SCAttNet: Semantic Segmentation Network with Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images (1912.09121v2)

Abstract: High-resolution remote sensing images (HRRSIs) contain substantial ground object information, such as texture, shape, and spatial location. Semantic segmentation, which is an important task for element extraction, has been widely used in processing mass HRRSIs. However, HRRSIs often exhibit large intraclass variance and small interclass variance due to the diversity and complexity of ground objects, thereby bringing great challenges to a semantic segmentation task. In this paper, we propose a new end-to-end semantic segmentation network, which integrates lightweight spatial and channel attention modules that can refine features adaptively. We compare our method with several classic methods on the ISPRS Vaihingen and Potsdam datasets. Experimental results show that our method can achieve better semantic segmentation results. The source codes are available at https://github.com/lehaifeng/SCAttNet.

• The paper introduces SCAttNet, a novel semantic segmentation network for high-resolution remote sensing images that integrates spatial and channel attention mechanisms to enhance feature representation.
• Built on SegNet and ResNet50 backbones, SCAttNet V1 and V2 demonstrate superior MIoU scores (e.g., V2 achieved 70.20% on Vaihingen) compared to other methods, especially improving segmentation of small objects.
• SCAttNet's effective integration of attention mechanisms offers practical applications in urban planning, environmental monitoring, and military reconnaissance by improving segmentation accuracy without excessive computational cost.

Semantic Segmentation Network with Attention Mechanism for High-Resolution Remote Sensing Images

The paper, "SCAttNet: Semantic Segmentation Network with Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images," introduces a novel approach to semantic segmentation tailored for high-resolution remote sensing images (HRRSIs). These images are characterized by their complexity, manifesting large intraclass variances and small interclass variances, making segmentation challenging. The authors propose a new neural network architecture, SCAttNet, which incorporates both spatial and channel attention mechanisms to enhance feature representation and improve segmentation accuracy.

Remote sensing images differ significantly from typical natural images in their imaging perspective and context complexity. While deep learning methods have shown success in natural image segmentation, their direct application to HRRSIs is suboptimal due to these differences. To address these challenges, SCAttNet employs an integrated attention mechanism that selectively focuses on the most informative parts of the image, both in terms of spatial arrangement and channel significance.

Key Contributions and Methodology

1. Attention Mechanisms:
   • The paper proposes a spatial attention module and a channel attention module, designed to refine feature maps by focusing on salient spatial and channel elements, respectively. The spatial attention selectively emphasizes important spatial features, which is crucial for handling diverse object sizes in HRRSIs. The channel attention mechanism, on the other hand, enhances semantically meaningful features by weighing the contribution of each channel.
2. Network Architecture:
   • SCAttNet is built on two backbone architectures: SegNet and ResNet50, referred to as SCAttNet V1 and SCAttNet V2, respectively. These backbones facilitate robust feature extraction, upon which the attention modules operate to refine the feature maps. The integration of the attention modules is positioned at the end layer of these backbones to optimize computational efficiency without significantly adding to the network's parameter burden.
3. Experimental Evaluation:
   • The experiments are conducted on two benchmark datasets, ISPRS Vaihingen and Potsdam, demonstrating improvements over several competing methods. The performance improvements are particularly notable in the segmentation of small objects, where traditional methods tend to falter. The proposed models showed superior MIoU scores of 66.96% and 70.20% on the Vaihingen dataset and 61.26% and 68.31% for the Potsdam dataset, for SCAttNet V1 and V2 respectively.
4. Visualization for Interpretability:
   • The authors provide visualization analyses to demonstrate how attention mechanisms refine feature representation, enhancing the network’s focus on pertinent regions of an image. This aids in understanding the contribution of attention modules to segmentation improvements.

Practical and Theoretical Implications

Practically, SCAttNet holds promise in several remote sensing applications, including urban planning, environmental monitoring, and military reconnaissance, where accurate and efficient image segmentation is critical. Theoretically, the integration of lightweight attention modules without excessive computational overhead opens avenues for similar enhancements in other domains of computer vision. Future exploration may involve optimizing these attention mechanisms further and adapting them to a broader range of data types within Earth observation and beyond.

This paper exemplifies the effective application of attention mechanisms in semantically segmenting HRRSIs, paving the way for future developments that leverage these mechanisms to optimize and solve complicated segmentation challenges.