DocumentCode
36167
Title
Local Edge Distributions for Detection of Salient Structure Textures and Objects
Author
Xiangyun Hu ; Jiajie Shen ; Jie Shan ; Li Pan
Author_Institution
Sch. of Remote Sensing & Inf. Eng., Wuhan Univ., Wuhan, China
Volume
10
Issue
3
fYear
2013
fDate
May-13
Firstpage
466
Lastpage
470
Abstract
Automatic detection of regions of salient texture and objects is useful for analysis of remotely sensed imagery, such as for land cover classification, object detection, and change detection. Intuitively, the local edges on an image indicate spectral discontinuity and the existence of structure texture or objects. This letter explores a simple method for measuring the saliency of texture and objects based on the edge density and spatial evenness of the edge distribution in the local window of each pixel. This method generates a saliency map by computing the saliency index of each pixel. By segmenting the saliency map, the salient structure texture regions and the locations of objects can be extracted. The algorithm requires only the window size as the input parameter and is relatively simple to implement. Experiments using high-resolution images show its effectiveness and accuracy in the detection of salient structure texture regions, such as crops and residential areas, and man-made objects, such as airplanes, cars, etc.
Keywords
geophysical image processing; geophysical techniques; image classification; image segmentation; remote sensing; change detection; high-resolution images; image indicate spectral discontinuity; land cover classification; local edge distributions; man-made objects; object detection; pixel local window; region automatic detection; remotely sensed imagery; saliency index; saliency map segmenting; salient structure textures; Airplanes; Feature extraction; Image edge detection; Image segmentation; Indexes; Object detection; Remote sensing; Edge distributions; object detection; saliency; structure texture;
fLanguage
English
Journal_Title
Geoscience and Remote Sensing Letters, IEEE
Publisher
ieee
ISSN
1545-598X
Type
jour
DOI
10.1109/LGRS.2012.2210188
Filename
6289342
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