Improving Hyperspectral Image Classification Using Spectral Information Divergence

By means of a sparse collaborative representation mechanism, sparse-representation-based classifiers show a superior performance in hyperspectral image (HSI) classification. Exploiting the similarity and distinctiveness of HSI neighboring pixels, we propose a new nearest regularized joint sparse representation (NRJSR) classification method in this letter. In the classification process of the central test pixel, the weights of different neighboring pixels and the sparse representation coefficients of different training samples are optimized simultaneously within a regularized sparsity model, which can obtain adaptive weights with good joint sparse representation ability. An alternative iteration strategy is used to solve the regularized joint sparsity model. The proposed NRJSR algorithm is tested on two benchmark HSI data sets. Experimental results demonstrate that the proposed algorithm performs better than other sparsity-based algorithms and spectral and spectral–spatial support vector machine classifiers.

A hyperspectral image can be considered as an image cube where the third dimension is the spectral domain represented by hundreds of spectral wavelengths. As a result, a hyperspectral image pixel is actually a column vector with dimension equal to the number of spectral bands and contains valuable spectral information that can be used to account for pixel variability, similarity and discrimination. We present a new hyperspectral measure, the spectral information measure (SIM), to describe spectral variability and two criteria, spectral information divergence and spectral discriminatory probability for spectral similarity and discrimination, respectively. The spectral information measure is an information-theoretic measure which treats each pixel as a random variable using its spectral signature histogram as the desired probability distribution. Spectral information divergence (SID) compares the similarity between two pixels by measuring the probabilistic discrepancy between two corresponding spectral signatures. The spectral discriminatory probability calculates spectral probabilities of a spectral database (library) relative to a pixel to be identified so as to achieve material identification. In order to compare the discriminatory power of one spectral measure relative to another, a criterion is also introduced for performance evaluation, which is based on the power of discriminating one pixel from another relative to a reference pixel. The experimental results demonstrate that the new hyperspectral measure can characterize spectral variability more effectively than the commonly used spectral angle mapper (SAM).

Traditional joint sparse representation based hyperspectral classification methods define a local region for each pixel. Through representing the pixels within the local region simultaneously, the class of the central pixel is able to be decided. A common limitation of this kind of methods is that only local pixels are considered in such methods, and thus, non-local information will be ignored. In order to improve the classification accuracy with the non-local information of hyperspectral images, a novel hyperspectral image classification based on K nearest neighbors (KNN) sparse representation is proposed in this paper. First, a feature space is defined based on the first principal components of the hyperspectral image and the spatial coordinates of different pixels. Then, in the defined feature space, K non-local neighborhoods of each pixel are found by using the KNN searching scheme. At last, through jointly representing the K pixels with the joint sparse model and comparing the representation residuals, the label of each pixel can be determined. Experiments performed on a widely used real HSI data set show that the proposed method obtain better classification performances when compared with the traditional joint sparse representation method and other recently proposed hyperspectral image classification methods.

In this paper, we present a facial expression recognition method based on Gabor feature and sparse representation. Sparse Representation based Classification (SRC) has been widely used in computer vision and pattern recognition. And Gabor filter banks can be used to approximately model the signal processing in visual primary cortex. We believe that the nature of the attractive performance of SRC and Gabor feature lies in that they both followed the natures of signal perception of retina and information processing of cortex in human vision. Therefore, we combined the Gabor feature and SRC for facial expression recognition. The comparison experiments of proposed Gabor+SRC algorithm and straightforward SRC application are conducted on JAFFE database. And the experimental results showed the attractive performance of the proposed Gabor+SRC method.

Joint discriminative and shared dictionary learning with dictionary extension strategy for bearing fault classification

Recently, multitask learning was applied to visual tracking by learning sparse particle representations in a joint task, which led to the so-called multitask tracking algorithm (MTT). Although MTT shows impressive tracking performances by mining the interdependencies between particles, the individual feature of each particle is underestimated. The utilized L1,q norm regularization assumes all features are shared between all particles and results in nearly identical representation coefficients in nonsparse rows. We propose a composite sparse multitask tracking algorithm (CSMTT). We develop a composite sparse model to formulate the object appearance as a combination of the shared feature component, the individual feature component, and the outlier component. The composite sparsity is achieved via the L1,∞ and L1,1 norm minimization, and is optimized by the alternating direction method of multipliers, which provides a favorable reconstruction performance and an impressive computational efficiency. Moreover, a dynamical dictionary updating scheme is proposed to capture appearance changes. CSMTT is tested on real-world video sequences under various challenges, and experimental results show that the composite sparse model achieves noticeable lower reconstruction errors and higher computational speeds than traditional sparse models, and CSMTT has consistently better tracking performances against seven state-of-the-art trackers.

