Remote Sensing (Mar 2022)

A Block Shuffle Network with Superpixel Optimization for Landsat Image Semantic Segmentation

  • Xuan Yang,
  • Zhengchao Chen,
  • Bing Zhang,
  • Baipeng Li,
  • Yongqing Bai,
  • Pan Chen

DOI
https://doi.org/10.3390/rs14061432
Journal volume & issue
Vol. 14, no. 6
p. 1432

Abstract

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In recent years, with the development of deep learning in remotely sensed big data, semantic segmentation has been widely used in large-scale landcover classification. Landsat imagery has the advantages of wide coverage, easy acquisition, and good quality. However, there are two significant challenges for the semantic segmentation of mid-resolution remote sensing images: the insufficient feature extraction capability of deep convolutional neural network (DCNN); low edge contour accuracy. In this paper, we propose a block shuffle module to enhance the feature extraction capability of DCNN, a differentiable superpixel branch to optimize the feature of small objects and the accuracy of edge contours, and a self-boosting method to fuse semantic information and edge contour information to further optimize the fine-grained edge contour. We label three sets of Landsat landcover classification datasets, and achieved an overall accuracy of 86.3%, 83.2%, and 73.4% on the three datasets, respectively. Compared with other mainstream semantic segmentation networks, our proposed block shuffle network achieves state-of-the-art performance, and has good generalization ability.

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