Multi-Scale Context Aggregation for Semantic Segmentation of Remote Sensing Images

Remote Sensing

Shaofu Lin¹  Jing Zhang¹  Lei Ding²  Lorenzo Bruzzone² 

[1] Faculty of Information Technology, Beijing University of Technology, Chaoyang District, Beijing 100022, China;Remote Sensing Laboratory, Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 5, Trento 38122, Italy;

关键词: semantic segmentation;    convolutional neural network;    deep learning;    image analysis;    remote sensing;   

DOI  :  10.3390/rs12040701

来源: DOAJ

【 摘 要 】

The semantic segmentation of remote sensing images (RSIs) is important in a variety of applications. Conventional encoder-decoder-based convolutional neural networks (CNNs) use cascade pooling operations to aggregate the semantic information, which results in a loss of localization accuracy and in the preservation of spatial details. To overcome these limitations, we introduce the use of the high-resolution network (HRNet) to produce high-resolution features without the decoding stage. Moreover, we enhance the low-to-high features extracted from different branches separately to strengthen the embedding of scale-related contextual information. The low-resolution features contain more semantic information and have a small spatial size; thus, they are utilized to model the long-term spatial correlations. The high-resolution branches are enhanced by introducing an adaptive spatial pooling (ASP) module to aggregate more local contexts. By combining these context aggregation designs across different levels, the resulting architecture is capable of exploiting spatial context at both global and local levels. The experimental results obtained on two RSI datasets show that our approach significantly improves the accuracy with respect to the commonly used CNNs and achieves state-of-the-art performance.

【 授权许可】

Unknown