Annual change detection of land use status based on siamese neural network

doi:10.13474/j.cnki.11-2246.2021.0084

Abstract

Abstract: In recent years, multi-phase high-resolution remote sensing images are used to build an annual change detection model of land use status and support the intelligent development of annual change detection of land use status on this basis, which is exactly the difficulty of current research. This paper discusses the construction of land use status quo of annual change detection model. Through the land information and data preprocessing, change detection based on twin neural network model being constructed, and the result of the model output optimizing GIS in to obtain the variation spot target area achieves rapid extraction according to current situation of land use change area. The experimental results show that this method can quickly find the change locations of land use status in the images in different periods, which effectively improve the intelligent level of annual change detection of land use status, and can serve the daily annual change detection of land use status.

Key words: annual change detection of land use status, natural resources survey and monitoring, image change detection, Siamese network, high-resolution remote sensing image

CLC Number:

P237

ZHANG Weiliang, LIU Qi, WU Changbin, YANG Guangdi. Annual change detection of land use status based on siamese neural network[J]. Bulletin of Surveying and Mapping, 2021, 0(3): 91-95,104.

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