PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Self-organising maps for change detection and monitoring of human activity in satellite imagery
Matthieu Molinier, Jorma Laaksonen and Tuomas Häme
In: ESA-EUSC 2006: Image Information Mining for Security and Intelligence, 27-29 Nov 2006, Madrid, Spain.

Abstract

The increasing number and resolution of satellite sensors to be launched in the coming years will dramatically increase the need for efficient image archiving and processing. Current Earth Observation archiving systems typically support queries by sensor type, acquisition date, imagery coverage or a combination of them. Concurrently, security-concerned applications relying on satellite imagery often demand repeated or continuous monitoring, and intelligent access to the extracted information. There is therefore a growing interest in the remote sensing community to access databases directly by the information contained in images. Content-Based Image Retrieval (CBIR) allows an efficient management of large image archives, as well as satellite image annotation and interpretation. We extended and exploited the potential of PicSOM, a CBIR system based on Self-Organising Maps (SOMs), for remote sensing image analysis, with a particular focus on detection of man-made structures and changes. The same framework allows for supervised and unsupervised change detection, with promising applications in long-term monitoring of strategic sites or content-driven novelty detection.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Information Retrieval & Textual Information Access
ID Code:2593
Deposited By:Jorma Laaksonen
Deposited On:14 February 2008