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Image classification is considered to be one of the critical tasks in hyperspectral remote sensing image processing. Recently, a convolutional neural network (CNN) has established itself as a powerful model in classification by demonstrating excellent performances. The use of a graphical model such as a conditional random field (CRF) contributes further in capturing contextual information and thus improving the classification performance. In this paper, we propose a method to classify hyperspectral images by considering both spectral and spatial information via a combined framework consisting of CNN and CRF. We use multiple spectral band groups to learn deep features using CNN, and then formulate deep CRF with CNN-based unary and pairwise potential functions to effectively extract the semantic correlations between patches consisting of 3-D data cubes. Furthermore, we introduce a deep deconvolution network that improves the final classification performance. We also introduced a new data set and experimented our proposed method on it along with several widely adopted benchmark data sets to evaluate the effectiveness of our method. By comparing our results with those from several state-of-the-art models, we show the promising potential of our method. [ABSTRACT FROM AUTHOR]

Jointly combining the spatial and spectral features has proved to dramatically improve the performance of classifying hyperspectral image. Recently, utilizing neural networks to automatically model the spatial-spectral feature representations for hyperspectral images has become of great interest. This paper proposes a simple but innovative framework to classify hyperspectral image with two shallow convolutional neural networks (CNNs). First, principal component analysis whitening is applied to decorrelate hundreds of spectral bands. Instead of selecting the principal components to reduce the spectral dimensionality, we retain all the spectral bands but compress the image cuboid into a one-channel spectral quilt by stacking spectral patches. In this way, not only all the spectral information is retained but also the computational complexity of training a neural network is reduced compared with conventional networks that directly input the spectral volumes. Moreover, the spectral quilt will contain some novel textural patterns that are effective at distinguishing classes. Two shallow CNNs are then applied to classify the spectral quilts. As shown in the experiments, both networks can outperform the standard analysis methods. [ABSTRACT FROM AUTHOR]

Multiple types of features, e.g., spectral, filtering, texture, and shape features, are helpful for hyperspectral image (HSI) classification tasks. Combining multiple features can describe the characteristics of pixels from different perspectives, and always results in better classification performance. Recently, multifeature combination learning has been widely employed to the multitask-learning-based representation-based model to obtain a multifeature representation vector. However, the linear sparse representation-based classifier (SRC) cannot handle the HSI with highly nonlinear distribution, and kernel sparse representation-based classifier (KSRC) can remedy the drawback of linear SRC. By adopting nonlinear mapping, the samples in kernel space are often of high or even infinite dimensionality. In this paper, we integrate kernel principal component analysis into multifeature-based KSRC and propose a novel multiple feature kernel sparse representation-based classifier (namely, MFKSRC) for hyperspectral imagery. More specifically, spatial features, Gabor textures, local binary patterns, and difference morphological profiles are adopted and then each kind of feature is transformed nonlinearly into a new low-dimensional kernel space. The proposed framework can handle data with nonlinear distribution and add a dimensionality reduction stage in kernel space before optimizing the corresponding cost function. Experimental results on different HSIs demonstrate that the proposed MFKSRC algorithm outperforms the state-of-the-art classifiers. [ABSTRACT FROM AUTHOR]

Combining spatial information with spectral information for classifying hyperspectral images can dramatically improve the performance. This paper proposes a simple but innovative framework to automatically generate spatial–spectral features for hyperspectral image classification. Two unsupervised learning methods— $K$ -means and principal component analysis—are utilized to learn the spatial feature bases in each decorrelated spectral band. The spatial feature representations are extracted with these spatial feature bases. Then, spatial–spectral features are generated by concatenating the spatial feature representations in all/principal spectral bands. The experimental results indicate that the proposed method is flexible enough to generate rich spatial–spectral features and can outperform the other state-of-the-art methods. [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]

With the development of hyperspectral sensors, nowadays, we can easily acquire large amount of hyperspectral images (HSIs) with very high spatial resolution, which has led to a better identification of relatively small structures. Owing to the high spatial resolution, there are much less mixed pixels in the HSIs, and the boundaries between these categories are much clearer. However, the high spatial resolution also leads to complex and fine geometrical structures and high inner-class variability, which make the classification results very “noisy.” In this paper, we propose a multimorphological superpixel (MMSP) method to extract the spectral and spatial features and address the aforementioned problems. To reduce the difference within the same class and obtain multilevel spatial information, morphological features (multistructuring element extended morphological profile or multiattribute filter extended multi-attribute profiles) are first obtained from the original HSI. After that, simple linear iterative clustering segmentation method is performed on each morphological feature to acquire the MMSPs. Then, uniformity constraint is used to merge the MMSPs belonging to the same class which can avoid introducing the information from different classes and acquire spatial structures at object level. Subsequently, mean filtering is utilized to extract the spatial features within and among MMSPs. At last, base kernels are obtained from the spatial features and original HSI, and several multiple kernel learning methods are used to obtain the optimal kernel to incorporate into the support vector machine. Experiments conducted on three widely used real HSIs and compared with several well-known methods demonstrate the effectiveness of the proposed model. [ABSTRACT FROM PUBLISHER]

