  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 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 framework, 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 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 metric on the LEVIR-CD dataset, while significantly
reducing trainable parameters. Our proposed ELGC-Net sets a new
state-of-the-art performance in remote sensing change detection 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. Project url
https://github.com/techmn/elgcnet.
