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Spectral unmixing is an important technique to exploit mineral distribution through remote sensing image. In this paper, we propose an unmixing algorithm combining clustering-aware method with the sparsity-constrained nonnegative matrix factorization (SNMF) algorithm. Pixels with similar spectra have high possibility to share similar typical endmembers, therefore we preprocess the image using K-means...
Herein, we explore both a new supervised and unsupervised technique for dimensionality reduction or multispectral sensor design via band group selection in hyperspectral imaging. Specifically, we investigate two algorithms, one based on the improved visual assessment of clustering tendency (iVAT) and the other based on the automatic extraction of “blocklike” structure in a dissimilarity matrix (CLODD...
In this article, a clustering-based band selection method is proposed to tackle the dimension reduction problem of hyperspectral data. The method is essentially based on low-rank doubly stochastic matrix decomposition, which is more stable than current low-rank approximation clustering methods. Experimental results show that the selected band subsets perform well in hyperspectral data classification...
In hyperspectral image processing technologies, anomaly detection is a valuable and practical way of searching small unknown targets based on spectral characteristics. For the lack of prior knowledge of targets, background modeling on hyperspectral images is the key process that affects the outcome of anomaly detection operator. In this paper, a novel method of anomaly detection based on quadratic...
Unsupervised classification plays a key role in remote sensing hyperspectral image analysis. Complexities arise from the high dimensionality of hyperspectral imagery and this implies the need for dimensionality reduction as a vital preprocessing step. However, conventional dimensionality reduction techniques, such as linear and nonlinear manifold learning approaches, may fail if the hyperspectral...
Band selection is an effective approach to mitigate the “Hughes phenomenon” of hyperspectral image (HSI) classification. In this paper, a novel squaring weighted low-rank subspace clustering band selection (SWLRSC) algorithm is proposed for hyperspectral imagery. The SWLRSC method can effectively capture the global structure information of the HSI band set by constructing a strongly connected adjacency...
Salient object detection in hyperspectral imagery has drawn people's attention in recent years. Some detection methods which focus on extending Itti's visual saliency model into spectral domain have been proposed. However, these methods are sensitive to high-contrast edges and cannot preserve boundary of salient object well. To address these shortcomings, we propose a region-based spectral gradient...
LE-based methods have been shown to be effective for target detection in HSI. However, they can be slow due to the costly graph construction and eigenvector computation steps. In this paper, we proposed including a step of pre-segmenting an HSI into superpixels prior to dimensionality reduction. Carrying out experiments on an HIS from the SHARE 2012 data campaign, we show that incorporating superpixels...
In this communication, we propose a new unsupervised clustering method, which uses a kNN graph to propagate labels, starting from high density regions of the representation space. A feature of this method is the fact that it only requires setting the number of neighbors of each object, a problem which can be addressed easily thanks to the clustering stability of the proposed approach. A multiresolution...
Integration of spatial context and spectral features of neighborhood pixels in preprocessing modules prior endmember (EM) extraction algorithms has been recently studied in hyperspectral images processing as a result of their capability in enhancing EMs signatures recognition and computational performance. In this paper, we propose an autonomous preprocessing module using incorporation of a novel...
Anomaly detection has been known to be a challenging, ill-posed problem due to the uncertainty of anomaly and the interference of noise. In this paper, we propose a novel low rank anomaly detection algorithm in hyperspectral images (HSI), where three components are involved. First, due to the highly mixed nature of pixels in HSI, instead of using the raw pixel directly for anomaly detection, the proposed...
Active learning usually is conducted in an iterative way. In the paper, a Computational Efficiency Active Learning (CEAL) algorithm is proposed to address this problem based on diversity measurement for classification of hyperspectral images. In particular, each unlabeled sample is pre-assigned a group label, which can be carried out by such as a clustering algorithm. After that, candidate patterns...
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