The deep convolutional neural network (CNN) is of great interest recently. It can provide excellent performance in hyperspectral image classification when the number of training samples is sufficiently large. In this paper, a novel pixel-pair method is proposed to significantly increase such a number, ensuring that the advantage of CNN can be actually offered. For a testing pixel, pixel-pairs, constructed by combining the center pixel and each of the surrounding pixels, are classified by the trained CNN, and the final label is then determined by a voting strategy. The proposed method utilizing deep CNN to learn pixel-pair features is expected to have more discriminative power. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than the conventional deep learning-based method. [ABSTRACT FROM AUTHOR]

In recent years, many studies on hyperspectral image classification have shown that using multiple features can effectively improve the classification accuracy. As a very powerful means of learning, multiple kernel learning (MKL) can conveniently be embedded in a variety of characteristics. This paper proposes a class-specific sparse MKL (CS-SMKL) framework to improve the capability of hyperspectral image classification. In terms of the features, extended multiattribute profiles are adopted because it can effectively represent the spatial and spectral information of hyperspectral images. CS-SMKL classifies the hyperspectral images, simultaneously learns class-specific significant features, and selects class-specific weights. Using an L1-norm constraint (i.e., group lasso) as the regularizer, we can enforce the sparsity at the group/feature level and automatically learn a compact feature set for the classification of any two classes. More precisely, our CS-SMKL determines the associated weights of optimal base kernels for any two classes and results in improved classification performances. The advantage of the proposed method is that only the features useful for the classification of any two classes can be retained, which leads to greatly enhanced discriminability. Experiments are conducted on three hyperspectral data sets. The experimental results show that the proposed method achieves better performances for hyperspectral image classification compared with several state-of-the-art algorithms, and the results confirm the capability of the method in selecting the useful features. [ABSTRACT FROM PUBLISHER]

A novel approach called Spectral–Spatial 1-D Manifold Embedding (SS1DME) is proposed in this paper for remotely sensed hyperspectral image (HSI) classification. This novel approach is based on a generalization of the recently developed smooth ordering model, which has gathered a great interest in the image processing area. In the proposed approach, first, we employ the spectral–spatial information-based affinity metric to learn the similarity of HSI pixels, where the contextual information is encoded into the affinity metric using spatial information. In our derived model, based on the obtained affinity metric, the created multiple 1-D manifold embeddings (1DMEs) consist of several different versions of 1DME of the same set of all HSI points. Since each 1DME of the data is a 1-D sequence, a label function on the data can be obtained by applying the simple 1-D signal processing tools (such as interpolation/regression). By collecting the predicted labels from these label functions, we build a subset of the current unlabeled points, on which the labels are correctly labeled with high confidence. Next, we add a proportion of the elements from this subset to the original labeled set to get the updated labeled set, which is used for the next running instance. Repeating this process for several loops, we get an extended labeled set, where the new members are correctly labeled by the label functions with much high confidence. Finally, we utilize the extended labeled set to build the target classifier for the whole HSI pixels. In the whole process, 1DME plays the role of learning data features from the given affinity metric. With the incrementation of learning features during iteration, the proposed scheme will gradually approximate the exact labels of all sample points. The proposed scheme is experimentally demonstrated using four real HSI data sets, exhibiting promising classification performance when compared with other recently introduced spatial analysis alternatives. [ABSTRACT FROM AUTHOR]

Due to the complementary properties of different features, multiple feature fusion has a large potential for hyperspectral imagery classification. At the meantime, hashing is promising in representing a high-dimensional float-type feature with extremely low bit binary codes while maintaining the performance. In this paper, we study the possibility of using hashing to fuse multiple features for hyperspectral imagery classification. For this purpose, we propose a multiple feature fusion framework to evaluate the performance of using different hashing methods. For comparison and completeness, we also have an extensive comparison to five subspace-based dimension reduction methods and six fusion-based methods which are popular solutions to deal with multiple features in hyperspectral image classification. Experimental results on four benchmark hyperspectral data sets demonstrate that using hashing to fuse multiple features can achieve comparable or better performance with the traditional subspace-based dimension reduction methods and fusion-based methods. Moreover, the binary features obtained by using hashing need much less storage and are faster to compute distances with the help of machine instructions. [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]