The theory of compressive sensing applies the sparse representation to the extraction of useful information from signals and brings a breakthrough to the theory of signal sampling. Based on compressive sensing, sparse representation-based classification (SRC) is proposed. SRC uses the compressibility of the image data to represent the facial image sparsely and could solve the problems of both massive calculation and information loss in dealing with signals. SRC does not, however, deal with the effects of variable illumination, posture and incomplete face image, which could result in severe performance degradation. This paper studies the differences between SRC recognition and human recognition. We find that there is an obvious disadvantage in the SRC algorithm, and it will significantly affect the face recognition performance in actual environment, especially for the variable illumination, posture and incomplete face image. To overcome the disadvantage of SRC algorithm, we propose an SRC-based twice face recognition algorithm named T_SRC. T_SRC uses bidirectional PCA, linear discriminant analysis and GradientFace to execute multichannel analysis, which could extract more "holistic/configural" face features in actual environment than by using SRC algorithm directly. Based on the multichannel analysis, we identify the test image by SRC firstly. Then, by analyzing the residual, this algorithm could decide whether the twice recognition is needed. If the twice recognition is needed, T_SRC extracts the facial details ("featural" face features) by the improved Harris point and Gabor filter detector. We suppose that the facial details are more stable than the whole face in actual environment, and later experiments verify our assumption. At last, this algorithm identifies the class of the test image by SRC again. The results of the experiments prove that the T_SRC algorithm has better recognition rate than SRC.

Obesity is becoming a widely concerned health problem of most part of the world. Computer vision based recognition system has great potential to be an efficient tool to monitor food intake and cope with the growing problem of obesity. This paper proposes a food recognition algorithm based on sparse representation. The proposed algorithm learns overcomplete dictionaries from local descriptors including texture and color features that are extracted from food image patches. With the learned two overcomplete dictionaries, a feature vector of the food image can be generated with the sparsely encoded local descriptors. SVM is used for the classification. This research creates a Chinese food image dataset for experiments. Classifying Chinese food is more challenging because they are not as distinguishable visually as western food. The proposed algorithm achieves an average classification accuracy of 97.91% in a dataset of 5309 images that comprises 18 classes. The proposed method can be easily employed to dataset with more classes. Our results demonstrate the feasibility of using the proposed algorithm for food recognition.

Single-sample face recognition is one of the most challenging problems in face recognition. We propose a novel algorithm to address this problem based on a sparse representation based classification (SRC) framework. The new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required gallery images to one sample per class. To compensate for the missing illumination information traditionally provided by multiple gallery images, a sparse illumination learning and transfer (SILT) technique is introduced. The illumination in SILT is learned by fitting illumination examples of auxiliary face images from one or more additional subjects with a sparsely-used illumination dictionary. By enforcing a sparse representation of the query image in the illumination dictionary, the SILT can effectively recover and transfer the illumination and pose information from the alignment stage to the recognition stage. Our extensive experiments have demonstrated that the new algorithms significantly outperform the state of the art in the single-sample regime and with less restrictions. In particular, the single-sample face alignment accuracy is comparable to that of the well-known Deformable SRC algorithm using multiple gallery images per class. Furthermore, the face recognition accuracy exceeds those of the SRC and Extended SRC algorithms using hand labeled alignment initialization.

We propose a new spectral-spatial method for hyperspectral image classification based on a graph cut. The classification task is formulated as an energy minimization problem on the graph of image pixels, and is solved by using the graph-cut α-expansion approach. The energy to optimize is computed as a sum of data and interaction energy terms, respectively. The data energy term is computed using the outputs of the probabilistic support vector machines classification. The second energy term, which expresses the interaction between spatially adjacent pixels, is computed by using dissimilarity measures between spectral vectors, such as vector norms, spectral angle mapper and spectral information divergence. Experimental results on hyperspectral images captured by the RO-SIS and the AVIRIS sensors reveal that the proposed method yields higher classification accuracies when compared to the recent state-of-the-art approaches.

