Unsupervised Change Detection Using Spectrum-Trend and Shape Similarity Measure

Yi Tian,Hua Zhang,Ming Hao

doi:10.3390/rs12213606

Yi Tian, Hua Zhang + Show 1 more

Open Access

https://doi.org/10.3390/rs12213606

Copy DOI

Journal: Remote sensing	Publication Date: Nov 3, 2020
Citations: 5	License type: CC BY 4.0

Affiliation: China University of Mining and Technology

Abstract

The emergence of very high resolution (VHR) images contributes to big challenges in change detection. It is hard for traditional pixel-level approaches to achieve satisfying performance due to radiometric difference. This work proposes a novel feature descriptor that is based on spectrum-trend and shape context for VHR remote sensing images. The proposed method is mainly composed of two aspects. The spectrum-trend graph is generated first, and then the shape context is applied in order to describe the shape of spectrum-trend. By constructing spectrum-trend graph, spatial and spectral information is integrated effectively. The approach is performed and assessed by QuickBird and SPOT-5 satellite images. The quantitative analysis of comparative experiments proves the effectiveness of the proposed technique in dealing with the radiometric difference and improving the accuracy of change detection. The results indicate that the overall accuracy and robustness are both boosted. Moreover, this work provides a novel viewpoint for discriminating changed and unchanged pixels by comparing the shape similarity of local spectrum-trend.

Highlights

Change detection is of great significance as an attractive scientific area, and it is a process of distinguishing changed and unchanged regions [1]
Aimed at boosting the accuracy of change detection, this paper proposes a novel method, called local-scene spectrum-trend shape context (LSSC) descriptor
An unsupervised method that is based on spectrum-trend graph and shape context has been proposed and applied to change detection for very high-resolution remote sensing images

Summary

Introduction

Change detection is of great significance as an attractive scientific area, and it is a process of distinguishing changed and unchanged regions [1]. It is performed by analyzing remote sensing images that were obtained from the same geographical area at different times [2]. The supervised methods can achieve higher accuracy than unsupervised methods; it is difficult to collect enough ground truths in many circumstances. This is why substantial researchers devoted much more efforts into unsupervised method. We focus on unsupervised change detection methods

Objectives

Methods

Results

Discussion

Conclusion