Change Detection in SAR Images Using Deep Learning Methods

Luca Bergamasco () and Francesca Bovolo ()
Additional contact information
Luca Bergamasco: Fondazione Bruno Kessler
Francesca Bovolo: Fondazione Bruno Kessler

A chapter in Synthetic Aperture Radar (SAR) Data Applications, 2022, pp 25-62 from Springer

Abstract: Abstract SAR data allow regular monitoring of target areas since their acquisition is not affected by weather or light condition problems. This characteristic makes them optimal for civil protection tasks, such as earthquake damage assessment, where quick responses in any weather and light conditions are needed. Change detection (CD) methods address this application by identifying changes over an analyzed area using bi-temporal or multi-temporal SAR images. These methods detect the changes using features that provide useful information about the changes. In the last years, CD methods exploiting deep learning (DL) models have become popular because of their capability to learn features from the input data. Many DL CD methods exploit supervised models to identify changes using labeled bi-temporal or multi-temporal data to train the model. However, the gathering of labeled multi-temporal data is challenging. Transfer learning methods avoid the supervised training and the labeled data gathering by exploiting pre-trained models as feature extractors for CD tasks. They achieve good results when the target images are similar to the images used to pre-train the model. We focus this chapter on the unsupervised DL CD methods that exploit unsupervised DL models to extract feature maps. Unsupervised DL models automatically learn features from unlabeled samples during the training. The feature maps retrieved from these models are used to detect changed areas in multi-temporal images and handle the speckle noise.

Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-21225-3_2

Ordering information: This item can be ordered from
http://www.springer.com/9783031212253

DOI: 10.1007/978-3-031-21225-3_2

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().