Spectral Bands Of Hyperspectral Images Research Articles

Background Reconstruction via 3D-Transformer Network for Hyperspectral Anomaly Detection

Recently, autoencoder (AE)-based anomaly detection approaches for hyperspectral images (HSIs) have been extensively proposed; however, the reconstruction accuracy is susceptible to the anomalies and noises. Moreover, these AE-based anomaly detectors simply compress each pixel into a hidden-layer with a lower dimension and then reconstruct it, which does not consider the spatial properties among pixels. To solve the above issues, this paper proposes a background reconstruction framework via a 3D-transformer (3DTR) network for anomaly detection in HSIs. The experimental results on both synthetic and real hyperspectral datasets demonstrate that the proposed 3DTR network is able to effectively detect most of the anomalies by comprehensively considering the spatial correlations among pixels and the spectral similarity among spectral bands of HSIs. In addition, the proposed method exhibits fewer false alarms than both traditional and state-of-the-art (including model-based and AE-based) anomaly detectors owing to the adopted pre-detection procedure and the proposed novel patch-generation method in this paper. Moreover, two ablation experiments adequately verified the effectiveness of each component in the proposed method.

Hyperspectral Super-Resolution Reconstruction Network Based on Hybrid Convolution and Spectral Symmetry Preservation

Hyperspectral images (HSI) have high-dimensional and complex spectral characteristics, with dozens or even hundreds of bands covering the same area of pixels. The rich information of the ground objects makes hyperspectral images widely used in satellite remote sensing. Due to the limitations of remote sensing satellite sensors, hyperspectral images suffer from insufficient spatial resolution. Therefore, utilizing software algorithms to improve the spatial resolution of hyperspectral images has become an urgent problem that needs to be solved. The spatial information and spectral information of hyperspectral images are strongly correlated. If only the spatial resolution is improved, it often damages the spectral information. Inspired by the high correlation between spectral information in adjacent spectral bands of hyperspectral images, a hybrid convolution and spectral symmetry preservation network has been proposed for hyperspectral super-resolution reconstruction. This includes a model to integrate information from neighboring spectral bands to supplement target band feature information. The proposed model introduces flexible spatial-spectral symmetric 3D convolution in the network structure to extract low-resolution and neighboring band features. At the same time, a combination of deformable convolution and attention mechanisms is used to extract information from low-resolution bands. Finally, multiple bands are fused in the reconstruction module, and the high-resolution hyperspectral image containing global information is obtained by Fourier transform upsampling. Experiments were conducted on the indoor hyperspectral image dataset CAVE, the airborne hyperspectral dataset Pavia Center, and Chikusei. In the X2 super-resolution task, the PSNR values achieved on the CAVE, Pavia Center, and Chikusei datasets were 46.335, 36.321, and 46.310, respectively. In the X4 super-resolution task, the PSNR values achieved on the CAVE, Pavia Center, and Chikusei datasets were 41.218, 30.377, and 38.365, respectively. The results show that our method outperforms many advanced algorithms in objective indicators such as PSNR and SSIM while maintaining the spectral characteristics of hyperspectral images.

Domain transfer and difference-aware band weighting for object tracking in hyperspectral videos

ABSTRACT Object tracking plays an important role in computer vision. In recent years, hyperspectral object tracking has gained increasing attention because the material information contained in a large number of spectral bands of hyperspectral images (HSIs), which is critical in distinguishing the target from the background. However, owing to the high-dimensional characteristics of HSIs and complex real-world scenarios, hyperspectral object tracking remains a challenging task. In this paper, we propose a domain transfer and difference-aware band weighting (DT-DBW) tracker for hyperspectral object tracking. Firstly, a domain transfer module is designed to adjust the feature distribution of HSIs, so that the deep learning object tracker can be effectively applied to hyperspectral videos. To further improve the performance and accuracy of the tracker, a difference-aware band weighting module is implemented to exploit the spectral difference features between the target and the background to generate individual band weights for the hyperspectral videos. Through the band weighting operation, the spectral response value of HSIs is recalibrated to enhance the value of spectral information and suppress the background spectral information. Experimental results on hyperspectral datasets demonstrate that the Area-Under-Curve (AUC) and tracking speed of DT-DBW tracker are up to 0.647 and 48.6 FPS, outperforming existing hyperspectral object trackers.

