Spectral Information Of Hyperspectral Images Research Articles

A graphical estimation and multiple-sparse representation strategy for hyperspectral anomaly detection

Most hyperspectral anomaly detection (AD) methods only utilize spectral information in hyperspectral images (HSI) to distinguish anomaly targets from backgrounds. However, the features extracted by spectral difference are incomplete and will limit the performance of AD. To overcome the drawbacks and obtain abundant feature information of diverse components, we propose a novel hyperspectral AD strategy based on graphical estimation and multiple-sparse representation. First, the potential anomalies can be screened out by a prior graphical connected region (PGCR) estimation, by which the potential anomalies are distinguished from robust background spatially. Second, based on sparse representation (SR) theory, a multiple SR framework is utilized to select spectral feature information. It can pick out the representative spectra that further enlarge anomaly’s distinction from background. Consequently, a fusion strategy is designed to comprehensively determine different groups of results generated by multiple SR method and to obtain the final detection result. In the experiment, the proposed method achieves a more superior result than other classic or popular AD detectors. A detailed analysis of the relevant parameters and their sensitivity under specific condition are also discussed in the experimental section.

Morphology‐based structure‐preserving projection for spectral–spatial feature extraction and classification of hyperspectral data

Incorporation of spatial information besides rich spectral information of hyperspectral image significantly enhances data classification accuracy. A morphology-based feature extraction and classification framework is proposed here, which includes the local neighbourhood information in a spatial window for extension of training set. The proposed method is morphology-based structure-preserving projection (MSPP) and tries to preserve the data structure in spectral-spatial feature space. Moreover, MSPP increases the class discrimination ability by defining a similarity matrix constructed by extended spectral-spatial training samples. The experimental results show the superiority of MSPP compared to some state-of-the-art classification methods from the classification accuracy point of view.

SLIC Superpixel-Based l2,1-Norm Robust Principal Component Analysis for Hyperspectral Image Classification.

Hyperspectral Images (HSIs) contain enriched information due to the presence of various bands, which have gained attention for the past few decades. However, explosive growth in HSIs’ scale and dimensions causes “Curse of dimensionality” and “Hughes phenomenon”. Dimensionality reduction has become an important means to overcome the “Curse of dimensionality”. In hyperspectral images, labeled samples are more difficult to collect because they require many labor and material resources. Semi-supervised dimensionality reduction is very important in mining high-dimensional data due to the lack of costly-labeled samples. The promotion of the supervised dimensionality reduction method to the semi-supervised method is mostly done by graph, which is a powerful tool for characterizing data relationships and manifold exploration. To take advantage of the spatial information of data, we put forward a novel graph construction method for semi-supervised learning, called SLIC Superpixel-based -norm Robust Principal Component Analysis (SURPCA2,1), which integrates superpixel segmentation method Simple Linear Iterative Clustering (SLIC) into Low-rank Decomposition. First, the SLIC algorithm is adopted to obtain the spatial homogeneous regions of HSI. Then, the -norm RPCA is exploited in each superpixel area, which captures the global information of homogeneous regions and preserves spectral subspace segmentation of HSIs very well. Therefore, we have explored the spatial and spectral information of hyperspectral image simultaneously by combining superpixel segmentation with RPCA. Finally, a semi-supervised dimensionality reduction framework based on SURPCA2,1 graph is used for feature extraction task. Extensive experiments on multiple HSIs showed that the proposed spectral-spatial SURPCA2,1 is always comparable to other compared graphs with few labeled samples.

Regularized Fuzzy Discriminant Analysis for Hyperspectral Image Classification With Noisy Labels

Numerous studies have been conducted for hyperspectral image (HSI) classification by assuming that the label information of training data is fully available and correct. However, such an assumption may not always be true in practical applications, which could impact feature extraction methods and eventually compromise the performance of hyperspectral image classification. To address this issue in hyperspectral image classification, we propose a Regularized Fuzzy Discriminant Analysis (RFDA) based feature extraction method to effectively utilize the spatial and spectral information of HSIs with noisy labels. Firstly, the physical properties of HSIs are explored to reconstruct the data. Secondly, the labeled training samples and their unlabeled spatial neighborhood samples are fuzzified using the Fuzzy K-Nearest Neighbor (FKNN) method. Finally, a regularization term using a Fuzzy Locality Preserving Scatter (FLPS) matrix is integrated into fuzzy discriminant analysis, and the spatial-spectral information of HSIs is effectively fused to construct the projection matrix. As a result, the proposed method not only corrects the mislabeled samples effectively, but also preserves the neighborhood relationship among the pixels in the spatial domain and the fundamental structure among the samples in the spectral-domain, which is beneficial for hyperspectral image classification. Experimental results on three synthetic datasets and three public hyperspectral datasets show that our proposed RFDA method outperforms several state-of-the-art feature extraction methods in terms of classification accuracy.

