SCA-Net: Spatial-Contextual Aggregation Network for Enhanced Small Building and Road Change Detection

TL;DR

SCA-Net is a novel deep learning architecture that significantly improves small building and road change detection accuracy in remote sensing images while reducing training time, through multi-scale analysis and attention mechanisms.

Contribution

The paper introduces SCA-Net, a new model with innovative modules like Difference Pyramid Block and multi-level attention, enhancing change detection performance and training efficiency.

Findings

Achieves 2.64% higher mIoU on LEVIR-MCI dataset.

Increases IoU for small buildings by 57.9%.

Reduces training time by 61%.

Abstract

Automated change detection in remote sensing imagery is critical for urban management, environmental monitoring, and disaster assessment. While deep learning models have advanced this field, they often struggle with challenges like low sensitivity to small objects and high computational costs. This paper presents SCA-Net, an enhanced architecture built upon the Change-Agent framework for precise building and road change detection in bi-temporal images. Our model incorporates several key innovations: a novel Difference Pyramid Block for multi-scale change analysis, an Adaptive Multi-scale Processing module combining shape-aware and high-resolution enhancement blocks, and multi-level attention mechanisms (PPM and CSAGate) for joint contextual and detail processing. Furthermore, a dynamic composite loss function and a four-phase training strategy are introduced to stabilize training and accelerate convergence. Comprehensive evaluations on the LEVIR-CD and LEVIR-MCI datasets demonstrate SCA-Net's superior performance over Change-Agent and other state-of-the-art methods. Our approach achieves a significant 2.64% improvement in mean Intersection over Union (mIoU) on LEVIR-MCI and a remarkable 57.9% increase in IoU for small buildings, while reducing the training time by 61%. This work provides an efficient, accurate, and robust solution for practical change detection applications.