Building Extraction From Remote Sensing Imagery With a High-Resolution Capsule Network

Abstract

The up-to-date and accurate building database serves as an important prerequisite to many applications. However, caused by the issues of shape and size variations, texture and distribution diversities, and occlusion and shadow covers of buildings in remote sensing images, it is still challenging to well guarantee the integrity and accuracy of the extracted building instances. This letter proposes a high-resolution capsule network (HR-CapsNet) to conduct building extraction. First, designed with an HR-CapsNet architecture assisted by multiresolution feature propagation and fusion, the HR-CapsNet can provide semantically strong and spatially accurate feature representations to promote the pixel-wise building extraction accuracy. In addition, integrated with an efficient capsule feature attention module, the HR-CapsNet can attend to channel-wise informative and class-specific spatial features to boost the feature encoding quality. Quantitative evaluations, visual inspections, and comparative experiments on two large remote sensing image datasets demonstrate that the HR-CapsNet provides a feasible and competitive solution to building extraction tasks.