Details
| Original language | English |
|---|---|
| Pages (from-to) | 9368-9396 |
| Number of pages | 29 |
| Journal | International Journal of Remote Sensing |
| Volume | 42 |
| Issue number | 24 |
| Publication status | Published - 2021 |
| Externally published | Yes |
Abstract
As a leading natural wealth, forests play an essential role in the development and prosperity of countries. Hence, monitoring their changes can lead to proper management and planning in conserving these resources. This study presents a novel unsupervised forest change detection method comprising two main steps: (1) generating a reliable difference image, i.e. sensitive to forest changes, and (2) producing a change map in which forest changes and their details (e.g. edges) are well characterized. In step (1), the vegetation indices- and spectral-based difference images were first calculated using a novel weighted angular operator. Afterwards, the difference images were combined using the 2D-multiresolution singular value decomposition (2D-MSVD) fusion approach to generate a noise-resistant difference image, in which forest changes are accurately highlighted. In step (2), the expectation-maximization gaussian mixture model (EMGMM) was first applied to the fused difference image to reach an initial binary change map. Next, an edge-aware MRF (EAMRF) model was initialized by the EMGMM-derived change map and then was adopted to achieve the final change map. Experimental results were achieved by utilizing five bi-temporal images acquired by the Landsat 5 and 8, and Sentinel 2 satellite sensors. The results indicated the efficacy of the proposed fused difference image in reflecting the forest changes. Compared with the traditional MRF method, the boundaries and geometrical shapes of changed regions were well preserved in the change maps obtained by EAMRF. The edge penalty function embedded in EAMRF also made this model converge in less running time compared to the traditional MRF algorithm. Furthermore, EAMRF outperformed the other change detection methods in terms of quantitative and qualitative results, demonstrating its high potential for forest change detection applications. The source code of the proposed change detection method with some samples of the datasets has been made available on https://github.com/AminMohsenifar to support related future works in remote sensing.
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- General Earth and Planetary Sciences
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In: International Journal of Remote Sensing, Vol. 42, No. 24, 2021, p. 9368-9396.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A novel unsupervised forest change detection method based on the integration of a multiresolution singular value decomposition fusion and an edge-aware Markov Random Field algorithm
AU - Mohsenifar, Amin
AU - Mohammadzadeh, Ali
AU - Moghimi, Armin
AU - Salehi, Bahram
N1 - Funding Information: This research did not receive any specific grant from funding agencies in the public, commercial. Publisher Copyright: © 2021 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2021
Y1 - 2021
N2 - As a leading natural wealth, forests play an essential role in the development and prosperity of countries. Hence, monitoring their changes can lead to proper management and planning in conserving these resources. This study presents a novel unsupervised forest change detection method comprising two main steps: (1) generating a reliable difference image, i.e. sensitive to forest changes, and (2) producing a change map in which forest changes and their details (e.g. edges) are well characterized. In step (1), the vegetation indices- and spectral-based difference images were first calculated using a novel weighted angular operator. Afterwards, the difference images were combined using the 2D-multiresolution singular value decomposition (2D-MSVD) fusion approach to generate a noise-resistant difference image, in which forest changes are accurately highlighted. In step (2), the expectation-maximization gaussian mixture model (EMGMM) was first applied to the fused difference image to reach an initial binary change map. Next, an edge-aware MRF (EAMRF) model was initialized by the EMGMM-derived change map and then was adopted to achieve the final change map. Experimental results were achieved by utilizing five bi-temporal images acquired by the Landsat 5 and 8, and Sentinel 2 satellite sensors. The results indicated the efficacy of the proposed fused difference image in reflecting the forest changes. Compared with the traditional MRF method, the boundaries and geometrical shapes of changed regions were well preserved in the change maps obtained by EAMRF. The edge penalty function embedded in EAMRF also made this model converge in less running time compared to the traditional MRF algorithm. Furthermore, EAMRF outperformed the other change detection methods in terms of quantitative and qualitative results, demonstrating its high potential for forest change detection applications. The source code of the proposed change detection method with some samples of the datasets has been made available on https://github.com/AminMohsenifar to support related future works in remote sensing.
AB - As a leading natural wealth, forests play an essential role in the development and prosperity of countries. Hence, monitoring their changes can lead to proper management and planning in conserving these resources. This study presents a novel unsupervised forest change detection method comprising two main steps: (1) generating a reliable difference image, i.e. sensitive to forest changes, and (2) producing a change map in which forest changes and their details (e.g. edges) are well characterized. In step (1), the vegetation indices- and spectral-based difference images were first calculated using a novel weighted angular operator. Afterwards, the difference images were combined using the 2D-multiresolution singular value decomposition (2D-MSVD) fusion approach to generate a noise-resistant difference image, in which forest changes are accurately highlighted. In step (2), the expectation-maximization gaussian mixture model (EMGMM) was first applied to the fused difference image to reach an initial binary change map. Next, an edge-aware MRF (EAMRF) model was initialized by the EMGMM-derived change map and then was adopted to achieve the final change map. Experimental results were achieved by utilizing five bi-temporal images acquired by the Landsat 5 and 8, and Sentinel 2 satellite sensors. The results indicated the efficacy of the proposed fused difference image in reflecting the forest changes. Compared with the traditional MRF method, the boundaries and geometrical shapes of changed regions were well preserved in the change maps obtained by EAMRF. The edge penalty function embedded in EAMRF also made this model converge in less running time compared to the traditional MRF algorithm. Furthermore, EAMRF outperformed the other change detection methods in terms of quantitative and qualitative results, demonstrating its high potential for forest change detection applications. The source code of the proposed change detection method with some samples of the datasets has been made available on https://github.com/AminMohsenifar to support related future works in remote sensing.
UR - http://www.scopus.com/inward/record.url?scp=85120176374&partnerID=8YFLogxK
U2 - 10.1080/01431161.2021.1995075
DO - 10.1080/01431161.2021.1995075
M3 - Article
AN - SCOPUS:85120176374
VL - 42
SP - 9368
EP - 9396
JO - International Journal of Remote Sensing
JF - International Journal of Remote Sensing
SN - 0143-1161
IS - 24
ER -