A novel change detection approach based on visual saliency and random forest from multi-temporal high-resolution remote-sensing images
(2018) In International Journal of Remote Sensing 39(22). p.7998-8021- Abstract
This article presents a novel change detection (CD) approach for high-resolution remote-sensing images, which incorporates visual saliency and random forest (RF). First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis. Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy c-means (FCM) clustering to obtain the... (More)
This article presents a novel change detection (CD) approach for high-resolution remote-sensing images, which incorporates visual saliency and random forest (RF). First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis. Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy c-means (FCM) clustering to obtain the pixel-level pre-classification result, which can be used as a prerequisite for super-pixel-based analysis. Third, on the basis of the optimal segmentation and pixel-level pre-classification results, different super-pixel change possibilities are calculated. Furthermore, the changed and unchanged super-pixels that serve as the training samples are automatically selected. The spectral features and Gabor features of each super-pixel are extracted. Finally, super-pixel-based CD is implemented by applying RF based on these samples. Experimental results on Quickbird, Ziyuan 3 (ZY3), and Gaofen 2 (GF2) multi-spectral images show that the proposed method outperforms the compared methods in the accuracy of CD, and also confirm the feasibility and effectiveness of the proposed approach.
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- author
- Feng, Wenqing ; Sui, Haigang ; Tu, Jihui ; Huang, Weiming LU and Sun, Kaimin
- organization
- publishing date
- 2018-11-17
- type
- Contribution to journal
- publication status
- published
- subject
- in
- International Journal of Remote Sensing
- volume
- 39
- issue
- 22
- pages
- 7998 - 8021
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:85050001502
- ISSN
- 0143-1161
- DOI
- 10.1080/01431161.2018.1479794
- language
- English
- LU publication?
- yes
- id
- da914f3c-24ab-41c8-8f28-e51aa9173262
- date added to LUP
- 2018-08-01 10:12:27
- date last changed
- 2022-04-25 08:34:31
@article{da914f3c-24ab-41c8-8f28-e51aa9173262,
abstract = {{<p>This article presents a novel change detection (CD) approach for high-resolution remote-sensing images, which incorporates visual saliency and random forest (RF). First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis. Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy c-means (FCM) clustering to obtain the pixel-level pre-classification result, which can be used as a prerequisite for super-pixel-based analysis. Third, on the basis of the optimal segmentation and pixel-level pre-classification results, different super-pixel change possibilities are calculated. Furthermore, the changed and unchanged super-pixels that serve as the training samples are automatically selected. The spectral features and Gabor features of each super-pixel are extracted. Finally, super-pixel-based CD is implemented by applying RF based on these samples. Experimental results on Quickbird, Ziyuan 3 (ZY3), and Gaofen 2 (GF2) multi-spectral images show that the proposed method outperforms the compared methods in the accuracy of CD, and also confirm the feasibility and effectiveness of the proposed approach.</p>}},
author = {{Feng, Wenqing and Sui, Haigang and Tu, Jihui and Huang, Weiming and Sun, Kaimin}},
issn = {{0143-1161}},
language = {{eng}},
month = {{11}},
number = {{22}},
pages = {{7998--8021}},
publisher = {{Taylor & Francis}},
series = {{International Journal of Remote Sensing}},
title = {{A novel change detection approach based on visual saliency and random forest from multi-temporal high-resolution remote-sensing images}},
url = {{http://dx.doi.org/10.1080/01431161.2018.1479794}},
doi = {{10.1080/01431161.2018.1479794}},
volume = {{39}},
year = {{2018}},
}