Deep Spectral Clustering With Regularized Linear Embedding for Hyperspectral Image Clustering
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2023.3267070
Abstract: The past decade has witnessed the rapid development of deep learning techniques, especially for large-scale and complex datasets. However, it is still a noteworthy problem in dealing with unsupervised hyperspectral image (HSI) segmentation since inefficiency… read more here.
Keywords: linear embedding; deep spectral; regularized linear; spectral clustering ... See more keywords
Deep Spectral Clustering With Constrained Laplacian Rank.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3213756
Abstract: Spectral clustering (SC) is a well-performed and prevalent technique for data processing and analysis, which has attracted significant attention in the field of clustering. While the scalability and generalization ability of this method make it… read more here.
Keywords: deep spectral; laplacian rank; constrained laplacian; spectral clustering ... See more keywords
Hyperspectral image secure retrieval based on encrypted deep spectral–spatial features
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Applied Remote Sensing"
DOI: 10.1117/1.jrs.13.018501
Abstract: Abstract. With the rapid development of remote-sensing earth observation technology, hyperspectral imagery has shown exponential growth. The quick and accurate retrieval of hyperspectral images has become a practical challenge in applications. Moreover, open network sharing… read more here.
Keywords: deep spectral; retrieval; hyperspectral image; spatial features ... See more keywords