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Hyperspectral change detection (HCD) is widely applied in various domains, such as accurate agriculture, disaster assessment, land use, and environmental monitoring. Most HCD methods aim at extracting and classifying the spectral variation features with dimension reduction and machine-learning methods. Different from previous work, this letter proposes an edge-guided HCD method. Specifically, a subtraction operation is adopted to extract difference hyperspectral image (HSI). Then, the edge-preserving filtering is performed on the difference HSI to extract spectral–spatial features. Next, the number of the extracted features is diminished through the kernel principal component analysis (PCA). Finally, the fused features are input into a spectral classifier followed by the edge-preserving filtering to obtain the final change detection result. Experiments on several HCD datasets demonstrate that the proposed method can consistently outperform other advanced approaches in both subjective and objective evaluations when only a limited number of labeled samples are available. [ABSTRACT FROM AUTHOR]

Coastal wetland monitoring plays an important role in the protection and restoration of ecosystems in this world. UAV-hyperspectral imaging, as an emerging technique for Earth observation and space exploration, provides the huge potential ability to identify different wetland species. In this work, a multilayer global spectral–spatial attention network (MGSSAN) is proposed for mapping coastal wetlands, which mainly consists of two major steps. First, a two-branch convolutional neural network (CNN) framework with residual connection is developed to obtain an initial classification probability map, in which one branch is used to capture the spectral information, the other branch is used to extract spatial information, and a global spectral–spatial attention module is designed to guide networks focusing on those features that are more discriminative. Second, an extended random walker method is utilized to optimize the initial classification probabilities, so as to yield the final map. Experiments performed on three wetland HSI datasets created by ourselves verify that the proposed method can obtain superior performance with respect to several state-of-the-art hyperspectral image classification methods. [ABSTRACT FROM AUTHOR]

Hyperspectral Image (HSI) visualization, which aims at displaying as much material information of original images as possible on a trichromatic monitor with natural color, plays an important role in image interpretation and analysis. However, most of the HSI visualization methods only focus on presenting the detail information of a scene without providing natural colors and distinguishing land covers with similar colors. In order to address this problem, this article proposes a multichannel pulse-coupled neural network (MPCNN)-based HSI visualization method, which consists of the following steps. First, the MPCNN is proposed and explored to fuse the original HSI so as to obtain a fused band with rich spatial details. Then, a color mapping scheme is proposed to determine the weights of red, green, and blue (RGB) channels. Finally, the weighted RGB channels are stacked together for visualization. Experiments performed on four hyperspectral data sets demonstrate that the proposed method not only displays the HSI with nature colors but also improves the details in the image. The effectiveness of the proposed method is demonstrated in terms of both visual effect and objective indexes. [ABSTRACT FROM AUTHOR]

In this article, a novel hyperspectral anomaly detection method with kernel Isolation Forest (iForest) is proposed. The method is based on an assumption that anomalies rather than background can be more susceptible to isolation in the kernel space. Based on this idea, the proposed method detects anomalies as follows. First, the hyperspectral data are mapped into the kernel space, and the first $K$ principal components are used. Then, the isolation samples in the image are detected with the iForest constructed using randomly selected samples in the principal components. Finally, the initial anomaly detection map is iteratively refined with locally constructed iForest in connected regions with large areas. Experimental results on several real hyperspectral data sets demonstrate that the proposed method outperforms other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this article, a novel hyperspectral image (HSI) classification method based on fusing multiple edge-preserving operations (EPOs) is proposed, which consists of the following steps. First, the edge-preserving features are obtained by performing different types of EPOs, i.e., local edge-preserving filtering and global edge-preserving smoothing on the dimension-reduced HSI. Then, with the assistance of a superpixel segmentation method, the edge-preserving features are further improved by considering the inter and intra spectral properties of superpixels. Finally, the spectral and edge-preserving features are fused to form one composite kernel, which is fed into the support vector machine (SVM) followed by a majority voting fusion scheme. Experimental results on three data sets demonstrate the superiority of the proposed method over several state-of-the-art classification approaches, especially when the training sample size is limited. Furthermore, 21 well-known methods, including mathematical morphology-based approaches, sparse representation models, and deep learning-based classifiers, are adopted to be compared with the proposed method on Houston data set with standard sets of training and test samples released during 2013 Data Fusion Contest, which also shows the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a novel multi-scale total variation method is proposed to extract structural features from hyperspectral images (HSIs), which consists of the following steps. First, the spectral dimension of the HSI is reduced with an averaging-based method. Then, the multi-scale structural features (MSFs), which are insensitive to image noise, are constructed with a relative total variation-based structure extraction technique. Finally, the MSFs are fused together using kernel principal component analysis (KPCA), so as to obtain the KPCA-fused MSFs for classification. Experimental results on three publicly available hyperspectral datasets, including both well-known, long-used data, and a recent dataset obtained from an international contest, demonstrate the competitive performance over several state-of-the-art classification approaches in this field. Moreover, the robustness of the proposed method to the small-sample-size problem and serious image noise is also demonstrated. [ABSTRACT FROM AUTHOR]

Recently, convolutional neural networks (CNNs) as a powerful tool have been introduced for classification of hyperspectral images (HSIs). However, it fails to take the feature redundancy into consideration. Hence, for the pixel-wise HSI classification, the CNN-based methods may not effectively extract the discriminative features from the complex scene in HSIs. In order to overcome this problem, in this letter, a novel dual-path network (DPN)-based HSI classification method is proposed, in which the DPN combines the advantages of the residual network and dense convolutional network. First, the principal component analysis is utilized to extract significant components of HSI. Second, training image patches centered on labeled pixels are constructed to train the DPN. Finally, the labels of test pixels are predicted by using the trained network. Experiments conducted on two hyperspectral data sets demonstrate the state-of-the-art performance of the proposed method over other compared methods in terms of classification accuracies. [ABSTRACT FROM AUTHOR]

In this paper, a novel method is introduced to detect and correct mislabeled training samples for hyperspectral image classification. First, domain transform recursive filtering-based feature extraction is used to improve the separability of the training samples. Then, constrained energy minimization-based object detection is performed on the training set with each training sample serving as the object spectrum. Finally, the label of each training sample is verified or corrected based on the averaged detection probabilities of different classes. Experiments performed on real hyperspectral data sets demonstrate the effectiveness of the proposed method in improving classification performance with respect to the classifier trained with the original training set that contains a number of mislabeled samples. [ABSTRACT FROM AUTHOR]

In this letter, a novel anomaly detection method is proposed, which can effectively fuse the multiscale information extracted by attribute and edge-preserving filters. The proposed method consists of the following steps. First, multiscale attribute and edge-preserving filters are utilized to obtain multiscale anomaly detection maps. Then, the multiscale detection maps are fused via an averaging approach, and the training samples of the anomalies and background are selected from the fused detection map. Next, the support vector machine classification is performed on the hyperspectral image to obtain an anomaly probability map. Finally, the detection result is obtained by multiplying the fused detection map and the anomaly probability map, followed by an edge-preserving filtering-based postprocessing. Experiments performed on four real hyperspectral data sets demonstrate that the proposed method shows a better detection performance with respect to several state-of-the-art hyperspectral anomaly detection methods. [ABSTRACT FROM AUTHOR]

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]