Group Shuffle and Spectral-Spatial Fusion for Hyperspectral Image Super-Resolution

Recently, super-resolution (SR) tasks for single hyperspectral images have been extensively investigated and significant progress has been made by introducing advanced deep learning-based methods. However, hyperspectral image SR is still a challenging problem because of the numerous narrow and successive spectral bands of hyperspectral images. Existing methods adopt the group reconstruction mode to avoid the unbearable computational complexity brought by the high spectral dimensionality. Nevertheless, the group data lose the spectral responses in other ranges and preserve the information redundancy caused by continuous and similar spectrograms, thus containing too little information. In this paper, we propose a novel single hyperspectral image SR method named GSSR, which pioneers the exploration of tweaking spectral band sequence to improve the reconstruction effect. Specifically, we design the group shuffle that leverages interval sampling to produce new groups for separating adjacent and extremely similar bands. In this way, each group of data has more varied spectral responses and less redundant information. After the group shuffle, the spectral-spatial feature fusion block is employed to exploit the spectral-spatial features. To compensate for the adjustment of spectral order by the group shuffle, the local spectral continuity constraint module is subsequently appended to constrain the features for ensuring the spectral continuity. Experimental results on both natural and remote sensing hyperspectral images demonstrate that the proposed method achieves the best performance compared to the state-of-the-art methods.

Unsupervised Robust Projection Learning by Low-Rank and Sparse Decomposition for Hyperspectral Feature Extraction

Owing to the strong correlation between the spectral bands of hyperspectral images (HSIs), many feature extraction (FE) methods have been proposed to reduce the redundancy of hyperspectral data. However, Euclidean distance-based FE methods are sensitive to noise. To address this issue, this letter proposed a new unsupervised FE method called robust projection learning (RPL) by integrating the low-rank and sparse decomposition with projection learning. Specifically, in order to enhance the discrimination of traditional robust principal component analysis (RPCA), discriminative RPCA (DRPCA) is first proposed by decomposing the raw data into a low-rank part, a discriminative sparse part, and a structured noise. Moreover, for the purpose of redundancy reduction, projection learning is integrated into DRPCA to obtain a projection matrix with robustness and discrimination. To verify the validity of RPL, two real hyperspectral data sets are used for basic comparison and robust analysis. The corresponding experimental results demonstrate that RPL outperforms the comparative FE methods.

Selection of informative hyperspectral band subsets based on entropy and correlation

ABSTRACTThe article proposes two novel and relatively simple unsupervised procedures for the selection of informative small subsets of spectral bands in hyperspectral images. To ensure the informativeness of the subsets, bands featuring higher entropy are included. The correlation of band images is restricted to avoid redundancy of the subsets. The entropy multiple correlation ratio procedure employs the entropy-correlation ratio for the selection of spectral bands. The entropy-based correlated band grouping (ECBG) procedure divides the spectrum into groups of bands featuring highly correlated images. The subsets obtained were characterized by the performance of classifiers using only data from included bands. The ECBG procedure provided better results than the alternatives if the number of selected bands was low. Another advantage of this procedure is the possibility of averaging the images obtained for spectral bands within the groups found. It is shown that classification results are significantly improved if such an averaging is used. In the data acquisition practice, it can be used for a purposeful merging of spectral bands in the configuration of hyperspectral imagers, which allows one to reduce the amount of data to be saved in real time and thus helps one to improve the achievable spatial resolution.

Evaluating the performance of the wavelet transform in extracting spectral alteration features from hyperspectral images

ABSTRACTWith the large number of spectral bands in hyperspectral images, the conventional classification methods commonly used for multispectral images are not effectively applicable. To overcome such difficulty, feature extraction methods could be used to reduce the dimension of hyperspectral images. In this study, the performance of the principal component analysis (PCA) as a widely used technique in feature extraction and the wavelet transform as a powerful decomposition tool on hyperspectral data is compared. In wavelet transform, a non-linear wavelet feature extraction was employed to select efficient features for more classification accuracy. Shortwave infrared bands of Hyperion imagery were selected as input data. The study area includes two well-known porphyry copper deposits, Darrehzar and Sarcheshmeh, located in the Iranian copper belt. Neural networks (NN), Support Vector Machine (SVM), and Spectral Angle Mapper (SAM) were used for multi-class classification based on hydrothermal alteration zones and then trained by mineral spectral features related to typical porphyry copper deposits. In the NN set-up used in this study, one hidden layer was used, with the number of neurons equal to the number of features in the input layer. Conjugate gradient backpropagation was employed as the network training function. Then, the efficiency of feature extraction methods was compared through their classification accuracies. According to the results, although the highest classification accuracy for the PCA method occurs in lower numbers of extracted features compared to wavelet transform, the wavelet transform outperforms the PCA, based on confusion matrix classification. Moreover, NN is stronger than SVM and SAM in discriminating favourable alteration zones associated with porphyry copper mineralization using hyperspectral images.

