激光与光电子学进展, 2020, 57 (4): 041012, 网络出版: 2020-02-20
基于多级通道注意力的遥感图像分割方法 下载: 1549次
Remote Sensing Image Segmentation Method Based on Multi-Level Channel Attention
图像处理 神经网络 遥感图像分割 注意力机制 多尺度特征融合 image processing neural network remote sensing image segmentation attention mechanism multi-scale feature fusion
摘要
针对深度卷积网络在遥感图像上存在小目标漏分、被遮挡目标无法提取、细节缺失等问题,在深度卷积编码-解码网络的基础上提出一种基于多级通道注意力的遥感图像分割方法(SISM-MLCA)。首先在网络编码阶段加入通道注意力机制,通过自我学习的方式获取更为有效的特征,解决遥感图像中目标遮挡问题;其次,在不同尺度上施加通道注意力的特征图融合,使网络提取到丰富的上下文信息,能应对目标尺度的变化,改善小目标难分割的问题。在两个数据集实验上的结果表明:SISM-MLCA具有更高的目标分割准确性,对小目标与被遮挡目标能取得更好的分割结果;在训练数据有限、背景复杂多样、尺度变化较大的遥感图像目标分割中取得了较好的结果,表明SISM-MLCA可应用于复杂的遥感图像目标分割中。
Abstract
To solve problems of leak classification of small targets, unable to extract occluded targets, and missing details of remote sensing image existing in deep convolution networks, a remote sensing image segmentation method based on multi-level channel attention (SISM-MLCA) is proposed. This deep convolution coding-decoding network-based method initially adds the channel attention mechanism in the network coding stage and obtains more effective features through self-learning to solve the problem of target occlusion in remote sensing images. Next, feature map fusion of channel attention is applied at different scales to extract abundant context information and deal with target scale changes. This solves the problem of small target segmentation and improves the performance of segmentation. In this study, experiments conducted on two datasets demonstrate that SISM-MLCA has high accuracy for target segmentation and good segmentation results for small and occluded targets. Good results are achieved in target segmentation of remote sensing images with limited training data, complex and diverse backgrounds, and large-scale changes. These results demonstrate that SISM-MLCA is applicable to the target segmentation of complex remote sensing images.
余帅, 汪西莉. 基于多级通道注意力的遥感图像分割方法[J]. 激光与光电子学进展, 2020, 57(4): 041012. Shuai Yu, Xili Wang. Remote Sensing Image Segmentation Method Based on Multi-Level Channel Attention[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041012.