Moreover, deep metrics usually accompany with MS-CNNs to improve the representational ability for the hyperspectral image. However, the usual metric learning would make the metric parameters in the learned model tend to behave similarly. This similarity leads to obvious model’s redundancy and, thus, shows negative effects on the description ability of the deep metrics. Traditionally, determinantal point process (DPP) priors, which encourage the learned factors to repulse from one another, can be imposed over these factors to diversify them. Taking advantage of both the MS-CNNs and DPP-based diversity-promoting deep metrics, this paper develops a CNN with multiscale convolution and diversified metric to obtain discriminative features for hyperspectral image classification. Experiments are conducted over four real-world hyperspectral image data sets to show the effectiveness and applicability of the proposed method. Experimental results show that our method is better than original deep models and can produce comparable or even better classification performance in different hyperspectral image data sets with respect to spectral and spectral–spatial features. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging is a widely used technique in remote sensing in which an imaging spectrometer collects hundreds of images (at different wavelength channels) for the same area on the surface of the earth. In the last two decades, several methods (unsupervised, supervised, and semisupervised) have been proposed to deal with the hyperspectral image classification problem. Supervised techniques have been generally more popular, despite the fact that it is difficult to collect labeled samples in real scenarios. In particular, deep neural networks, such as convolutional neural networks (CNNs), have recently shown a great potential to yield high performance in the hyperspectral image classification. However, these techniques require sufficient labeled samples in order to perform properly and generalize well. Obtaining labeled data is expensive and time consuming, and the high dimensionality of hyperspectral data makes it difficult to design classifiers based on limited samples (for instance, CNNs overfit quickly with small training sets). Active learning (AL) can deal with this problem by training the model with a small set of labeled samples that is reinforced by the acquisition of new unlabeled samples. In this paper, we develop a new AL-guided classification model that exploits both the spectral information and the spatial-contextual information in the hyperspectral data. The proposed model makes use of recently developed Bayesian CNNs. Our newly developed technique provides robust classification results when compared with other state-of-the-art techniques for hyperspectral image classification. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for spectral unmixing of remotely sensed hyperspectral data. It exploits probabilistic latent topics in order to take advantage of the semantics pervading the latent topic space when identifying spectral signatures and estimating fractional abundances from hyperspectral images. Despite the contrasted potential of topic models to uncover image semantics, they have been merely used in hyperspectral unmixing as a straightforward data decomposition process. This limits their actual capabilities to provide semantic representations of the spectral data. The proposed model, called dual-depth sparse probabilistic latent semantic analysis (DEpLSA), makes use of two different levels of topics to exploit the semantic patterns extracted from the initial spectral space in order to relieve the ill-posed nature of the unmixing problem. In other words, DEpLSA defines a first level of deep topics to capture the semantic representations of the spectra, and a second level of restricted topics to estimate endmembers and abundances over this semantic space. An experimental comparison in conducted using the two standard topic models and the seven state-of-the-art unmixing methods available in the literature. Our experiments, conducted using four different hyperspectral images, reveal that the proposed approach is able to provide competitive advantages over available unmixing approaches. [ABSTRACT FROM AUTHOR]

Compression of hyperspectral images onboard of spacecrafts is a tradeoff between the limited computational resources and the ever-growing spatial and spectral resolution of the optical instruments. As such, it requires low-complexity algorithms with good rate-distortion performance and high throughput. In recent years, the Consultative Committee for Space Data Systems (CCSDS) has focused on lossless and near-lossless compression approaches based on predictive coding, resulting in the recently published CCSDS 123.0-B-2 recommended standard. While the in-loop reconstruction of quantized prediction residuals provides excellent rate-distortion performance for the near-lossless operating mode, it significantly constrains the achievable throughput due to data dependencies. In this paper, we study the performance of a faster method based on the prequantization of the image followed by a lossless predictive compressor. While this is well known to be suboptimal, one can exploit powerful signal models to reconstruct the image at the ground segment, recovering part of the suboptimality. In particular, we show that convolutional neural networks can be used for this task and that they can recover the whole SNR drop incurred at a bit rate of 2 bits per pixel. [ABSTRACT FROM AUTHOR]

In this paper, we propose a spectral–spatial unified network (SSUN) with an end-to-end architecture for the hyperspectral image (HSI) classification. Different from traditional spectral–spatial classification frameworks where the spectral feature extraction (FE), spatial FE, and classifier training are separated, these processes are integrated into a unified network in our model. In this way, both FE and classifier training will share a uniform objective function and all the parameters in the network can be optimized at the same time. In the implementation of the SSUN, we propose a band grouping-based long short-term memory model and a multiscale convolutional neural network as the spectral and spatial feature extractors, respectively. In the experiments, three benchmark HSIs are utilized to evaluate the performance of the proposed method. The experimental results demonstrate that the SSUN can yield a competitive performance compared with existing methods. [ABSTRACT FROM AUTHOR]

Recently, multifeature learning in collaborative representation classification (CRC) for hyperspectral images has generated promising performance. In this paper, two novel multifeature learning algorithms that update dictionary directly and indirectly are proposed. In order to offer the complementarity of multifeature, four different types of features—global feature (i.e., Gabor feature), local feature (i.e., local binary pattern), shape feature (i.e., extended multiattribute profiles), and spectral feature—are adopted in this paper. Under the hypothesis that most of the features should share the same coding pattern in CRC, this paper proposes to learn proper dictionaries for each feature until obtaining stable codes in a linear classifier. Furthermore, to avoid the explicit mapping of infinite-dimensional dictionaries in a nonlinear kernelized classifier, an indirect approach to construct the transformation matrix from original dictionaries to learn new dictionaries is developed. Three real hyperspectral images acquired from different sensors are adopted for performance evaluation. The experimental results demonstrate that the proposed methods can provide superior performance compared with those of the state-of-the-art classifiers. [ABSTRACT FROM AUTHOR]

In order to provide reliable information about the instantaneous field of view considered in hyperspectral images through spectral unmixing, understanding the kind of mixture that occurs over each pixel plays a crucial role. In this paper, in order to detect nonlinear mixtures, a method for fast identification of linear mixtures is introduced. The proposed method does not need statistical information and performs an a priori test on the spectral linearity of each pixel. It uses standard least squares optimization to achieve estimates of the likelihood of occurrence of linear combinations of endmembers by taking advantage of the geometrical properties of hyperspectral signatures. Experimental results on both real and synthetic data sets show that the aforesaid algorithm is actually able to deliver a reliable and thorough assessment of the kind of mixtures present in the pixels of the scene. [ABSTRACT FROM PUBLISHER]

