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.