Airborne hyperspectral mapping of trees in an urban area

ABSTRACTThe management of trees in urban areas requires accurate maps, which are difficult to build in the dense patchwork of numerous material properties. Remote sensing is a useful technique that measures the response of all vegetation occurrences, including trees, when high spatial resolution is available. The continuous narrow spectral bands of hyperspectral images enable the detection of the oxygen and water content, which ensures a perfect correction of the atmospheric effect. When calibrated, sunlight reflectance images can be used to map surface chemical compositions by the detection of diagnostic and sharp absorption features. In the visible and near-infrared, the vegetation is detected by chlorophyll-a absorption features that are characteristic of the pigment content. The reflectance intensities due to the texture of leaves occur between 450 and 920 nm while the water content imprint is detectable beyond 920 nm. The sharp spectral feature intensities of the main associated pigments, not only chlorophylls, are well quantified by indices measuring a normalized difference of reflectance in a spectral interval between two bounding wavelengths. A regression line calculated on all bands within that interval ensures a low sensitivity of the indices to the smaller variations in reflectance intensity. Such unbiased indices may be combined, using successive index thresholds deduced from a training spectral library, to divide the spectra into subsets, minimizing the confusion between the numerous vegetation types with almost identical compositions. Therefore, for each subset of the spectra, a classic spectral angle mapping (SAM) method can be used on the corresponding sub-selection of the spectral library to measure angles at full spectral resolution and map tree types with great accuracy, grouped according to their spectral similarity. In this study, chemical and physical information is carefully separated. The tree crown physical properties are studied by comparing the local juxtaposition of pixel sets to a characteristic texture identifiable by image segmentation into objects. Instead of looking for objects in the reflectance image or any statistical compression of its information, a 25 channel co-image, built from 11 information layers of chemical sharp spectral feature indices and 14 information layers of SAM indices matching a spectral library of reference vegetation groups, was used. Tree canopies also present wide internal variations due to (i) a complex mixture with a background in the case of sparse foliage, or (ii) pigment content adaptation to light exposure intensity from one side to another. Both effects are minimized by using the mean spectrum of each object, assuming that less significant spectra, being at plus or minus one or two standard deviations from an object mean spectrum, would be less affected by anomalous pixel data. Thus, two overlapping hierarchic layers at the pixel scale and the object scale are available to describe the main chemistry or pigment content that identifies the vegetation types. The final classification is given by the upper layer at the object scale but in such an organization, the pixel scale layers can be used to analyse the data further and reorder them to obtain other parameters potentially useful for management purposes.

Multiple Kernel Learning Based on Discriminative Kernel Clustering for Hyperspectral Band Selection

In hyperspectral images, band selection plays a crucial role for land-cover classification. Multiple kernel learning (MKL) is a popular feature selection method by selecting the relevant features and classifying the images simultaneously. Unfortunately, a large number of spectral bands in hyperspectral images result in excessive kernels, which limit the application of MKL. To address this problem, a novel MKL method based on discriminative kernel clustering (DKC) is proposed. In the proposed method, a discriminative kernel alignment (KA) (DKA) is defined. Traditional KA measures kernel similarity independently of the current classification task. Compared with KA, DKA measures the similarity of discriminative information by introducing the comparison of intraclass and interclass similarities. It can evaluate both kernel redundancy and kernel synergy for classification. Then, DKA-based affinity-propagation clustering is devised to reduce the kernel scale and retain the kernels having high discrimination and low redundancy for classification. Additionally, an analysis of necessity for DKC in hyperspectral band selection is provided by empirical Rademacher complexity. Experimental results on several hyperspectral images demonstrate the effectiveness of the proposed band selection method in terms of classification performance and computation efficiency.

Mutual-Information-Based Semi-Supervised Hyperspectral Band Selection With High Discrimination, High Information, and Low Redundancy

The large number of spectral bands in hyperspectral images provides abundant information to distinguish different land covers. However, these spectral bands have much redundancy and bring an extra computational burden. Thus, band selection is important for hyperspectral images. Since the labeled samples are difficult to obtain, a semi-supervised criterion based on maximum discrimination and information (MDI) is defined by using both limited labeled samples and sufficient unlabeled samples. This MDI criterion aims to select the most highly discriminative and informative bands, but it is hard to accurately calculate. Therefore, a novel criterion based on high discrimination, high information, and low redundancy (DIR) is proposed as its low-order approximation. Moreover, from an information theory perspective, a theoretical proof is given that many traditional semi-supervised feature selection criteria are the low-order approximations of this MDI criterion. Compared with them, the proposed criterion needs more relaxed approximation conditions. To search and optimize the proposed criterion, a novel clonal selection algorithm is proposed, where the adaptive clone and mutation operators are devised to speed up the convergence. Experimental results on hyperspectral images demonstrate the effectiveness of the proposed semi-supervised band selection method.