The classification accuracy of remote sensing data can be increased by integrating ancillary data provided by multisource acquisition of the same scene. We propose to merge the spectral and spatial content of hyperspectral images (HSIs) with elevation information from light detection and ranging (LiDAR) measurements. In this paper, we propose to fuse the data sets using orthogonal total variation component analysis (OTVCA). Extinction profiles are used to automatically extract spatial and elevation information from HSI and rasterized LiDAR features. The extracted spatial and elevation information is then fused with spectral information using the OTVCA-based feature fusion method to produce the final classification map. The extracted features have high dimension, and therefore OTVCA estimates the fused features in a lower dimensional space. OTVCA also promotes piece-wise smoothness while maintaining the spatial structures. Both attributes are important to provide homogeneous regions in the final classification maps. We benchmark the proposed approach (OTVCA-fusion) with an urban data set captured over an urban area in Houston/USA and a rural region acquired in Trento/Italy. In the experiments, OTVCA-fusion is evaluated using random forest and support vector machine classifiers. Our experiments demonstrate the ability of OTVCA-fusion to produce accurate classification maps while using fewer features compared with other approaches investigated in this paper. [ABSTRACT FROM PUBLISHER]

The AisaOWL is a recent-to-market thermal hyperspectral instrument. As such, there is little information about the sensor performance in the literature. The sensor covers the 7.6– 12.6~\mu \textm part of the long-wave infrared region with 102 continuous bands, and is capable of imaging in low-light conditions. This paper presents an independent characterization of the AisaOWL sensor, examining the spectral accuracy of black body measurements at different temperatures and validating manufacturer recommendations for warm-up, integration, and calibration times. This analysis is essential for establishing high quality operational procedures and in giving confidence to users of the data. In this paper, the sensor has been found to have a maximum error of 2 °C in absolute temperature measurement, and provides spectra most accurate in the 8– 9~\mu \textm region. The recommended warm-up time of 15 min has been confirmed, with a 1% increase in error identified for data collected only 7 min after switch on. The optimal integration time of 1.18 ms has been validated and an exponential decrease in performance observed outside the 0.85–1.2 ms range. The detector used by the sensor is shown to have stability issues and this has been examined by comparing black body data processed with different calibration data. While the detector is operating in a stable regime compatible with the calibration, these black body readings stay within 5% across the central bands, approaching 10% below 8~\mu \textm and just exceeding 20% above 11 \mu \textm . [ABSTRACT FROM AUTHOR]

Hyperspectral imaging (HSI) classification has become a popular research topic in recent years, and effective feature extraction is an important step before the classification task. Traditionally, spectral feature extraction techniques are applied to the HSI data cube directly. This paper presents a novel algorithm for HSI feature extraction by exploiting the curvelet-transformed domain via a relatively new spectral feature processing technique—singular spectrum analysis (SSA). Although the wavelet transform has been widely applied for HSI data analysis, the curvelet transform is employed in this paper since it is able to separate image geometric details and background noise effectively. Using the support vector machine classifier, experimental results have shown that features extracted by SSA on curvelet coefficients have better performance in terms of classification accuracy over features extracted on wavelet coefficients. Since the proposed approach mainly relies on SSA for feature extraction on the spectral dimension, it actually belongs to the spectral feature extraction category. Therefore, the proposed method has also been compared with some state-of-the-art spectral feature extraction techniques to show its efficacy. In addition, it has been proven that the proposed method is able to remove the undesirable artifacts introduced during the data acquisition process. By adding an extra spatial postprocessing step to the classified map achieved using the proposed approach, we have shown that the classification performance is comparable with several recent spectral–spatial classification methods. [ABSTRACT FROM PUBLISHER]

Nonlinear unmixing algorithms are playing a key role in modern earth observation analysis thanks to their ability to characterize complex phenomena occurring in the instantaneous field of view. When unmixing hyperspectral images according to nonlinear mixture models by means of state-of-the-art methods, actual abundances of the elements in the scene can be only indirectly estimated. Thus, the reliability of the investigation can be dramatically jeopardized, hence degrading the accuracy of the characterization of the surface composition. In order to overcome this issue, we propose in this paper a nonlinear programming scheme that aims at providing direct estimation of the end members fractions. The method we introduce is based on a structural optimization approach where the abundances are directly assessed, so that no epistemic uncertainties are injected in the framework. Experimental results show that the proposed method is able to deliver accurate and reliable estimates of these quantities in hyperspectral images. [ABSTRACT FROM AUTHOR]

Band selection is an effective approach to mitigate the “Hughes phenomenon” of hyperspectral image (HSI) classification. Recently, sparse representation (SR) theory has been successfully introduced to HSI band selection, and many SR-based methods have been developed and shown great potential and superiority. However, due to the inherent limitations of the SR scheme, i.e., individually representing each band with only a few other bands from the same subspace, the SR-based methods cannot effectively capture the global structures of the data, which limit the band selection performance. In this paper, to overcome this obstacle, the novel Laplacian-regularized low-rank subspace clustering (LLRSC) algorithm is proposed for HSI band selection. On the one hand, the low-rank subspace clustering model is introduced to capture the global structure information for the learned representation coefficient matrix and deal with the HSI band selection task in the clustering framework. On the other hand, considering the high correlation between adjacent bands, 1-D Laplacian regularization is utilized to incorporate the neighboring band information and further reduce the representation bias. Lastly, an eigenvalue analysis algorithm based on band mutation information is utilized to estimate the appropriate size of the band subset. The experimental results indicate that the proposed LLRSC algorithm outperforms the other state-of-the-art methods and achieves a very competitive band selection performance for HSIs. [ABSTRACT FROM AUTHOR]

