Next Article in Journal
Estimating Chlorophyll Fluorescence Parameters Using the Joint Fraunhofer Line Depth and Laser-Induced Saturation Pulse (FLD-LISP) Method in Different Plant Species
Previous Article in Journal
Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(6), 597; doi:10.3390/rs9060597

Region-of-Interest Extraction Based on Local–Global Contrast Analysis and Intra-Spectrum Information Distribution Estimation for Remote Sensing Images

1
The College of Information Science and Technology, Beijing Normal University, Beijing 100875, China
2
The State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Academic Editors: Guoqing Zhou and Prasad S. Thenkabail
Received: 27 March 2017 / Revised: 25 May 2017 / Accepted: 7 June 2017 / Published: 12 June 2017
View Full-Text   |   Download PDF [33394 KB, uploaded 15 June 2017]   |  

Abstract

Traditional saliency analysis models have made great advances in region of interest (ROI) extraction in natural scene images and videos. However, due to different imaging mechanisms and image features, those approaches are not quite appropriate for remote sensing images. Thus, we propose a novel saliency analysis and ROI extraction method for remote sensing images, which is composed of local–global contrast analysis for panchromatic images and intra-spectrum information distribution estimation (LI) for multi-spectral images. The panchromatic image is first segmented into superpixels via level set methods to reduce the subsequent computation complexity and keep region boundaries. Then, the spatially weighted superpixel intensity contrast is calculated globally to highlight superpixels unique to others and obtain the intensity saliency map. In multi-spectral images, ROIs are often included in informative superpixels; therefore, the information theory is introduced to each spectrum independently to acquire the spectrum saliency map. The final result is obtained by fusing the intensity saliency map and the spectrum saliency map and enhancing pixel-level saliency. To improve the anti-noise properties, we employ the Gaussian Pyramid for multi-scale analysis, which removes noise points by the blurring operation and the down-sampling operation. Experiments were conducted aiming at comparing the LI model with nine competing models qualitatively and quantitatively. The results show that the LI model performs better in maintaining intact ROIs with well-defined boundaries and less outside interference, and it tends to be stable when faced with images contaminated by noise. View Full-Text
Keywords: remote sensing; image processing; regionofinterest extraction; saliency detection; contrast analysis; information distribution estimation remote sensing; image processing; regionofinterest extraction; saliency detection; contrast analysis; information distribution estimation
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zhang, L.; Wang, S. Region-of-Interest Extraction Based on Local–Global Contrast Analysis and Intra-Spectrum Information Distribution Estimation for Remote Sensing Images. Remote Sens. 2017, 9, 597.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top