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Change Detection Based on Multi-Feature Clustering Using Differential Evolution for Landsat Imagery

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
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Remote Sens. 2018, 10(10), 1664; https://doi.org/10.3390/rs10101664
Received: 31 August 2018 / Revised: 2 October 2018 / Accepted: 16 October 2018 / Published: 21 October 2018
(This article belongs to the Section Remote Sensing Image Processing)
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Abstract

Change detection (CD) of natural land cover is important for environmental protection and to maintain an ecological balance. The Landsat series of satellites provide continuous observation of the Earth’s surface and is sensitive to reflection of water, soil and vegetation. It offers fine spatial resolutions (15–80 m) and short revisit times (16–18 days). Therefore, Landsat imagery is suitable for monitoring natural land cover changes. Clustering-based CD methods using evolutionary algorithms (EAs) can be applied to Landsat images to obtain optimal changed and unchanged clustering centers (clusters) with minimum clustering index. However, they directly analyze difference image (DI), which finds itself subject to interference by Gaussian noise and local brightness distortion in Landsat data, resulting in false alarms in detection results. In order to reduce image interferences and improve CD accuracy, we proposed an unsupervised CD method based on multi-feature clustering using the differential evolution algorithm (M-DECD) for Landsat Imagery. First, according to characteristics of Landsat data, a multi-feature space is constructed with three elements: Wiener de-noising, detail enhancement, and structural similarity. Then, a CD method based on differential evolution (DE) algorithm and fuzzy clustering is proposed to obtain global optimal clusters in the multi-feature space, and generate a binary change map (CM). In addition, the control parameters of the DE algorithm are adjusted to improve the robustness of M-DECD. The experimental results obtained with four Landsat datasets confirm the effectiveness of M-DECD. Compared with the results of conventional methods and the current state-of-the-art methods based on evolutionary clustering, the detection accuracies of the M-DECD on the Mexico dataset and the Sardinia dataset are very close to the best results. The accuracies of the M-DECD in the Alaska dataset and the large Canada dataset increased by about 3.3% and 11.9%, respectively. This indicates that multiple features are suitable for Landsat images and the DE algorithm is effective in searching for an optimal CD result. View Full-Text
Keywords: change detection; detail enhancement; differential evolution; multi-feature clustering; noise robust; remote sensing imagery; structural similarity change detection; detail enhancement; differential evolution; multi-feature clustering; noise robust; remote sensing imagery; structural similarity
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Song, M.; Zhong, Y.; Ma, A. Change Detection Based on Multi-Feature Clustering Using Differential Evolution for Landsat Imagery. Remote Sens. 2018, 10, 1664.

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