Tensor-decomposition-based methods for reducing random noise components in hyperspectral images (HSIs), both dependent and independent from signal, are proposed. In this paper, noise is described by a parametric model that accounts for the dependence of noise variance on the signal. This model is thus suitable for the cases where photon noise is dominant compared with the electronic noise contribution. To denoise HSIs distorted by both signal-dependent (SD) and signal-independent (SI) noise, some hybrid methods, which reduce noise by two steps according to the different statistical properties of those two types of noise, are proposed in this paper. The first one, named as the PARAFACSI- PARAFACSD method, uses a multilinear algebra model, i.e., parallel factor analysis (PARAFAC) decomposition, twice to remove SI and SD noise, respectively. The second one is a combination of the well-known multiple-linear-regression-based approach termed as the HYperspectral Noise Estimation (HYNE) method and PARAFAC decomposition, which is named as the HYNE-PARAFAC method. The last one combines the multidimensional Wiener filter (MWF) method and PARAFAC decomposition and is named as the MWF-PARAFAC method. For HSIs distorted by both SD and SI noise, first, most of the SI noise is removed from the original image by PARAFAC decomposition, the HYNE method, or the MWF method based on the statistical property of SI noise; then, the residual SD components can be further reduced by PARAFAC decomposition due to its own statistical property. The performances of the proposed methods are assessed on simulated HSIs. The results on the real-world airborne HSI Hyperspectral Digital Imagery Collection Experiment (HYDICE) are also presented and analyzed. These experiments show that it is worth taking into account noise signal-dependence hypothesis for processing HYDICE data. [ABSTRACT FROM PUBLISHER]

This paper proposes an approach for the development of automatic object-based techniques used for hyperspectral image classification. The proposed approach employs an adaptive smoothing step that utilizes an extension of partial differential equations (PDEs) from real domain (RPDE) to complex domain (CPDE). This idea results in generalized PDEs that simultaneously have the properties of both forward and backward diffusions. The genetic algorithm and an innovative fitness function are applied for adaptively tuning the CPDE parameters. The smoothed data are then fed into a Pixon-based object extraction process, which is itself an adaptive process. We also propose a novel distance metric for the Pixon creation step in order to facilitate the use of textural information which exists in the data. The spectral features of the extracted objects are used for a support vector machine classifier. [ABSTRACT FROM AUTHOR]

Recent classification-oriented proposals to thematic maps building from hyperspectral images have used both semisupervised approaches and spatial information for correction of spectral classification. Semisupervised approaches enrich the training data set adding similar samples to each class, whereas spatial correction is based on the natural assumption of thematic class spatial compactness. In this paper, we propose and validate the following innovations: 1) a new spectral classifier, which is called anticipative hybrid extreme rotation forest (AHERF); 2) a spatial–spectral semisupervised approach; and 3) a final spatial classification correction step. The novel heterogeneous ensemble learning approach AHERF starts with a model selection phase, using a small subsample of the training data, in order to define a ranking-based selection probability distribution of the classifier architectures that will be used in the ensemble, so that the architecture best adapted to the data domain will be used more frequently to train individual classifiers in the ensemble. After this initial phase, AHERF trains a heterogeneous ensemble applying random rotations to bootstrapped samples of the remaining training data, aiming to obtain diversified and data-domain adapted individual classifiers. The natural assumption that spatially close pixels will most likely have highly correlated values is exploited in two phases of the process pipeline. First, semisupervised label assignment is supported by spectral similarity and spatial proximity. Unsupervised spectral similarity is detected by latent class discovery. In this paper, we use a clustering algorithm (i.e., k-means). Second, maximizing class spatial compactness removes classification errors that appear as speckle noise in the classification image. The whole approach aims to use minimal sets of labeled pixels for training, which we call the seed training data set. Testing results are computed over the entire image ground truth. For comparison, we provide results in several steps: 1) of classification by AHERF and competing classifiers built by semisupervised training and 2) after spatial correction. We validate the approach on several conventional benchmarking images, achieving results which are comparable with state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In hyperspectral unmixing, bilinear and linear–quadratic models have become popular recently, and also the polynomial postnonlinear model shows promising results. These models do not consider endmember interactions involving more than two endmembers, although such interactions might compose a nontrivial part of the observed spectrum in scenarios involving bright materials and complex geometrical structures, such as vegetation and intimate mixtures. In this paper, we present an extension of these models to include an infinite number of interactions. Several technical problems, such as divergence of the resulting series, can be avoided by introducing an optical interaction probability, which becomes the only free parameter of the model in addition to the abundances. We present an unmixing strategy based on this multilinear mixing (MLM) model; present comparisons with the bilinear models and the Hapke model for intimate mixing; and show that, in several scenarios, the MLM model obtains superior results. [ABSTRACT FROM AUTHOR]

In this paper, we consider signal subspace estimation based on low-rank representation for hyperspectral imagery. It is often assumed that major signal sources occupy a low-rank subspace. Due to the mixed nature of hyperspectral remote sensing data, the underlying data structure may include multiple subspaces instead of a single subspace. Therefore, in this paper, we propose the use of low-rank subspace representation to estimate the number of subspaces in hyperspectral imagery. In particular, we develop simple estimation approaches without user-defined parameters because these parameters can be fixed as constants. Both real data experiments and computer simulations demonstrate excellent performance of the proposed approaches over those currently in the literature. [ABSTRACT FROM PUBLISHER]

Image decolorization is known to be an effective way in transferring a color image into a gray one while well preserving the major information of all three bands. In this paper, a simple yet effective hyperspectral image visualization framework based on decolorization, named decolorization based hyperspectral visualization, is proposed, which enables us to fully exploit the benefits of decolorization technique. The proposed framework consists of the following two main steps. First, the hyperspectral image is partitioned into nine subsets of adjacent hyperspectral bands and the averaged band of each subset is calculated. Then, the dimension reduced image is further divided into three groups of adjacent bands, and the bands in each group are fused by using an image decolorization method. The main contribution of this paper is that the strong correlations in two different fields, i.e., image decolorization and hyperspectral image visualization, are first built. Experiments performed on several real hyperspectral data sets demonstrate that the proposed framework can obtain outstanding visualization performance in terms of both subjective and objective evaluations. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) noise reduction is an active research topic in HSI processing due to its significance in improving the performance for object detection and classification. In this paper, we propose a joint spectral and spatial low-rank (LR) regularized method for HSI denoising, based on the assumption that the free-noise component in an observed signal can exist in latent low-dimensional structure while the noise component does not have this property. The proposed HSI denoising method not only considers the traditional LR property across the spectral domain but also leverages nonlocal LR property over the spatial domain. The main contribution of this paper is the incorporation of the low-rankness-based nonlocal similarity into sparse representation to characterize the spatial structure. Specially, the similar patches in each cluster usually contain similar sharp structure such as edges and textures; LR performed on cluster entitles to achieve a lower rank than that on the global spectral correlation. To make the proposed method more tractable and robust, we develop a variable splitting-based technique to solve the optimization problem. Experiment results on both simulated and real hyperspectral data sets demonstrate that the proposed method outperforms state-of-the-art methods with significant improvements both visually and quantitatively. [ABSTRACT FROM AUTHOR]