Constrained Manifold Learning for Hyperspectral Imagery Visualization

Displaying the large number of bands in a hyperspectral image (HSI) on a trichromatic monitor is important for HSI processing and analysis system. The visualized image shall convey as much information as possible from the original HSI and meanwhile facilitate the image interpretation. However, most existing methods display HSIs in false color, which contradicts with user experience and expectation. In this paper, we propose a visualization approach based on constrained manifold learning, whose goal is to learn a visualized image that not only preserves the manifold structure of the HSI, but also has natural colors. Manifold learning preserves the image structure by forcing pixels with similar signatures to be displayed with similar colors. A composite kernel is applied in manifold learning to incorporate both the spatial and spectral information of HSI in the embedded space. The colors of the output image are constrained by a corresponding natural-looking RGB image, which can either be generated from the HSI itself (e.g., band selection from the visible wavelength) or be captured by a separate device. Our method can be done at instance level and feature level. Instance-level learning directly obtains the RGB coordinates for the pixels in the HSI while feature-level learning learns an explicit mapping function from the high-dimensional spectral space to the RGB space. Experimental results demonstrate the advantage of the proposed method in information preservation and natural color visualization.

Anomaly Detection for Hyperspectral Images Based on Anisotropic Spatial-Spectral Total Variation and Sparse Constraint

A novel anomaly detection method for hyperspectral images (HSIs) is proposed based on anisotropic spatial-spectral total variation and sparse constraint. HSIs are assumed to be not only smooth in spectral dimension but also piecewise smooth in spatial dimension. The proposed method adopts the anisotropic spatial-spectral total variation model which combines 2D spatial total variation and 1D spectral variation to explore the spatial-spectral smooth property of HSIs. Meanwhile, the sparse property of anomalies is exploited for its low probability in the image. To utilize both the spatial and spectral information of HSIs, we preserve the original cubic form of HSIs and divide the HSIs into three 3D arrays, each representing the background, the anomaly, and the noise respectively. By using anisotropic spatial-spectral total variation regularization on the background component and sparse constraint on the anomaly component, this anomaly detection problem has therefore been formulated as a constraint optimization problem whose solution has been derived by alternately using Split Bregman Method and Go Decomposition (GoDec) Method. Experimental results on hyperspectral datasets illustrate that our proposed method has a better detection performance than state-of-the-art hyperspectral anomaly detection methods.

Destriping hyperspectral imagery via spectral–spatial low-rank representation

In this paper, we address the problem of removing striping noise in hyperspectral images (HSI). To this end, we develop a novel destriping method by taking advantage of the spectral and spatial information of the hyperspectral image simultaneously. To obtain satisfactory destriped results, our consideration is two-fold: (1) To model the spectral information of HSI, we utilize low-rank representation to exploit the low-rank property of HSI; (2) To incorporate the spatial information into our method, we adopt the Huber-Markov random field (HMRF) prior model to preserve the edge information of HSI while reducing the striping noise. Finally, we integrate the spectral and spatial model into a unified objective function. In addition, we also devise an effective optimization algorithm to minimize the objective function. The experimental results on real-world HSI data validate the efficacy of the proposed scheme for destriping of HSI.

The evaluation and application of an urban land cover map with image data fusion and laboratory measurements

High spatial and spectral resolution aerial images make it possible to develop detailed and large-scale (about 1:5,000) urban land cover maps. The main objectives of this study are (1) to evaluate the correlation between laboratory and hyperspectral image spectra to select proper bands and training samples for classification; (2) to develop a classification process to combine the spectral and spatial information of multispectral and hyperspectral images and make an urban land cover map for the study area in Szeged, Hungary; and (3) to examine the effect of different roof types on the modification of surface temperature. Reference materials were collected from the training area and their spectral characteristics were measured by a laboratory spectrometer. The hyperspectral image and laboratory spectral data between 500-800 nm showed a very strong correlation, the correlation coefficient was 0.99. The urban land cover map was produced by the combination of segmentation procedure and Spectral Angle Mapper (SAM) method using the spatial information derived from multispectral image and the spectral information of the hyperspectral image. Eight land cover classes were identified as impervious surfaces (asphalt, 4 types of tiled roof), water, and green vegetation. The overall accuracy of urban land cover map was 87.9 per cent. According to the results, an accurate large-scale urban land cover map can be generated from the fusion of multispectral and hyperspectral images. We presented that certain roof types have significant effect on surface temperature, which is strongly connected to the urban heat island phenomenon, and influences population health.

