Non-Registration Change Detection: A Novel Change Detection Task and Benchmark Dataset

TL;DR

This paper introduces a new change detection task called non-registration change detection, along with a benchmark dataset, highlighting its importance in real-world emergencies and its impact on existing methods.

Contribution

It defines the non-registration change detection task, creates a benchmark dataset, and demonstrates its significant challenges to current state-of-the-art methods.

Findings

Non-registration change detection can cause catastrophic failure in existing methods.

Eight real-world scenarios for non-registration issues are systematically proposed.

A dataset and transformation schemes for non-registration detection are developed.

Abstract

In this study, we propose a novel remote sensing change detection task, non-registration change detection, to address the increasing number of emergencies such as natural disasters, anthropogenic accidents, and military strikes. First, in light of the limited discourse on the issue of non-registration change detection, we systematically propose eight scenarios that could arise in the real world and potentially contribute to the occurrence of non-registration problems. Second, we develop distinct image transformation schemes tailored to various scenarios to convert the available registration change detection dataset into a non-registration version. Finally, we demonstrate that non-registration change detection can cause catastrophic damage to the state-of-the-art methods. Our code and dataset are available at https://github.com/ShanZard/NRCD.