Hyperspectral imagery (HSI) exploitation typically requires spectral signatures for target detection and identification algorithms. As the longwave infrared (LWIR) region of the electromagnetic spectrum is dominated by thermal emission, spectral radiance measurements are influenced by object temperature, and thus, estimates of target temperature may be necessary for emissivity retrieval to support these algorithms. Therefore, lack of accurate temperature information poses a significant challenge for HSI target detection and identification. Previous studies have demonstrated LWIR hyperspectral unmixing in both radiance and emissivity domains using in-scene target signatures. Here, a radiance-domain LWIR material identification algorithm for subpixel target identification of solid materials is developed by combining spectral radiance and linear mixing models with Bayesian model averaging. Application to experimental LWIR HSI illustrates that the algorithm effectively distinguishes between solid materials with a high degree of spectral similarity and reduces the probability of false alarms by at least one order of magnitude over a standard adaptive coherence estimator detector. Limits of identification are inferred from the imagery and found to depend on material type, target size, and target geometry. For the sensor and materials in this paper, the results imply that targets of nominally 5 m2 in size with strong spectral features can be identified for ground sampling distances (GSDs) on the order of 5-10 m (with abundances as low as ~10%) whereas blackbody-like materials are difficult to distinguish for GSDs larger than approximately 3 m. [ABSTRACT FROM PUBLISHER]

Noise contamination is a ubiquitous problem in hyperspectral images (HSIs), which is a challenging and promising theme in many remote sensing applications. A large number of methods have been proposed to remove noise. Unfortunately, most denoising methods fail to take full advantages of the high spectral correlation and to simultaneously consider the specific noise distributions in HSIs. Recently, a spectral–spatial adaptive hyperspectral total variation (SSAHTV) was proposed and obtained promising results. However, the SSAHTV model is insensitive to the image details, which makes the edges blur. To overcome all of these drawbacks, a spectral–spatial kernel method for HSI denoising is proposed in this paper. The proposed method is inspired by the observation that the spectral–spatial information is highly redundant in HSIs, which is sufficient to estimate the clear images. In this paper, a spectral–spatial kernel regularization is proposed to maintain the spectral correlations in spectral dimension and to match the original structure between two spatial dimensions. Moreover, an adaptive mechanism is developed to balance the fidelity term according to different noise distributions in each band. Therefore, it cannot only suppress noise in the high-noise band but also preserve information in the low-noise band. The reliability of the proposed method in removing noise is experimentally proved on both simulated data and real data. [ABSTRACT FROM PUBLISHER]

Hyperspectral imaging relies on sophisticated acquisition and data processing systems able to acquire, process, store, and transmit hundreds or thousands of image bands from a given area of interest. In this paper, we exploit the high correlation existing among the components of the hyperspectral data sets to introduce a new compressive sensing methodology, termed hyperspectral coded aperture (HYCA), which largely reduces the number of measurements necessary to correctly reconstruct the original data. HYCA relies on two central properties of most hyperspectral images, usually termed data cubes: 1) the spectral vectors live on a low-dimensional subspace; and 2) the spectral bands present high correlation in both the spatial and the spectral domain. The former property allows to represent the data vectors using a small number of coordinates. In this paper, we particularly exploit the high spatial correlationmentioned in the latter property, which implies that each coordinate is piecewise smooth and thus compressible using local differences. The measurement matrix computes a small number of random projections for every spectral vector, which is connected with coded aperture schemes. The reconstruction of the data cube is obtained by solving a convex optimization problem containing a data term linked to the measurement matrix and a total variation regularizer. The solution of this optimization problem is obtained by an instance of the alternating direction method of multipliers that decomposes very hard problems into a cyclic sequence of simpler problems. In order to address the need to set up the parameters involved in the HYCA algorithm, we also develop a constrained version of HYCA (C-HYCA), in which all the parameters can be automatically estimated, which is an important aspect for practical application of the algorithm. A series of experiments with simulated and real data shows the effectiveness of HYCA and C-HYCA, indicating their potential in real-world applications. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the structure and performance of the maximum entropy temperature-emissivity separation (MaxEnTES) algorithm for assessing land temperature and emissivity from thermal infrared hyperspectral images. This procedure derives the emissivity spectrum and the temperature of the target adopting the maximum entropy (MaxEnt) estimation approach. The main advantage of the MaxEnt statistical inference is the absence of any external hypothesis, which is, instead, the main critical point characterizing any other temperature-emissivity separation (TES) algorithm. The MaxEnTES algorithm carries out the TES task adopting a modified version of the subgradient Shor's r-algorithm adopted for numerical optimization of a MaxEnt objective function. For this purpose, we have utilized the C/C++ Solvopt code from the University of Gratz to develop a practical data processing implementation. In this paper, we discuss the mathematical structure of the MaxEnTES algorithm and analyze its performance in depth using numerical simulations and remote sensing Multispectral Infrared/Visible Imaging Spectrometer images. We show that the MaxEnTES algorithm provides improved accuracy for temperature and emissivity estimation, lowering the standard estimation error to a fraction of degree Kelvin. In agreement with previous investigations, we find that the estimation accuracy grows when increasing the number of available spectral channels. The systematic errors affecting the temperature estimates (e.g., bias) are thoroughly evaluated. We prove that the MaxEnTES algorithm retrieves the correct shape of the target emissivity spectrum even in presence of a significant temperature estimation error. [ABSTRACT FROM PUBLISHER]

This paper presents a new spectral–spatial (SS) approach to hyperspectral image classification (HSIC), called SS feedback close network system (SSFCNS), which has not been explored in the past. Unlike commonly used SS-based methods SSFCNS includes a feedback close network system (FCNS) to obtain spatial information via a selective spatial filter in an iterative manner. More specifically, SSFCNS takes advantage of FCNS which utilizes a particularly selected spatial filter to capture a posteriori spatial information directly from spectral-classified data samples and then feeds back such obtained spatial-filtered image to be combined with the current image cube to create a new image cube that can be used as a new input to re-implement SSFCNS. The process is carried out in such a way that the spatial information obtained from spectral classification results is updated by FCNS iteratively and terminated by a Tanimoto index (TI)-derived automatic stopping rule. To evaluate the performance of SSFCNS several spatial filters (i.e., Gaussian, bilateral, guided, and Gabor filters) are explored for real image experiments. The experimental results demonstrate that SSFCNS performs significantly better in classification accuracy compared to SS-based methods which do not use FCNS. [ABSTRACT FROM AUTHOR]

