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New Saliency Point Detection and Evaluation
Methods for Finding Structural Differences in
Remote Sensing Images of Long
Time-Span Samples
Andrea Kovacs
1
and Tamas Sziranyi
2
1
Pazmany Peter Catholic University
Prater 50/A, 1083, Budapest, Hungary
2
Hungarian Academy of Sciences, Computer and Automation Research Institute
Distributed Events Analysis Research Group
Kende 13-17, 1111, Budapest, Hungary
{
kovacs.andrea,sziranyi
}
@sztaki.hu
Abstract.
The paper introduces a novel methodology to find changes in
remote sensing image series. Some remotely sensed areas are scanned fre-
quently 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 dif-
ferent lighting and surface conditions. The presented method is basically
an exploitation of Harris saliency function and its derivatives for find-
ing 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 gener-
ating 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.
Keywords:
remote sensing, Harris function, change detection.
1
Introduction
Automatic evaluation of aerial photograph repositories is an important field of
research since manual administration is time consuming and cumbersome. Long
time-span surveillance or reconnaissance about the same area can be crucial for
quick and up-to-date content retrieval. The extraction of changes may facilitate
applications like urban development analysis, disaster protection, agricultural
monitoring, and detection of illegal garbage heaps, or wood cuttings. The ob-
tained change map should provide useful information about size, shape, or quan-
tity of the changed areas, which could be applied directly by higher level object
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