Hypergraph Embedding for Spatial-Spectral Joint Feature Extraction in Hyperspectral Images

The fusion of spatial and spectral information in hyperspectral images (HSIs) is useful for improving the classification accuracy. However, this approach usually results in features of higher dimension and the curse of the dimensionality problem may arise resulting from the small ratio between the number of training samples and the dimensionality of features. To ease this problem, we propose a novel algorithm for spatial-spectral feature extraction based on hypergraph embedding. Firstly, each HSI pixel is regarded as a vertex and the joint of extended morphological profiles (EMP) and spectral features is adopted as the feature associated with the vertex. A hypergraph is then constructed by the K-Nearest-Neighbor method, in which each pixel and its most K relevant pixels are linked as one hyperedge to represent the complex relationships between HSI pixels. Secondly, the hypergraph embedding model is designed to learn a low dimensional feature with the reservation of geometric structure of HSI. An adaptive hyperedge weight estimation scheme is also introduced to preserve the prominent hyperedges by the regularization constraint on the weight. Finally, the learned low-dimensional features are fed to the support vector machine (SVM) for classification. The experimental results on three benchmark hyperspectral databases are presented. They highlight the importance of spatial–spectral joint features embedding for the accurate classification of HSI data. The weight estimation is better for further improving the classification accuracy. These experimental results verify the proposed method.

Band Selection for Nonlinear Unmixing of Hyperspectral Images as a Maximal Clique Problem.

Kernel-based nonlinear mixing models have been applied to unmix spectral information of hyperspectral images when the type of mixing occurring in the scene is too complex or unknown. Such methods, however, usually require the inversion of matrices of sizes equal to the number of spectral bands. Reducing the computational load of these methods remains a challenge in large-scale applications. This paper proposes a centralized band selection (BS) method for supervised unmixing in the reproducing kernel Hilbert space. It is based upon the coherence criterion, which sets the largest value allowed for correlations between the basis kernel functions characterizing the selected bands in the unmixing model. We show that the proposed BS approach is equivalent to solving a maximum clique problem, i.e., searching for the biggest complete subgraph in a graph. Furthermore, we devise a strategy for selecting the coherence threshold and the Gaussian kernel bandwidth using coherence bounds for linearly independent bases. Simulation results illustrate the efficiency of the proposed method.

CP tensor-based compression of hyperspectral images.

In this paper, an effective CANDECOMP/PARAFAC tensor-based compression (CPTBC) approach is proposed for on-ground hyperspectral images (HSIs). By considering the observed HSI cube as a whole three-order tensor, the proposed CPTBC method utilizes the CANDECOMP/PARAFAC tensor decomposition to decompose the original HSI data into the sum of R rank-1 tensors, which can simultaneously exploit both the spatial and spectral information of HSIs. Specifically, compared with the original HSI data, the R rank-1 tensors have fewer non-zero entries. In addition, non-zero entries of the R rank-1 tensors are sparse and follow a regular distribution. Therefore, the HSI can be efficiently compressed into R rank-1 tensors with the proposed CPTBC method. Our experimental results on real three HSIs demonstrate the superiority of the proposed CPTBC method over several well-known compression approaches and the average PSNR improvements of the proposed method over the six compared methods (i.e., MPEG4, band-wise JPEG2000, TD, 3D-SPECK, 3D-TCE, 3D-TARP) are more than 13, 10, 6, 4, 3, and 3 dB, respectively.

Class-Specific Sparse Multiple Kernel Learning for Spectral–Spatial Hyperspectral Image Classification

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 $L_{1}$ -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.

Morphological Principal Component Analysis for Hyperspectral Image Analysis

This article deals with the issue of reducing the spectral dimension of a hyperspectral image using principal component analysis (PCA). To perform this dimensionality reduction, we propose the addition of spatial information in order to improve the features that are extracted. Several approaches proposed to add spatial information are discussed in this article. They are based on mathematical morphology operators. These morphological operators are the area opening/closing, granulometries and grey-scale distance function. We name the proposed family of techniques the Morphological Principal Component Analysis (MorphPCA). Present approaches provide new feature spaces able to jointly handle the spatial and spectral information of hyperspectral images. They are computationally simple since the key element is the computation of an empirical covariance matrix which integrates simultaneously both spatial and spectral information. The performance of the different feature spaces is assessed for different tasks in order to prove their practical interest.