For hyperspectral image classification, there is a large gap between the theoretical method and the practical application. Hyperspectral image classification in theoretical research trains a new classifier for each data set, which is ineffective and even infeasible in large-scale applications. In this paper, we make a preliminary attempt to recycle the classification model to new data sets in an unsupervised way. Specially, we propose a cross-data set hyperspectral image classification method based on deep domain adaptation. The proposed method contains three modules: domain alignment module that learns to minimize the domain discrepancy with the guide of an irrelevant task, task allocation module that learns to classify on the source domain with the regulation of domain alignment, and domain adaptation module that transfers both the alignment ability and classification ability to the target domain by an adaptation strategy. As a result, with the information of an irrelevant task on dual-domain data sets, we can minimize the domain discrepancy and transfer the task-relevant knowledge from the source domain to the target domain in an unsupervised way. Extensive experiments on three hyperspectral images demonstrate the effectiveness of our method compared with other related methods when dealing with new data sets. [ABSTRACT FROM AUTHOR]

In recent years, anomaly detection (AD) has enjoyed a growing interest in hyperspectral data analysis. However, most state-of-the-art detectors need to work with the entire hyperspectral cube, what prevents their use for applications under real-time constraints, especially when the hyperspectral data are collected by push-broom scanners that acquire the hyperspectral images (HSIs) in a line-by-line fashion. In this paper, a Line-by-Line Fast Anomaly Detector for Hyperspectral Imagery (LbL-FAD) is proposed, which is capable of processing each sensed line as soon as it is captured. The LbL-FAD works under the assumption that anomalous pixels cannot be well represented by the background distribution. It uses an orthogonal projection strategy for extracting a set of pixels from the first captured hyperspectral frames, i.e., lines of pixels, that are used for representing the background distribution. Using these pixels, the LbL-FAD proposes a hardware-friendly alternative to compute the orthogonal subspace to that spanned by the selected background samples, making the anomalous pixels better detectable. In addition, the LbL-FAD incorporates an automatic thresholding method which provides line-by-line and real-time binary maps where anomalous targets are segmented from the background. This novelty clearly differentiates the proposed LbL-FAD from the conventional anomaly detectors, which usually are not able to automatically discriminate anomalous pixels from background pixels until the entire image is processed. Several experiments have been carried out using real HSIs collected by different sensors. The obtained results clearly support the benefits of our proposal, both in terms of the accuracy of the detection performance and the computational complexity. [ABSTRACT FROM AUTHOR]

In recent years, sparse representations have been widely studied in the context of remote sensing image analysis. In this paper, we propose to exploit sparse representations of morphological attribute profiles for remotely sensed image classification. Specifically, we use extended multiattribute profiles (EMAPs) to integrate the spatial and spectral information contained in the data. EMAPs provide a multilevel characterization of an image created by the sequential application of morphological attribute filters that can be used to model different kinds of structural information. Although the EMAPs' feature vectors may have high dimensionality, they lie in class-dependent low-dimensional subpaces or submanifolds. In this paper, we use the sparse representation classification framework to exploit this characteristic of the EMAPs. In short, by gathering representative samples of the low-dimensional class-dependent structures, any given sample may by sparsely represented, and thus classified, with respect to the gathered samples. Our experiments reveal that the proposed approach exploits the inherent low-dimensional structure of the EMAPs to provide state-of-the-art classification results for different multi/hyperspectral data sets. [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]

Hyperspectral remote sensing images contain hundreds of data channels. Due to the high dimensionality of the hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for such imagery. In this paper, a new multilevel thresholding method is introduced for the segmentation of hyperspectral and multispectral images. The new method is based on fractional-order Darwinian particle swarm optimization (FODPSO) which exploits the many swarms of test solutions that may exist at any time. In addition, the concept of fractional derivative is used to control the convergence rate of particles. In this paper, the so-called Otsu problem is solved for each channel of the multispectral and hyperspectral data. Therefore, the problem of n-level thresholding is reduced to an optimization problem in order to search for the thresholds that maximize the between-class variance. Experimental results are favorable for the FODPSO when compared to other bioinspired methods for multilevel segmentation of multispectral and hyperspectral images. The FODPSO presents a statistically significant improvement in terms of both CPU time and fitness value, i.e., the approach is able to find the optimal set of thresholds with a larger between-class variance in less computational time than the other approaches. In addition, a new classification approach based on support vector machine (SVM) and FODPSO is introduced in this paper. Results confirm that the new segmentation method is able to improve upon results obtained with the standard SVM in terms of classification accuracies. [ABSTRACT FROM AUTHOR]

This paper presents an interactive feedback scheme of spatial resolution enhancement and spectral unmixing in hyperspectral imaging. Traditionally spatial resolution enhancement and spectral unmixing operations have been carried out separately, often in series. In such sequential processing, spatially enhanced hyperspectral images (HSIs) may introduce distortion in spectral fidelity making spectral unmixing results unreliable, or vice versa. Since both high- and low-resolution HSIs have the same endmembers, the deviation in spectral unmixing between targets and estimated high-resolution HSIs can be used as feedback to control spatial resolution enhancement. The spatial difference before and after unmixing can also be used as feedback to enhance spectral unmixing. Therefore, spectral unmixing is utilized as a constraint to spatial resolution enhancement, while spatial resolution enhancement helps improve spectral unmixing results. The performance of spatial resolution enhancement and spectral unmixing can be improved since one behaves like a prior to the other. Experimental results on both simulated and real HSI data sets demonstrate that the proposed interactive feedback scheme simultaneously achieved spatial resolution enhancement and spectral unmixing fidelity. This paper is an extended version of the previous work. [ABSTRACT FROM PUBLISHER]

In this paper, we study self-taught learning for hyperspectral image (HSI) classification. Supervised deep learning methods are currently state of the art for many machine learning problems, but these methods require large quantities of labeled data to be effective. Unfortunately, existing labeled HSI benchmarks are too small to directly train a deep supervised network. Alternatively, we used self-taught learning, which is an unsupervised method to learn feature extracting frameworks from unlabeled hyperspectral imagery. These models learn how to extract generalizable features by training on sufficiently large quantities of unlabeled data that are distinct from the target data set. Once trained, these models can extract features from smaller labeled target data sets. We studied two self-taught learning frameworks for HSI classification. The first is a shallow approach that uses independent component analysis and the second is a three-layer stacked convolutional autoencoder. Our models are applied to the Indian Pines, Salinas Valley, and Pavia University data sets, which were captured by two separate sensors at different altitudes. Despite large variation in scene type, our algorithms achieve state-of-the-art results across all the three data sets. [ABSTRACT FROM PUBLISHER]

