Threshold Attention Network for Semantic Segmentation of Remote Sensing Images

TL;DR

This paper introduces TANet, a novel semantic segmentation network for remote sensing images that employs a threshold attention mechanism to reduce computational complexity and improve feature modeling, demonstrating superior performance on benchmark datasets.

Contribution

The paper proposes a threshold attention mechanism (TAM) and a TANet architecture that enhances segmentation accuracy while reducing computational costs compared to existing self-attention methods.

Findings

TANet outperforms state-of-the-art models on ISPRS Vaihingen and Potsdam datasets.

The threshold attention mechanism reduces computational complexity significantly.

TANet effectively models long-range dependencies with less redundancy.

Abstract

Semantic segmentation of remote sensing images is essential for various applications, including vegetation monitoring, disaster management, and urban planning. Previous studies have demonstrated that the self-attention mechanism (SA) is an effective approach for designing segmentation networks that can capture long-range pixel dependencies. SA enables the network to model the global dependencies between the input features, resulting in improved segmentation outcomes. However, the high density of attentional feature maps used in this mechanism causes exponential increases in computational complexity. Additionally, it introduces redundant information that negatively impacts the feature representation. Inspired by traditional threshold segmentation algorithms, we propose a novel threshold attention mechanism (TAM). This mechanism significantly reduces computational effort while also better modeling the correlation between different regions of the feature map. Based on TAM, we present a threshold attention network (TANet) for semantic segmentation. TANet consists of an attentional feature enhancement module (AFEM) for global feature enhancement of shallow features and a threshold attention pyramid pooling module (TAPP) for acquiring feature information at different scales for deep features. We have conducted extensive experiments on the ISPRS Vaihingen and Potsdam datasets. The results demonstrate the validity and superiority of our proposed TANet compared to the most state-of-the-art models.