A CHANGE DETECTION ALGORITHM FOR SAR IMAGES BASED ON LOGISTIC REGRESSION

This paper presents an incoherent change detection algorithm (CDA) for synthetic aperture radar (SAR) images based on logistic regression. The input data consists of a set of 24 SAR images acquired in a test site in northern Sweden [1]. Subsets of these images are trained based on pixel amplitude, f...

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Bibliographic Details
Published in:IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
Main Authors: Molin, Ricardo D., Jr., Rosa, Rafael A. S., Bayer, Fabio M., Pettersson, Mats, Machado, Renato
Format: Conference Object
Language:English
Published: Blekinge Tekniska Högskola, Institutionen för matematik och naturvetenskap 2019
Subjects:
Change Detection Algorithm
Logistic Regression
SAR images
Other Electrical Engineering
Electronic Engineering
Information Engineering
Annan elektroteknik och elektronik
Northern Sweden
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:bth-19384
https://doi.org/10.1109/IGARSS.2019.8900064
Description
Summary:This paper presents an incoherent change detection algorithm (CDA) for synthetic aperture radar (SAR) images based on logistic regression. The input data consists of a set of 24 SAR images acquired in a test site in northern Sweden [1]. Subsets of these images are trained based on pixel amplitude, flight heading and neighboring features such as local mean, standard deviation and skewness. The proposed method intends to explore the advantadges from both pixel- and object-based approaches, while evaluating multiple features in amplitude only SAR images. Preliminary results based on K-fold cross validation have shown that the proposed CDA achieves good performance when compared to the results presented in [1].

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