Change detection using multi-scale convolutional feature maps of bi-temporal satellite high-resolution images

ABSTRACT

Change detection in high-resolution satellite images is essential to understanding the land surface (e.g. agriculture and urban change) or maritime surface (e.g. oil spilling). Many deep-learning-based change detection methods have been proposed to enhance the performance of the classical techniques. However, the massive amount of satellite images and missing ground-truth images are still challenging concerns. In this paper, we propose a supervised deep network for change detection in bi-temporal remote sensing images. We feed multi-level features from convolutional networks of two images (feature-extraction) into one architecture (feature-difference) to have better shape and texture properties using a dual attention module We also utilize a multi-scale dice coefficient error function to decrease overlapping between changed and background pixel. The network is applied to public datasets (ACD, SYSU-CD and OSCD). We compare the proposed architecture with various attention modules and loss functions to verfiy the performance of the proposed method. We also compare the proposed method with the stateof-the-art methods in terms of three metrics: precision, recall and F1-score. The experimental outcomes confirm that the proposed method has good performance compared to benchmark methods.

KEYWORDS:

• Change detection
• supervised deep network
• dual attention module

Acknowledgment

This material is based upon work supported by Tamkeen under the New York University, Abu Dhabi Research Institute grant G1502.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The dataset supporting this article is available in the http://web.eee.sztaki.hu/remotesensing/airchange_benchmark.html, https://drive.google.com/drive/folders/1ALb8rzw9zEMSxwNTvIrIaA83zjjs04CE and https://rcdaudt.github.io/oscd/.

Additional information

Funding

This work was supported by the New York University Abu Dhabi Institute Grant (ADH01-73-71210-G1502-ADHPG) and Emirati Research Grant (ADH01-76-71202-EMIRP-ADHPG)