SAR image change detection based on multi-scale fusion convolutional neural network

doi:10.13474/j.cnki.11-2246.2023.0355

Abstract

Abstract: The presence of speckle noise significantly affects the capability of synthetic aperture radar (SAR) image to recognize changing information. In order to enhance the accuracy of change detection, the influence of speckle noise must be adequately addressed. This study introduces a novel approach for change detection. Firstly, a context-aware saliency extraction method is employed to extract potential change regions and background information from the difference image. This process retains the main textural details of the image while removing background noise. A multi-scale channel attention module, the squeeze, expand, and excitation (SEE) module, is designed to address the issue of inadequate feature representation in current change detection methods. This module captures multi-scale information while emphasizing crucial details without introducing information redundancy.Building upon this foundation, a multi-scale fusion convolutional neural network called the squeeze, expand, and excitation network (SEENet) is proposed. SEENet connects three SEE modules through residual connections to achieve multi-level information utilization. Through experimentation on four real SAR datasets, the effectiveness of this method is validated.

Key words: multi-scale, SAR image, change detection, SEENet

CLC Number:

P237

DUAN Yu, LIU Shanwei, WAN Jianhua, MUHAMMAD Yasir, ZHENG Shuang. SAR image change detection based on multi-scale fusion convolutional neural network[J]. Bulletin of Surveying and Mapping, 2023, 0(12): 31-37.

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