Soil salinization is one of the most common land desertification processes that can be found worldwide. It is a certainly severe environment hazard and threatens the stability of ecosystems. As a rapid and inexpensive tool, remote sensing technology combining with the measurements of soil spectra has been widely concerned on identifying and mapping salt effect on lands. However, as effects of the soil moisture often immerge the effects of salt to soil reflectance spectra, soil moisture became a major factor to restrict soil salinity monitoring from soil reflectance. High soil moisture content will lead to failure on soil salinity estimation from soil reflectance data. In this paper, a semianalytical model using an exponent function was developed to estimate soil salt content (SSC) under different moisture levels based on a control laboratory experiment. And the root-mean-square error and mean relative error were 0.799 g/kg and 31.294%, respectively, when the model was applied to estimate SSCs by wet soil reflectance. To sum up, considering both effects of soil moisture and soil salt on soil reflectance, the semianalytical model reduced SSC estimated error. The approach presented in this paper provides a new way of estimating soil salinity from soil spectra under various soil moisture conditions, and it will be a potential application for large-scale SSC mapping. [ABSTRACT FROM PUBLISHER]

A big challenge of hyperspectral image (HSI) classification is the small size of labeled pixels for training classifier. In real remote sensing applications, we always face the situation that an HSI scene is not labeled at all, or is with very limited number of labeled pixels, but we have sufficient labeled pixels in another HSI scene with the similar land cover classes. In this paper, we try to classify an HSI scene containing no labeled sample or only a few labeled samples with the help of a similar HSI scene having a relative large size of labeled samples. The former scene is defined as the target scene, while the latter one is the source scene. We name this classification problem as cross-scene classification. The main challenge of cross-scene classification is spectral shift, i.e., even for the same class in different scenes, their spectral distributions maybe have significant deviation. As all or most training samples are drawn from the source scene, while the prediction is performed in the target scene, the difference in spectral distribution would greatly deteriorate the classification performance. To solve this problem, we propose a dictionary learning-based feature-level domain adaptation technique, which aligns the spectral distributions between source and target scenes by projecting their spectral features into a shared low-dimensional embedding space by multitask dictionary learning. The basis atoms in the learned dictionary represent the common spectral components, which span a cross-scene feature space to minimize the effect of spectral shift. After the HSIs of two scenes are transformed into the shared space, any traditional HSI classification approach can be used. In this paper, sparse logistic regression (SRL) is selected as the classifier. Especially, if there are a few labeled pixels in the target domain, multitask SRL is used to further promote the classification performance. The experimental results on synthetic and real HSIs show the advantages of the proposed method for cross-scene classification. [ABSTRACT FROM AUTHOR]

This paper proposes a spectral-spatial classification algorithm based on principal components (PCs)-based smooth ordering and multiple 1-D interpolation, which can alleviate the general classification problems effectively. Because of the characteristics of hyperspectral image, there always exist easily separable samples (ESSs) and difficultly separable samples (DSSs) in view of the different sets of labeled samples. In this paper, the PC analysis is first used for reducing features and extracting the few first PCs of a hyperspectral image. Then, PC-based smooth ordering is designed for the separation of ESSs and DSSs, and multiple 1-D interpolation is used for the accurate classification of the ESSs. Next, the highly confident samples are selected from the ESSs by the spatial neighborhood information, which are added into the training set for the classification of DSSs. In the case of sufficient training samples, a supervised spectral-spatial method is used for classifying the DSSs by combining the spatial information built with popular extended multiattribute profiles. The proposed algorithm is compared with some state-of-the-art methods on three hyperspectral data sets. The results demonstrate that the presented algorithm achieves much better classification performance in terms of the accuracy and the computation time. [ABSTRACT FROM AUTHOR]

Classical target detection schemes are usually obtained by deriving the likelihood ratio under Gaussian hypothesis and replacing the unknown background parameters by their estimates. In most applications, interference signals are assumed to be Gaussian with zero mean [or with a known mean vector (MV)] and with an unknown covariance matrix (CM). When the MV is unknown, it has to be jointly estimated with the CM. In this paper, adaptive versions of the classical matched filter (MF) and the normalized MF, as well as two versions of the Kelly detector are first derived and then analyzed for the case where the MV of the background is unknown. More precisely, theoretical closed-form expressions for false alarm (FA) regulation are derived and the constant FA rate property is pursued to allow the detector to be independent of nuisance parameters. Finally, the theoretical contributions are validated through simulations. [ABSTRACT FROM AUTHOR]

The Reed–Xiaoli (RX) algorithm has been widely used as an anomaly detector for hyperspectral images. Recently, kernel RX (KRX) has been proven to yield high performance in anomaly detection and change detection. In this paper, we present a generalization of the KRX algorithm. The novel algorithm is called cluster KRX (CKRX), which becomes KRX under certain conditions. The key idea is to group background pixels into clusters and then apply a fast eigendecomposition algorithm to generate the anomaly detection index. Both global and local versions of CKRX have been implemented. Application to anomaly detection using actual hyperspectral images is included. In addition to anomaly detection, the CKRX algorithm has been integrated with other prediction algorithms for change detection. Spatially registered visible and near-infrared hyperspectral images collected from a tower-based geometry have been used in the anomaly and change detection studies. Receiver operating characteristics curves and actual computation times were used to compare different algorithms. It was demonstrated that CKRX has comparable detection performance as KRX, but with much lower computational requirements. [ABSTRACT FROM PUBLISHER]

This paper proposes a new semisupervised dimension reduction (DR) algorithm based on a discriminative locally enhanced alignment technique. The proposed DR method has two aims: to maximize the distance between different classes according to the separability of pairwise samples and, at the same time, to preserve the intrinsic geometric structure of the data by the use of both labeled and unlabeled samples. Furthermore, two key problems determining the performance of semisupervised methods are discussed in this paper. The first problem is the proper selection of the unlabeled sample set; the second problem is the accurate measurement of the similarity between samples. In this paper, multilevel segmentation results are employed to solve these problems. Experiments with extensive hyperspectral image data sets showed that the proposed algorithm is notably superior to other state-of-the-art dimensionality reduction methods for hyperspectral image classification. [ABSTRACT FROM AUTHOR]