An Optimal HSI Image Compression using DWT and CP

The compression of hyperspectral images (HSIs) has recently become a very attractive issue for remote sensing applications because of their volumetric data. An efficient method for hyperspectral image compression is presented. The proposed algorithm, based on Discrete Wavelet Transform and CANDECOM/PARAFAC (DWT-CP), exploits both the spectral and the spatial information in the images. The core idea behind our proposed technique is to apply CP on the DWT coefficients of spectral bands of HSIs. We use DWT to effectively separate HSIs into different sub-images and CP to efficiently compact the energy of sub-images. We evaluate the effect of the proposed method on real HSIs and also compare the results with the well-known compression methods. The obtained results show a better performance when comparing with the existing method PCA with JPEG 2000 and 3D SPECK. DOI: http://dx.doi.org/10.11591/ijece.v4i3.6326

Feature weighting algorithms for classification of hyperspectral images using a support vector machine.

The support vector machine (SVM) is a widely used approach for high-dimensional data classification. Traditionally, SVMs use features from the spectral bands of hyperspectral images with each feature contributing equally to the classification. In practical applications, although affected by noise, slight contributions can also be obtained from deteriorated bands. Thus, compared with feature reduction or equal assignment of weights to all the features, feature weighting is a trade-off choice. In this study, we examined two approaches to assigning weights to SVM features to increase the overall classification accuracy: (1)"CSC-SVM" refers to a support vector machine with compactness and a separation coefficient feature weighting algorithm, and (2)"SE-SVM" refers to a support vector machine with a similarity entropy feature weighting algorithm. Analyses were conducted on a public data set with nine selected land-cover classes. In comparison with traditional SVMs and other classical feature weighting algorithms, the proposed weighting algorithms increase the overall classification accuracy, and even better results could be obtained with few training samples.

Evaluating the Potential of Clonal Selection Optimization Algorithm to Hyperspectral Image Feature Selection

The great number of captured near spectral bands in hyperspectral images causes the curse of dimensionality problem and results in low classification accuracy. The feature selection algorithms try to overcome this problem by limiting the input space dimensions of classification for hyperspectral images. In this paper, immune clonal selection optimization algorithm is used for feature selection. Also one of the fastest Artificial Immune classification algorithms is used to compute fitness function of the feature selection. The comparison of the feature selection results with genetic algorithm shows the clonal selection’s higher performance to solve selection of features.

Evaluating the Potential of Clonal Selection Optimization Algorithm to Hyperspectral Image Feature Selection

The great number of captured near spectral bands in hyperspectral images causes the curse of dimensionality problem and results in low classification accuracy. The feature selection algorithms try to overcome this problem by limiting the input space dimensions of classification for hyperspectral images. In this paper, immune clonal selection optimization algorithm is used for feature selection. Also one of the fastest Artificial Immune classification algorithms is used to compute fitness function of the feature selection. The comparison of the feature selection results with genetic algorithm shows the clonal selection’s higher performance to solve selection of features.

Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations

A high spatial resolution (0.5–1.0 mm) hyperspectral imaging system is presented as a tool for selecting better multispectral methods to detect defective and contaminated foods and agricultural products. Examples of direct linear or non-linear analysis of the spectral bands of hyperspectral images that resulted in more efficient multispectral imaging techniques are given. Various image analysis methods for the detection of defects and/or contaminations on the surfaces of Red Delicious, Golden Delicious, Gala, and Fuji apples are compared. Surface defects/contaminations studied include side rots, bruises, flyspecks, scabs and molds, fungal diseases (such as black pox), and soil contaminations. Differences in spectral responses within the 430–900 nm spectral range are analyzed using monochromatic images and second difference analysis methods for sorting wholesome and contaminated apples. An asymmetric second difference method using a chlorophyll absorption waveband at 685 nm and two bands in the near-infrared region is shown to provide excellent detection of the defective/contaminated portions of apples, independent of the apple color and cultivar. Simple and requiring less computation than other methods such as principal component analysis, the asymmetric second difference method can be easily implemented as a multispectral imaging technique.