This paper presents a kernel-based feature selection method for the classification of hyperspectral images. The proposed method aims at selecting a subset of the original features that are both 1) relevant (discriminant) for the considered classification problem, i.e., preserve the functional relationship between input and output variables, and 2) invariant (stable) across different domains, i.e., minimize the data-set shift between the source and the target domains. Domains can be associated with hyperspectral images collected either on different geographical areas or on the same area at different times. We propose a novel measure of data-set shift for evaluating the domain stability, which computes the distance of the conditional distributions between the source and target domains in a reproducing kernel Hilbert space. Such a measure is defined on the basis of the kernel embeddings of the conditional distributions resulting in a nonparametric approach that does not require estimating the distribution of the classes. The adopted search strategy is based on a multiobjective optimization algorithm, which optimizes the two terms of the criterion function for the estimation of the Pareto-optimal solutions. This results in an effective approach of performing feature selection in a transfer learning setting. The experimental results obtained on two hyperspectral images show the effectiveness of the proposed method in selecting features with high generalization capabilities. [ABSTRACT FROM AUTHOR]

Background feature extraction is an important step in hyperspectral anomaly detection. However, the lack of prior information about anomaly targets and the complex spectral mixture result in a challenge for robust background feature extraction. Can we solve the anomaly detection problem other than with background feature extraction? Relative to anomalies, the background spectral signal is usually stable and slowly varying. In view of this point, slowly varying background analysis is introduced into anomaly detection in this paper. The desired background signals are obtained through a generalized eigenvalue decomposition problem based on the original data and the differential image. The extracted signals are then combined with a Mahalanobis distance metric to construct the detection estimation. Different data processing procedures and signal extraction patterns are respectively formulated to construct different versions of the slowly varying background-signal-based detector. The performances of the proposed methods were validated on both synthetic and real hyperspectral data. The experimental results reveal that the proposed methods outperform the state-of-the-art anomaly detectors, with superior receiver operating characteristic (ROC) curves, area-under-ROC values, and background–target separation. The sensitivity of the relevant parameters was also analyzed in an experimental analysis. [ABSTRACT FROM PUBLISHER]

Spatial–spectral feature fusion is well acknowledged as an effective method for hyperspectral (HS) image classification. Many previous studies have been devoted to this subject. However, these methods often regard the spatial–spectral high-dimensional data as 1-D vector and then extract informative features for classification. In this paper, we propose a new HS image classification method. Specifically, matrix-based spatial–spectral feature representation is designed for each pixel to capture the local spatial contextual and the spectral information of all the bands, which can well preserve the spatial–spectral correlation. Then, matrix-based discriminant analysis is adopted to learn the discriminative feature subspace for classification. To further improve the performance of discriminative subspace, a random sampling technique is used to produce a subspace ensemble for final HS image classification. Experiments are conducted on three HS remote sensing data sets acquired by different sensors, and experimental results demonstrate the efficiency of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper proposes a region kernel to measure the region-to-region distance similarity for hyperspectral image (HSI) classification. The region kernel is designed to be a linear combination of multiscale box kernels, which can handle the HSI regions with arbitrary shape and size. Integrating labeled pixels and labeled regions, we further propose a region-kernel-based support vector machine (RKSVM) classification framework. In RKSVM, three different composite kernels are constructed to describe the joint spatial–spectral similarity. Particularly, we design a desirable stack composite kernel that consists of the point-based kernel, the region-based kernel, and the cross point-to-region kernel. The effectiveness of the proposed RKSVM is validated on three benchmark hyperspectral data sets. Experimental results show the superiority of our region kernel method over the classical point kernel methods. [ABSTRACT FROM PUBLISHER]

It is of great interest in exploiting texture information for classification of hyperspectral imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich texture information of HSI is proposed. The proposed framework employs local binary patterns (LBPs) to extract local image features, such as edges, corners, and spots. Two levels of fusion (i.e., feature-level fusion and decision-level fusion) are applied to the extracted LBP features along with global Gabor features and original spectral features, where feature-level fusion involves concatenation of multiple features before the pattern classification process while decision-level fusion performs on probability outputs of each individual classification pipeline and soft-decision fusion rule is adopted to merge results from the classifier ensemble. Moreover, the efficient extreme learning machine with a very simple structure is employed as the classifier. Experimental results on several HSI data sets demonstrate that the proposed framework is superior to some traditional alternatives. [ABSTRACT FROM PUBLISHER]

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

Recently, many researchers have attempted to exploit spectral-spatial features and sparsity-based hyperspectral image classifiers for higher classification accuracy. However, challenges remain for efficient spectral-spatial feature generation and combination in the sparsity-based classifiers. This paper utilizes the empirical mode decomposition (EMD) and morphological wavelet transform (MWT) to gain spectral-spatial features, which can be significantly integrated by the sparse multitask learning (MTL). In the feature extraction step, the sum of the intrinsic mode functions extracted by an optimized EMD is taken as spectral features, whereas the spatial features are formed by the low-frequency components of one-level MWT. In the classification step, a kernel-based sparse MTL solved by the accelerated proximal gradient is applied to analyze both the spectral and spatial features simultaneously. Experiments are conducted on two benchmark data sets with different spectral and spatial resolutions. It is found that the proposed methods provide more accurate classification results compared to the state-of-the-art techniques with various ratio of training samples. [ABSTRACT FROM PUBLISHER]