NDLI: Ship Detection in High-Resolution Optical Imagery Based on Anomaly Detector and Local Shape Feature

Content Provider	IEEE Xplore Digital Library
Author	Zhenwei Shi Xinran Yu Zhiguo Jiang Bo Li
Copyright Year	1980
Abstract	Ship detection in high-resolution optical imagery is a challenging task due to the variable appearances of ships and background. This paper aims at further investigating this problem and presents an approach to detect ships in a “coarse-to-fine” manner. First, to increase the separability between ships and background, we concentrate on the pixels in the vicinities of ships. We rearrange the spatially adjacent pixels into a vector, transforming the panchromatic image into a “fake” hyperspectral form. Through this procedure, each produced vector is endowed with some contextual information, which amplifies the separability between ships and background. Afterward, for the “fake” hyperspectral image, a hyperspectral algorithm is applied to extract ship candidates preliminarily and quickly by regarding ships as anomalies. Finally, to validate real ships out of ship candidates, an extra feature is provided with histograms of oriented gradients (HOGs) to generate a hypothesis using AdaBoost algorithm. This extra feature focuses on the gray values rather than the gradients of an image and includes some information generated by very near but not closely adjacent pixels, which can reinforce HOG to some degree. Experimental results on real database indicate that the hyperspectral algorithm is robust, even for the ships with low contrast. In addition, in terms of the shape of ships, the extended HOG feature turns out to be better than HOG itself as well as some other features such as local binary pattern.
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	4511
Ending Page	4523
Page Count	13
File Size	1789797
File Format	PDF
ISSN	01962892
Volume Number	52
Issue Number	8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Marine vehicles Hyperspectral imaging Vectors Optical imaging Detectors Feature extraction ship detection Circle frequency–histograms of oriented gradients (CF-HOG) feature optical panchromatic image analysis “Reed–Xiaoli” algorithm
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Unsupervised Ship Detection Based on Saliency and S-HOG Descriptor From Optical Satellite Images

Adjusted Spectral Matched Filter for Target Detection in Hyperspectral Imagery

An Efficient Hardware Implementation of HOG Feature Extraction for Human Detection

Vision-Based Vehicle Detection System With Consideration of the Detecting Location

A Selective KPCA Algorithm Based on High-Order Statistics for Anomaly Detection in Hyperspectral Imagery

Image Super-Resolution With Sparse Neighbor Embedding

A Novel Hierarchical Method of Ship Detection from Spaceborne Optical Image Based on Shape and Texture Features

A Hierarchical Ship Detection Scheme for High-Resolution SAR Images

A Vector SIFT Detector for Interest Point Detection in Hyperspectral Imagery

Ship Detection in High-Resolution Optical Imagery Based on Anomaly Detector and Local Shape Feature

Similar Documents

Unsupervised Ship Detection Based on Saliency and S-HOG Descriptor From Optical Satellite Images

Adjusted Spectral Matched Filter for Target Detection in Hyperspectral Imagery

An Efficient Hardware Implementation of HOG Feature Extraction for Human Detection

Vision-Based Vehicle Detection System With Consideration of the Detecting Location

A Selective KPCA Algorithm Based on High-Order Statistics for Anomaly Detection in Hyperspectral Imagery

Image Super-Resolution With Sparse Neighbor Embedding

A Novel Hierarchical Method of Ship Detection from Spaceborne Optical Image Based on Shape and Texture Features

A Hierarchical Ship Detection Scheme for High-Resolution SAR Images

A Vector SIFT Detector for Interest Point Detection in Hyperspectral Imagery

Ship Detection in High-Resolution Optical Imagery Based on Anomaly Detector and Local Shape Feature