SCA-Net: Multiscale Contextual Information Network for Building Extraction Based on High-Resolution Remote Sensing Images

Author:

Wang Yuanzhi123,Zhao Qingzhan123,Wu Yuzhen123,Tian Wenzhong124,Zhang Guoshun123

Affiliation:

1. College of Information Science and Technology, Shihezi University, Shihezi 832002, China

2. Geospatial Information Engineering Research Center, Xinjiang Production and Construction Crops, Shihezi 832002, China

3. Xinjiang Production and Construction Corps Industrial Technology Research Institute, Shihezi 832002, China

4. College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832002, China

Abstract

Accurately extracting buildings is essential for urbanization rate statistics, urban planning, resource allocation, etc. The high-resolution remote sensing images contain rich building information, which provides an important data source for building extraction. However, the extreme abundance of building types with large differences in size, as well as the extreme complexity of the background environment, result in the accurate extraction of spatial details of multi-scale buildings, which remains a difficult problem worth studying. To this end, this study selects the representative Xinjiang Tumxuk urban area as the study area. A building extraction network (SCA-Net) with feature highlighting, multi-scale sensing, and multi-level feature fusion is proposed, which includes Selective kernel spatial Feature Extraction (SFE), Contextual Information Aggregation (CIA), and Attentional Feature Fusion (AFF) modules. First, Selective kernel spatial Feature Extraction modules are used for cascading composition, highlighting information representation of features, and improving the feature extraction capability. Adding a Contextual Information Aggregation module enables the acquisition of multi-scale contextual information. The Attentional Feature Fusion module bridges the semantic gap between high-level and low-level features to achieve effective fusion between cross-level features. The classical U-Net, Segnet, Deeplab v3+, and HRNet v2 semantic segmentation models are compared on the self-built Tmsk and WHU building datasets. The experimental results show that the algorithm proposed in this paper can effectively extract multi-scale buildings in complex backgrounds with IoUs of 85.98% and 89.90% on the two datasets, respectively. SCA-Net is a suitable method for building extraction from high-resolution remote sensing images with good usability and generalization.

Funder

National Natural Science Foundation of China

Xinjiang Production and Construction Corps Key Field Science and Technology Tackling Program Project

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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