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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]

Remote sensing image super-resolution (SR) aims to boost the image resolution while recovering rich high-frequency details. Currently, most of the SR methods are based on an assumption that the degradation kernel is a specific downsampler. However, the degradation kernel is unknown and sophisticated for real remote sensing scenes, leading to a severe performance drop. To alleviate this problem, we propose a multilayer degradation representation-guided blind SR method for remote sensing images, which mainly consists of three key steps. First, an unsupervised representation learning is exploited to learn the degradation representation from low-resolution images. Then, a degradation-guided deep residual module is designed to model high-order features across different scales from the original images. Finally, a multilayer degradation-aware feature fusion mechanism is proposed to restore the finer details. Experiments on synthetic and real datasets demonstrate that the proposed method can achieve promising performance with respect to other state-of-the-art SR approaches. [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 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]

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]