@inproceedings{601c31784aac4549b727a04c92e1b4a6,
title = "Algorithms for efficient multi-temporal change detection in SAR imagery",
abstract = "Advancements in the tasking and collection capabilities of SAR providers have reduced the spatiotemporal constraints on SAR-based change detection. As data constraints are relaxed, pairwise SAR-based change detection algorithms are rapidly becoming insufficient for summarizing change activity. To address this, we present two multi-temporal change detection algorithms that categorize, filter, and reduce the changes detected in any number of repeat pass SAR scenes: (1) Object Permanence change detection (OPcd); and (2) Activity Exclusion change detection (AEcd). When compared to traditional pairwise change detection methods, OPcd and AEcd allow for rapid digestion and efficient visualization of changes.",
keywords = "Change Detection, Deforestation, Forest Change, SAR, Time Series",
author = "Michael Allen and Kosianka, \{Justyna W.\} and Mark Perillo",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE.; Algorithms for Synthetic Aperture Radar Imagery XXX 2023 ; Conference date: 02-05-2023 Through 03-05-2023",
year = "2023",
doi = "10.1117/12.2663997",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Edmund Zelnio and Garber, \{Frederick D.\}",
booktitle = "Algorithms for Synthetic Aperture Radar Imagery XXX",
}