changestar

ChangeStar

Change is Everywhere Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery

Introduction

Official Repo

Code Snippet

Abstract

For high spatial resolution (HSR) remote sensing images, bitemporal supervised learning always dominates change detection using many pairwise labeled bitemporal images. However, it is very expensive and time-consuming to pairwise label large-scale bitemporal HSR remote sensing images. In this paper, we propose single-temporal supervised learning (STAR) for change detection from a new perspective of exploiting object changes in unpaired images as supervisory signals. STAR enables us to train a high-accuracy change detector only using unpaired labeled images and generalize to real-world bitemporal images. To evaluate the effectiveness of STAR, we design a simple yet effective change detector called ChangeStar, which can reuse any deep semantic segmentation architecture by the ChangeMixin module. The comprehensive experimental results show that ChangeStar outperforms the baseline with a large margin under single-temporal supervision and achieves superior performance under bitemporal supervision. Code is available at https://github.com/Z-Zheng/ChangeStar.

@inproceedings{zheng2021change,
  title={Change is everywhere: Single-temporal supervised object change detection in remote sensing imagery},
  author={Zheng, Zhuo and Ma, Ailong and Zhang, Liangpei and Zhong, Yanfei},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={15193--15202},
  year={2021}
}

Results and models

LEVIR-CD

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Precision	Recall	F1-Score	IoU	config	download
ChangeStar	r18	512x512	40000	-	93.75	88.90	91.26	83.92	config

• All metrics are based on the category "change".
• All scores are computed on the test set.