Block-Based Fusion Algorithm With Simulated Band Generation for Hyperspectral and Multispectral Images of Partially Different Wavelength Ranges

As current and future satellite systems provide both hyperspectral and multispectral images, a need has arisen for image fusion using hyperspectral and multispectral images to improve the fusion quality. This study introduces a hyperspectral image fusion algorithm using multispectral images with a higher spatial resolution and partially different wavelength range compared with the corresponding hyperspectral images. This study focuses on an image fusion technique that enhances the spatial quality and preserves the spectral information of hyperspectral images. The proposed algorithm generates a simulated multispectral band via a spectral unmixing technique and extracts high-frequency information based on blocks of associated bands. The algorithm was applied to Compact Airborne Spectrographic Imager (CASI) datasets acquired in two modes and was compared with two existing methods. Although the wavelength range of the multispectral image did not coincide with that of the hyperspectral image, the proposed algorithm efficiently improved the spatial details and preserved the spectral information of the fused results.

Semi-supervised change detection method for multi-temporal hyperspectral images

Abstract Change detection is one of the most important open topics for multi-temporal remote sensing technology to observe the earth. Recently, many methods are proposed to detect the land-cover change information by multi-temporal hyperspectral images. However, many existing traditional change detection methods failed to utilize the spectral information effectively. Hence the models are not robust enough for more widely applications with “noise” bands. In this case, a semi-supervised distance metric learning method is proposed to detect the change areas by abundant spectral information of hyperspectral image under the “noisy” condition. This paper focuses on semi-supervised change detection method, and proposes a new distance metric learning framework for change detection in “noisy” condition with three mainly contributions: (1) Distance metric learning is demonstrated to be an effective method for revealing the change information by high spectral features. (2) An evolution regular framework is utilized to handle change detection under a “noisy” condition without removing any noise bands, which is impacted by atmosphere (or water) and always removed manually in other literatures. (3) A semi-supervised Laplacian Regularized Metric Learning method is exploited to tackle the ill-posed sample problem, and large unlabeled data is exploited in our method. The proposed method is performed on two multi-temporal hyperspectral datasets. Experimental results show that the proposed method outperforms the state-of-the-art change detection methods under both “ideal” and “noisy” conditions.

Unsupervised methods for the classification of hyperspectral images with low spatial resolution

The problem of structure detection and unsupervised classification of hyperspectral images with low spatial resolution is addressed in this paper. Hyperspectral imaging is a continuously growing area in remote sensing applications. The wide spectral range, providing a very high spectral resolution, allows the detection and classification surfaces and chemical elements of the observed image. The main problem of hyperspectral images is that the spatial resolution can vary from a few to tens of meters. Many factors, such as imperfect imaging optics, atmospheric scattering, secondary illumination effects and sensor noise cause a degradation of the acquired image quality, making the spatial resolution one of the most expensive and hardest to improve in imaging systems. Due to such a constraint, mixed pixels, e.g., pixels containing mixture of different materials, are quite common in hyperspectral images. In this work, we exploit the rich spectral information of hyperspectral images to deal with the problem. Two methods, based on the concept of spectral unmixing and unsupervised classification, are proposed to obtain thematic maps at a finer spatial scale in a totally unsupervised way. Experiments are carried out on one simulated and two real hyperspectral data sets and clearly show the comparative effectiveness of the proposed method with respect to traditional unsupervised methods, both for classification and detection of spatial structures.

Integration of Spatial–Spectral Information for Resolution Enhancement in Hyperspectral Images

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In this paper, a new algorithm is proposed for resolution enhancement in hyperspectral images (HSIs). The key techniques are included: spectral unmixing and superresolution mapping, by which spatial and spectral information of HSIs is substantially fused. The proposed algorithm first represents each pixel in scene as a linear combination of landcover spectra and noise. Then, a fully constrained least squares algorithm is used to obtain the proportion of each landcover in each pixel, i.e., abundance, subjecting to two constraints: nonnegativity and sum-to-one. After that, superresolution mapping is performed on high-resolution grids according to spectral unmixing abundances of each landcover and following spatial correlation of clutters. Thus, by reasonably integrating spatial and spectral information of landcovers in HSIs, the proposed algorithm realizes resolution enhancement of the HSIs based on a back-propagation neural network. The proposed algorithm is independent from the <emphasis emphasistype="italic">a priori</emphasis> information associated with original HSIs, i.e., a main merit of the algorithm. In order to evaluate the performance of the new algorithm, numerical experiments are conducted on both simulated images and real HSIs collected by the Airborne Visible/Infrared Imaging Spectrometer. The proposed algorithm is compared with the traditional method in the experiments. The experimental results prove that the proposed algorithm effectively enhances the resolution of HSIs and indicate its applicability. </para>