Detecting and monitoring landslides are hot topics in remote sensing community, particularly with the development of remote sensing technologies and the significant progress of computer vision. To the best of our knowledge, no study focused on deep learning-based methods for landslide detection on hyperspectral images. We proposes a deep learning framework with constraints to detect landslides on hyperspectral image. The framework consists of two steps. First, a deep belief network is employed to extract the spectral–spatial features of a landslide. Second, we insert the high-level features and constraints into a logistic regression classifier for verifying the landslide. Experimental results demonstrated that the framework can achieve higher overall accuracy when compared to traditional hyperspectral image classification methods. The precision of the landslide detection on the whole image, obtained by the proposed method, can reach 97.91%, whereas the precision of the linear support vector machine, spectral information divergence, and spectral angle match are 94.36%, 84.50%, and 86.44%, respectively. Also, this article reveals that the high-level feature extraction system has a significant potential for landslide detection, especially in multi-source remote sensing.

The high spectral resolution of hyperspectral images (HSI) requires a heavy processing load. Assigning each pixel to a group in the image, which is called superpixel, and processing the superpixels instead of the pixels is resorted as a means to overcome this challenge in the hyperspectral literature. In this paper, we propose an algorithm to segment a hyperspectral image into superpixels by means of iteratively updating the boundary pixels of superpixels. We first explore the optimal similarity metric for the boundary pixel updates with the contraint of keeping the superpixel boundaries aligned with the object boundaries in the image. We investigate two approaches for similarity detection between pixels during this update, first comparing the hyperspectral pixels individually, and second, comparing the pixels by using also their neigborhood. The spectral similarity metrics used for investigation are selected as spectral angle mapping (SAM) [1], spectral information divergence (SID) [2] and spatial coherence distance [3] due to their common usage. The proposed approach is compared with a pioneer state-of-the-art superpixel algorithm, SLIC [4], and its superiority is verified in terms of the superpixelization performance metrics, namely boundary recall and undersegmentation error [5].

For the instrument limitation and imperfect imaging optics, it is difficult to acquire high spatial resolution hyperspectral imagery. Low spatial resolution will result in a lot of mixed pixels and greatly degrade the detection and recognition performance, affect the related application in civil and military fields. As a powerful statistical image modeling technique, sparse representation can be utilized to analyze the hyperspectral image efficiently. Hyperspectral imagery is intrinsically sparse in spatial and spectral domains, and image super-resolution quality largely depends on whether the prior knowledge is utilized properly. In this article, we propose a novel hyperspectral imagery super-resolution method by utilizing the sparse representation and spectral mixing model. Based on the sparse representation model and hyperspectral image acquisition process model, small patches of hyperspectral observations from different wavelengths can be represented as weighted linear combinations of a small number of atoms in pre-trained dictionary. Then super-resolution is treated as a least squares problem with sparse constraints. To maintain the spectral consistency, we further introduce an adaptive regularization terms into the sparse representation framework by combining the linear spectrum mixing model. Extensive experiments validate that the proposed method achieves much better results.

Bayesian sparse hierarchical model for image denoising

The endmember extraction is a challenging problem in spectral unmixing (SU) of a mixed pixel in hyperspectral imagery. There are plenty of attempts to solve the endmember extraction problem. Still, the pure pixel assumption-based algorithms have probably been used most in solving the endmember extraction of SU due to the light computational burden. These pure pixel assumption-based algorithms usually follow one of the criteria: (1) Maximum simplex volume or (2) Extreme projection on a subspace. We propose a novel integrated framework that uses both the criteria mentioned above and the proposed one is referred to as the Convexity-based Pure Index (CPI) algorithm. The CPI generates a fixed number of convex sets based on the number of available bands in the hyperspectral image. The algorithm defines the purity score based on the availability of pixels in the convex sets for the two-band data. The CPI has been compared with contemporary algorithms such as Automatic Target Generation Process (ATGP), Vertex Component Analysis (VCA), Pixel Purity Index (PPI), Successive Volume MAXimization (SVMAX), Alternating Volume MAXimization (AVMAX), TRIple-P: P-norm based Pure Pixel identification (TRIP), Successive Decoupled Volume Max–Min (SDVMM), Negative ABundance-Oriented (NABO), and Entropy-based Convex Set Optimization (ECSO). The metrics, Spectral Angle Distance (SAD) and Spectral Information Divergence (SID) used in the comparison were improved up to 5.9% and 9%, respectively. The CPI outperforms prevailing algorithms on real benchmark data and new AVIRIS-NG data. The robustness of the CPI is also tested for various noisy synthetic data. The efficacy of the proposed algorithm is also tested by using qualitative analysis by visualizing the spectra comparison, and abundance maps for all real data.