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DEEP NO LEARNING APPROACH FOR UNSUPERVISED CHANGE DETECTION IN HYPERSPECTRAL IMAGES
2021
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Abstract. Unsupervised deep transfer-learning based change detection (CD) methods require pre-trained feature extractor that can be used to extract semantic features from the target bi-temporal scene. However, it is difficult to obtain such feature extractors for hyperspectral images. Moreover, it is not trivial to reuse the models trained with the multispectral images for the hyperspectral images due to the significant difference in number of spectral bands. While hyperspectral images show
doi:10.5194/isprs-annals-v-3-2021-311-2021
fatcat:bllx2gqeb5hgllh43tmtp6zko4