This paper proposes to use empirical mode decomposition (EMD) with spectral gradient enhancement to increase the classification accuracy of hyperspectral images with support vector machine (SVM) classification. Recently, it has been shown that higher hyperspectral image classification accuracy can be achieved by using 2-D EMD that is applied to each hyperspectral band separately to obtain the intrinsic mode functions (IMFs) of each band, while the sum of the IMFs are used as feature data in the SVM classification process. In the previous approach, IMFs have been summed directly, i.e., with equal weights. It is shown in this paper, that it is possible to significantly increase the classification accuracy by using appropriate weights for the IMFs in the summation process. In the proposed approach, the weights of the IMFs are obtained so as to optimize the total absolute spectral gradient, and a genetic algorithm-based optimization strategy has been adopted to obtain the weights automatically in this way. While the 2-D EMD basically provides spatial processing, the proposed method further incorporates spectral enhancement into the process. It is shown that a significant increase in hyperspectral image classification accuracy can be achieved using the proposed approach. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a new framework for spectral–spatial classification of hyperspectral image data. The proposed approach serves as an engine in the context of which active learning algorithms can exploit both spatial and spectral information simultaneously. An important contribution of our paper is the fact that we exploit the marginal probability distribution which uses the whole information in the hyperspectral data. We learn such distributions from both the spectral and spatial information contained in the original hyperspectral data using loopy belief propagation. The adopted probabilistic model is a discriminative random field in which the association potential is a multinomial logistic regression classifier and the interaction potential is a Markov random field multilevel logistic prior. Our experimental results with hyperspectral data sets collected using the National Aeronautics and Space Administration's Airborne Visible Infrared Imaging Spectrometer and the Reflective Optics System Imaging Spectrometer system indicate that the proposed framework provides state-of-the-art performance when compared to other similar developments. [ABSTRACT FROM PUBLISHER]

Selection of threshold values to generate nonredundant filtered images in attribute profiles (APs) is an unresolved issue. This paper presents a novel filtering approach to the construction of APs that does not require the definition of any threshold value. The proposed approach creates a max-tree (or min-tree), traverse to the first encountered leaf node using depth first traversal, and defines a leaf attribute function (LAF) to demonstrate the changes in attribute values from leaf to root node. The LAF is analyzed based on a novel criterion to automatically detect the node along the path that has a first significant difference in the attribute value. All its descendant nodes are merged to it and the process is repeated for each unvisited leaf node to create the final filtered tree which is transformed back as a filtered image. The proposed approach can incorporate maximum spatial information by applying a few filtering operations without the need to define any threshold value. This is of great importance in spectral–spatial classification applications. Moreover, since the proposed approach requires one depth first traversal to generate a filtered image, it is very efficient in terms of computation time. To show the effectiveness of the proposed method, three real hyperspectral data sets are considered and the results are compared to the state-of-the-art method considering five different attributes. The results show that the proposed method has several important advantages with respect to the existing threshold-based filtering techniques. Furthermore, the proposed method is also effective when compared with different spectral–spatial classification techniques. [ABSTRACT FROM AUTHOR]

Although hyperspectral technology has continued to improve over the years, it is still limited to size, weight, and power (SWaP) constraints. One major issue is the need to sample a large number of very fine spectral bands. Such prohibitively large size of hyperpsectral data creates challenges in both data archival and processing. Compressive sensing (CS) is an enabling technology for reducing the overall data processing and SWaP requirements. This paper explores the viability of performing classification for hyperspectral data on a compressively sensed band domain (CSBD) via CS instead of the original data space, without performing sparse reconstruction. In particular, the well-known restricted isometry property (RIP) and a random spectral sampling strategy are explored for hyperspectral image classification (HSIC) in CSBD. A mathematical analysis is also presented to show that the classification error can be expressed in terms of the restricted isometry constant (RIC) so that the HSIC in the original full-band data space can be achieved in CSBD provided that sufficient band-sensing conditions are met. To validate the proposed CS-HSIC a set of real hyperspectral image experiments are performed where a commonly used spectral–spatial classification algorithm based on support vector machine (SVM) and edge-preserving filters (EPFs) is implemented for a comparative study and analysis. The results clearly demonstrate the potential of CS-HSIC in future research directions. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) classification (HIC) has attracted much attention in the last decade. Spectral–spatial HIC methods have been the state-of-the-art methods in recent years. Small labeled training sample size (SLTSS) problem is still an important issue in HIC. This paper presents a spectral–spatial HIC method that is based on superpixel (SP) segmentation and distance-weighted linear regression classifier to tackle the SLTSS problem. First, SP segmentation is applied to the original HSI. Then, those SPs that contain training samples belonging to only one class are first searched out, and all the pixels of each of these SPs are assigned to the class of the training samples it contains. Next, with the identified labels, all these classified pixels are added to the initial training sample set for training sample set enlargement. Later, using this enlarged training sample set, the distance-weighted linear regression classifier is applied to classify each mean vector of each SP. Finally, the last classification map is obtained by assigning each SP with the same label as its mean vector. Experimental results on three HSI data sets demonstrate that the proposed approach can solve SLTSS problem very well and outperforms several state-of-the-art algorithms in classification accuracy under different training samples sizes. [ABSTRACT FROM AUTHOR]

In this paper, we consider the parameter estimation of $K$ -Gaussians, given convex combinations of their realizations. In the remote sensing literature, this setting is known as the normal compositional model (NCM) and has shown promising gains in modeling hyperspectral images. Current NCM parameter estimation techniques are based on Bayesian methodology and are computationally slow and sensitive to their prior assumptions. Here, we introduce a deterministic variant of the NCM, named DNCM, which assumes that the unknown mixing coefficients are nonrandom. This leads to a standard Gaussian model with a simple estimation procedure, which we denote by $K$ -Gaussians. Its iterations are provided in closed form and do not require any sampling schemes or simplifying structural assumptions. We illustrate the performance advantages of $K$ -Gaussians using synthetic and real images, in terms of accuracy and computational costs in comparison to state of the art. We also demonstrate the use of our algorithm in hyperspectral target detection on a real image with known targets. [ABSTRACT FROM AUTHOR]

In this paper, we propose a spatial–spectral feature fusion model with a predictive feature weighting mechanism and demonstrate its applications to the problems of hyperspectral image classification and segmentation. To address these problems, we learn a set of 1-D convolutional local spectral filters and 2-D spatial–spectral filters that feed features into a fusion module, in an end-to-end fashion. We propose a lightweight predictive feature weighting component embedded in the fusion model and consider four design fusion options, i.e., by adding or concatenating features with equal or predicted weights. For the pixel classification task, the training input consists of image patches with labeled central pixels, whereas for the spatial segmentation task, it includes the label maps of image regions. The proposed networks have been evaluated on the Indian Pines, Pavia University, and Houston University for the classification problem and the SpaceNet data set for the spatial segmentation problem. The quantitative results favor the proposed approach over the state-of-the-art methods across all the four data sets. [ABSTRACT FROM AUTHOR]

