NDLI: Object Detection Based on Sparse Representation and Hough Voting for Optical Remote Sensing Imagery

Content Provider	IEEE Xplore Digital Library
Author	Yokoya, N. Iwasaki, A.
Copyright Year	2008
Abstract	We present a novel method for detecting instances of an object class or specific object in high-spatial-resolution optical remote sensing images. The proposed method integrates sparse representations for local-feature detection into generalized-Hough-transform object detection. Object parts are detected via class-specific sparse image representations of patches using learned target and background dictionaries, and their co-occurrence is spatially integrated by Hough voting, which enables object detection. We aim to efficiently detect target objects using a small set of positive training samples by matching essential object parts with a target dictionary while the residuals are explained by a background dictionary. Experimental results show that the proposed method achieves state-of-the-art performance for several examples including object-class detection and specific-object identification.
Starting Page	2053
Ending Page	2062
Page Count	10
File Size	1493790
File Format	PDF
ISSN	19391404
Volume Number	8
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Object detection Training Remote sensing Boats Feature extraction Airplanes sparse representations Hough transforms object detection
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Computers in Earth Sciences

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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