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Weiming Huang

Weiming Huang

Postdoctoral fellow

Weiming Huang

A novel change detection approach for multi-temporal high-resolution remote sensing images based on rotation forest and coarse-to-fine uncertainty analyses

Author

  • Wenqing Feng
  • Haigang Sui
  • Jihui Tu
  • Weiming Huang
  • Chuan Xu
  • Kaimin Sun

Summary, in English

In the process of object-based change detection (OBCD), scale is a significant factor related to extraction and analyses of subsequent change data. To address this problem, this paper describes an object-based approach to urban area change detection (CD) using rotation forest (RoF) and coarse-to-fine uncertainty analyses of multi-temporal high-resolution remote sensing images. First, highly homogeneous objects with consistent spatial positions are identified through vector-raster integration and multi-scale fine segmentation. The multi-temporal images are stacked and segmented under the constraints of a historical land use vector map using a series of optimal segmentation scales, ranging from coarse to fine. Second, neighborhood correlation image analyses are performed to highlight pixels with high probabilities of being changed or unchanged, which can be used as a prerequisite for object-based analyses. Third, based on the coarse-to-fine segmentation and pixel-based pre-classification results, change possibilities are calculated for various objects. Furthermore, changed and unchanged objects identified at different scales are automatically selected to serve as training samples. The spectral and texture features of each object are extracted. Finally, uncertain objects are classified using the RoF classifier. Multi-scale classification results are combined using a majority voting rule to generate the final CD results. In experiments using two pairs of real high-resolution remote sensing datasets, our proposed approach outperformed existing methods in terms of CD accuracy, verifying its feasibility and effectiveness.

Department/s

  • Dept of Physical Geography and Ecosystem Science

Publishing year

2018-07-01

Language

English

Publication/Series

Remote Sensing

Volume

10

Issue

7

Document type

Journal article

Publisher

MDPI AG

Topic

  • Environmental Engineering

Keywords

  • Change detection
  • Features
  • Majority voting
  • Neighborhood correlation image analyses
  • Rotation forest
  • Scale
  • Segmentation

Status

Published

ISBN/ISSN/Other

  • ISSN: 2072-4292