ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection

Authors

Mubashir Noman, Mohamed Bin Zayed University of Artificial IntelligenceFollow
Mustansar Fiaz, IBM Research
Hisham Cholakkal, Mohamed Bin Zayed University of Artificial IntelligenceFollow
Salman Khan, Mohamed Bin Zayed University of Artificial IntelligenceFollow
Fahad Shahbaz Khan, Mohamed Bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

IEEE Transactions on Geoscience and Remote Sensing

Abstract

Deep learning has shown remarkable success in remote sensing change detection (CD), aiming to identify semantic change regions between co-registered satellite image pairs acquired at distinct time stamps. However, existing convolutional neural network (CNN) and transformer-based frameworks often struggle to accurately segment semantic change regions. Moreover, transformers-based methods with standard self-attention suffer from quadratic computational complexity with respect to the image resolution, making them less practical for CD tasks with limited training data. To address these issues, we propose an efficient change detection (CD) framework, efficient global and local context aggregation network (ELGC-Net), which leverages rich contextual information to precisely estimate change regions while reducing the model size. Our ELGC-Net comprises a Siamese encoder, fusion modules, and a decoder. The focus of our design is the introduction of an efficient local-global context aggregator (ELGCA) module within the encoder, capturing enhanced global context and local spatial information through a novel pooled-transpose (PT) attention and depthwise convolution, respectively. The PT attention employs pooling operations for robust feature extraction and minimizes computational cost with transposed attention. Extensive experiments on three challenging CD datasets demonstrate that ELGC-Net outperforms existing methods. Compared to the recent transformer-based CD approach (ChangeFormer), ELGC-Net achieves a 1.4% gain in intersection over union (IoU) metric on the LEVIR-CD dataset, while significantly reducing trainable parameters. Our proposed ELGC-Net sets a new state-of-the-art (SOTA) performance in remote sensing CD benchmarks. Finally, we also introduce ELGC-Net-LW, a lighter variant with significantly reduced computational complexity, suitable for resource-constrained settings, while achieving comparable performance.

First Page

1

Last Page

11

DOI

10.1109/TGRS.2024.3362914

Publication Date

1-1-2024

Keywords

Change detection (CD), local and global context aggregation, remote sensing, transformers

Recommended Citation

M. Noman et al., "ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection," IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1 - 11, Jan 2024.

The definitive version is available at https://doi.org/10.1109/TGRS.2024.3362914