Spatial-Aware Transformer (SAT): Enhancing Global Modeling in Transformer Segmentation for Remote Sensing Images

Author:

Wang Duolin12,Chen Yadang12ORCID,Naz Bushra3,Sun Le124ORCID,Li Baozhu5

Affiliation:

1. School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China

2. Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China

3. Department of Computer Systems Engineering, Mehran University of Engineering and Technology, Jamshoro 76062, Pakistan

4. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China

5. Internet of Things & Smart City Innovation Platform, Zhuhai Fudan Innovation Institute, Zhuhai 519031, China

Abstract

In this research, we present the Spatial-Aware Transformer (SAT), an enhanced implementation of the Swin Transformer module, purposed to augment the global modeling capabilities of existing transformer segmentation mechanisms within remote sensing. The current landscape of transformer segmentation techniques is encumbered by an inability to effectively model global dependencies, a deficiency that is especially pronounced in the context of occluded objects. Our innovative solution embeds spatial information into the Swin Transformer block, facilitating the creation of pixel-level correlations, and thereby significantly elevating the feature representation potency for occluded subjects. We have incorporated a boundary-aware module into our decoder to mitigate the commonly encountered shortcoming of inaccurate boundary segmentation. This component serves as an innovative refinement instrument, fortifying the precision of boundary demarcation. After these strategic enhancements, the Spatial-Aware Transformer achieved state-of-the-art performance benchmarks on the Potsdam, Vaihingen, and Aerial datasets, demonstrating its superior capabilities in recognizing occluded objects and distinguishing unique features, even under challenging conditions. This investigation constitutes a significant advancement toward optimizing transformer segmentation algorithms in remote sensing, opening a wealth of opportunities for future research and development.

Funder

National Natural Science Foundation of China

Shandong Provincial Natural Science Foundation

China Postdoctoral Science Foundation

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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