Building change detection of remote sensing image based on improved DeepLabV3+

doi:10.13474/j.cnki.11-2246.2023.0119

Abstract

Abstract: As an important task in the field of remote sensing, change detection plays an important role in the scenes of law enforcement inspection of satellite land image, farmland conversion. In recent years, relevant practitioners have used AI related technologies to solve the task of change detection. The common technical solution is to concatenate two images by channel dimension and use semantic segmentation algorithm to predict the change area. This paper uses the change detection dataset LEVIR-CD as experimental data,based on DeepLabV3+algorithm, improves the model structure according to the characteristics of change detection task. The siamese network uses DeepLabV3+as the backbone. This paper uses multi-level feature interaction to fully fuse image features. The results show that the improved network structure is more suitable for change detection task.

Key words: change detection, deep learning, convolutional neural network, sematic segmentation

CLC Number:

P237

QI Jianwei, WANG Weifeng, ZHANG Le, WANG Guangyan. Building change detection of remote sensing image based on improved DeepLabV3+[J]. Bulletin of Surveying and Mapping, 2023, 0(4): 145-149.

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