This paper focuses on the blind unmixing technique for analyzing hyperspectral images (HSIs). A joint deconvolution and blind hyperspectral unmixing (DBHU) algorithm is proposed, which is aimed at eliminating the impact of the adjacency effect (AE) on unmixing. In remote sensing imagery, the AE occurs in the presence of atmospheric scattering over a heterogeneous surface. The AE leads to blurring and additional mixing of HSIs and makes it difficult to estimate endmembers and abundances accurately. In this paper, we first model the blurred HSIs by the use of a bilinear mixing model (BMM), where a blurring kernel is used to model the mixing caused by the AE. Based on the BMM, the DBHU problem is formulated as a constrained and biconvex optimization problem. Specifically, the minimum-volume simplex (MVS) is incorporated to deal with the additional mixing caused by the AE, and 3-D total variation (TV) priors are adopted to model the spectral–spatial correlation of the data. In DBHU, the biconvex problem is efficiently solved by a nonstandard application of the alternating direction method of multipliers (ADMM) algorithm, where a block coordinate descent scheme is applied by splitting the original problem into two saddle point subproblems, and then minimizing the subproblems alternately via the ADMM until convergence. The experimental results obtained with both simulated and real data confirm the viability of the proposed algorithm, and DBHU works well, even where both blurring and noise are present in the scene. [ABSTRACT FROM AUTHOR]

Land-cover classification with hyperspectral imagery has been an active topic in the remote sensing community. It aims at relating a unique class label to each pixel in the scene, so that it can be well defined by a given land cover type. In this paper, we explore the intrinsic characteristics of hyperspectral imagery from a subpixel-level perspective and propose a new subpixel component analysis (SCA) approach for feature extraction and land-cover classification. The core idea of SCA is that we extract a subpixel attribute component feature from the abundance maps. Compared with the abundance maps, the extracted subpixel feature image shows higher signal-to-noise level and clearer spatial distribution details. In order to deal with spectral variability, as well as obtain representative image endmember signatures and their corresponding abundance maps, we adopt a regional clustering-based spatial preprocessing (RCSPP) strategy for endmember identification, and a partial unmixing model based on mixture tuned matched filtering (MTMF) for abundance estimation. Furthermore, to highlight the spatial distribution details as well as eliminate the noise disturbance in the derived abundance maps, we perform sparse image decomposition on the obtained abundance maps, thus achieving a new subpixel feature representation for classification. Our experimental results reveal that the proposed SCA approach can obtain feature representation with explicit physical meaning, clear spatial distribution details, and better noise robustness, leading to state-of-the-art classification results. [ABSTRACT FROM AUTHOR]

Endmember extraction (EE) plays an important role in the quantitative analysis of hyperspectral images, as the main step in the decomposition of mixed pixels. At present, scholars have proposed many EE algorithms based on the linear spectral mixture model and the convex geometry principle, such as the pixel purity index (PPI) and the vertex component analysis (VCA). At the same time, many intelligent optimization algorithms, such as the particle swarm optimization (PSO) and the discrete PSO (DPSO), have been applied to EE, which can get promising results for real images. However, PSO and DPSO have theoretical limitations and cannot guarantee the global convergence. The problem of premature convergence will reduce the accuracy of the EE result. The quantum-behaved PSO (QPSO) can theoretically guarantee the convergence of the algorithm by combining the quantum mechanics into the PSO. In order to increase the accuracy of the algorithm, this paper proposes an improved QPSO (IQPSO) algorithm for EE. IQPSO has made innovations in population coding and initialization methods. Besides, the collaborative approach for updating the optimal positions of particles can help to solve the difficulties caused by high dimensions. The experimental results show that IQPSO can extract endmembers efficiently and effectively. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSIs) can describe the subtle differences in the spectral signatures of materials. However, they have low spatial resolution due to various hardware limitations. Improving it via postprocess without an auxiliary high-resolution (HR) image still remains a challenging problem. In this paper, we address this problem and propose a new HSI super-resolution (SR) method. Our approach, called deep feature matrix factorization (DFMF), blends feature matrix extracted by a deep neural network (DNN) with nonnegative matrix factorization strategy for super-resolving real-scene HSI. The estimation of the HR HSI is formulated as a combination of latent spatial feature matrix and spectral feature matrix. In the DFMF model, the input low-resolution (LR) HSI is first partitioned into several subsets according to the correlation matrix, and the key band is selected from each subset. Then, the key band group is super-resolved by a DNN model, and the HR key band group is then used as a guide to carry out deep spatial feature matrix. Specifically, the input LR HSI with prototype reflectance spectral vectors of the scene will be preserved when super-resolving in a spatial domain. Thus, the nonnegative spectral and spatial feature matrices are extracted simultaneously from alternately factorizing the pair of LR HSI and the HR key band group. Finally, the HR HSI is obtained by the integration of the spectral and spatial feature matrices. Experiments have been conducted on real-scene remote sensing HSI. Comparative analyses validate that the proposed DFMF method presents a superior super-resolving performance, as it preserves spectral information better. [ABSTRACT FROM AUTHOR]

Spectral unmixing is one of the most important techniques for analyzing hyperspectral images. Many of the hyperspectral unmixing algorithms developed in recent years have been developed under an assumption that pure pixels exist, and some algorithms, such as N-finder algorithm (N-FINDR) and vertex component analysis (VCA), can only search data points in this case. However, the pure-pixel assumption may be seriously violated for highly mixed data. Whether a pure pixel exists or not, the endmember extraction can be regarded as an optimization problem. In this paper, we incorporate the linear mixture model (LMM) and particle swarm optimization (PSO) to develop LMM constrained PSO (LMMC-PSO) for endmember extraction from highly mixed data. The main contribution of the proposed method is that we redefine the particle motion rules. Each particle in LMMC-PSO moves in the search space according to the LMM, rather than with a velocity. The LMM is one kind of relationship between endmembers and mixed pixels, which can help guide efforts to build a path from mixed pixels to endmembers in PSO. The proposed algorithm was tested and evaluated with both synthetic and real hyperspectral data sets. The experimental results indicated that the proposed method obtains better results with highly mixed data than the algorithms of VCA, minimum volume constrained nonnegative matrix factorization, minimum volume simplex analysis (MVSA), robust MVSA, the convex analysis-based minimum volume enclosing simplex, and simplex identification via variable splitting and augmented Lagrangian. [ABSTRACT FROM AUTHOR]