A scale-invariant change detection method for land use/cover change research

被引:32
作者
Xing, Jin [1 ]
Sieber, Renee [1 ]
Caelli, Terrence [2 ]
机构
[1] McGill Univ, Dept Geog, Montreal, PQ, Canada
[2] Univ Melbourne, Dept Elect & Elect Engn, Melbourne, Vic, Australia
关键词
Land use/cover change detection; Scale variance; Scale-invariant feature transformation; Maximally Stable Extremal Region; Hadoop; Cloud computing; CLASSIFICATION; COVER; FEATURES; ALGORITHMS; ACCURACY; ENTROPY; IMAGES; TIME; AREA;
D O I
10.1016/j.isprsjprs.2018.04.013
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Land Use/Cover Change (LUCC) detection relies increasingly on comparing remote sensing images with different spatial and spectral scales. Based on scale-invariant image analysis algorithms in computer vision, we propose a scale-invariant LUCC detection method to identify changes from scale heterogeneous images. This method is composed of an entropy-based spatial decomposition, two scale invariant feature extraction methods, Maximally Stable Extremal Region (MSER) and Scale-Invariant Feature Transformation (SIFT) algorithms, a spatial regression voting method to integrate MSER and SIFT results, a Markov Random Field-based smoothing method, and a support vector machine classification method to assign LUCC labels. We test the scale invariance of our new method with a LUCC case study in Montreal, Canada, 2005-2012. We found that the scale-invariant LUCC detection method provides similar accuracy compared with the resampling-based approach but this method avoids the LUCC distortion incurred by resampling. (C) 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:252 / 264
页数:13
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