Paper Info
Title
New Saliency Point Detection and Evaluation Methods for Finding Structural Differences in Remote Sensing Images of Long Time-Span Samples
Abstract
The paper introduces a novel methodology to find changes in remote sensing image series. Some remotely sensed areas are scanned frequently to spot relevant changes, and several repositories contain multi-temporal image samples for the same area. The proposed method finds changes in images scanned by a long time-interval difference in very different lighting and surface conditions. The presented method is basically an exploitation of Harris saliency function and its derivatives for finding featuring points among image samples. To fit together the definition of keypoints and their active contour around them, we have introduced the Harris corner detection as an outline detector instead of the simple edge functions. We also demonstrate a new local descriptor by generating local active contours. Saliency points support the boundary hull definition of objects, constructing by graph based connectivity detection and neighborhood description. This graph based shape descriptor works on the saliency points of the difference and in-layer features. We prove the method in finding structural changes on remote sensing images.
Year
DOI
Venue
2010
10.1007/978-3-642-17691-3_25
ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS, PT II
Keywords
Field
DocType
remote sensing,Harris function,change detection
Change detection,Corner detection,Salience (neuroscience),Computer science,Remote sensing,Artificial intelligence,Detector,Active contour model,Computer vision,Graph,Pattern recognition,Image Series,Hull
Conference
Volume
ISSN
Citations 
6475
0302-9743
2
PageRank 
References 
Authors
0.44
18
2
Name
Order
Citations
PageRank
Andrea Kovács1373.56
Sziranyi, T.239544.76