A theoretical framework for unsupervised land cover change detection in dense satellite image time series

Indira Aprilia Listiani; Francesca Bovolo; Massimo Zanetti

doi:10.1117/12.2679728

19 October 2023 A theoretical framework for unsupervised land cover change detection in dense satellite image time series

Indira Aprilia Listiani, Francesca Bovolo, Massimo Zanetti

Author Affiliations +

Proceedings Volume 12733, Image and Signal Processing for Remote Sensing XXIX; 127330A (2023) https://doi.org/10.1117/12.2679728
Event: SPIE Remote Sensing, 2023, Amsterdam, Netherlands

Abstract

This paper addresses the complex task of detecting and characterizing changes in dense Satellite Image Time Series (SITS). Although Change Vector Analysis (CVA) is widely used for Change Detection (CD), it has limitations due to missing prior information on changes, such as: optimal spectral channels and change timing. Time series data can help overcome these limitations, but working with them is challenging. To address these challenges, the paper introduces a novel framework called Time Series Change Vector Analysis (TSCVA), which builds upon the principles of CVA. In TSCVA, the paper redefines CVA in the time series feature space and introduces new definitions for change in time series magnitude and direction. This allows for a detailed analysis of change components in the time and spectrum domain within the SITS, enabling unsupervised CD. We utilize the expectation-maximization algorithm to estimate parameters of statistical distributions for change and no change classes. The effectiveness of the proposed TSCVA method is evaluated using Sentinel-2 time series data. The results, both quantitative and qualitative, confirm the robustness of this approach in effectively addressing the CD problem in dense SITS.

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(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Indira Aprilia Listiani, Francesca Bovolo, and Massimo Zanetti "A theoretical framework for unsupervised land cover change detection in dense satellite image time series", Proc. SPIE 12733, Image and Signal Processing for Remote Sensing XXIX, 127330A (19 October 2023); https://doi.org/10.1117/12.2679728

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