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Change detection in multitemporal hyperspectral images (HSI) can be regarded as a classification task, consisting of two steps: change feature extraction and identification. To extract clean change features from heavily corrupted spectral change vectors (SCV) of multitemporal HSI, this paper proposes a novel spectrally-spatially regularized low-rank and sparse decomposition model (LRSDSS). It exploits...
Change detection (CD) for multitemporal hyperspectral images (HSI) consists of two steps, change feature extraction and identification. This paper proposes a novel spectrally-spatially regularized low-rank and sparse decomposition model (LRSD_SS), to extract clean change features from corrupted spectral change vectors (SCV) of multitemporal HSI. It decomposes SCV into spatially smoothed low-rank data,...
Recently, spectral-spatial classification for hyperspectral imagery (HSI) has become popular since it addresses the issues of limited prior knowledge and spectral internal-class variability. To provide simple and effective approaches in this area, we propose a novel supervised spectral-spatial measurement, affinity score (AS). It considers three factors: local spatial consistency, spectral similarity,...
Change detection for multitemporal hyperspectral images (HSIs) involves two major steps: change feature extraction and classification. For the first part, conventional methods mostly consider spectral features but neglect spatial patterns. Since multitemporal HSIs consist of four dimensions (one for time, one for spectral domain and two for spatial domain), we propose using 4-dimensional Higher Order...
It needs both spectral and spatial information to refine classification of hyperspectral images. There is a general spectral-spatial framework to address the issue. It consists of three major steps: classification, segmentation and combination, to which we have made two improvements. First, superpixels generated by over-segmentation are clustered according to superpixel-wise distances as to balance...
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