Cross-Scale Feature Propagation Network for Semantic Segmentation of High-Resolution Remote Sensing Images
Qiaolin Zeng, Jingxiang Zhou, Xuerui Niu
Abstract
Over the past few years, various strategies have been proposed to improve the multi-scale information capture capability of networks, such as encoder-decoder framework, convolution layers with different kernel sizes in parallel and multiple branches framework. However, many methods only rely on one of the strategies, which limits their performance when processing remote sensing images with large scale variance. To address this issue and enable the fast and effective extraction of multi-scale semantic information, this manuscript introduces a novel cross-scale feature propagation network (CFPNet). Specifically, the multi-scale convolution (MSC) module aims to capture fine-grained multi-scale context with different receptive fields, and the attention up-sample (AUS) module embeds the semantic information of high-level features into low-level features while maintaining spatial details. Besides, the feature semantic enhancement (FSE) module is proposed to aggregate the multi-layer features of the decoder to enhance the final feature representation. The experimental results on the BLU and GID datasets demonstrate the effectiveness and efficiency of our CFPNet.