A change detection method by combining spectral-object-temporal features for remote sensing imagery

doi:10.7523/j.ucas.2020.0005

Abstract

Abstract: According to the fact that traditional change detection methods are difficult to meet the needs of practical applications in the era of big data of remote sensing, this paper proposes a method by combining spectral-object-temporal features to solve this problem. On the basis of extracting various features of remote sensing images, the Bi-LSTM network is used to extract the joint spectrum-temporal-object feature to obtain the effective information of changes in bi-temporal images. Experimental results based on bi-temporal medium-resolution remote sensing images show that the overall accuracy of this method is greater than 0.9, and the Kappa coefficient reaches 0.84. Compared to traditional methods, the proposed method effectively improves the accuracy and degree of automation of change detection.

Key words: change detection, spectral-object-temporal feature, Bi-LSTM

CLC Number:

TP79

CAO Zhou, LIU Shibin, MA Yong, YAO Wutao, JIANG Liyuan. A change detection method by combining spectral-object-temporal features for remote sensing imagery[J]. Journal of University of Chinese Academy of Sciences, 2022, 39(1): 102-109.

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