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A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images.

Bin Hou1, Yunhong Wang2, Qingjie Liu3

  • 1State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China. houbin@buaa.edu.cn.

Sensors (Basel, Switzerland)
|September 14, 2016
PubMed
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This study introduces a new method for detecting building changes using high-resolution remote sensing images. The approach combines pixel and object-based techniques for accurate change detection in urban areas.

Area of Science:

  • Geosciences
  • Computer Science
  • Remote Sensing

Background:

  • Accurate Earth surface characterization is vital for urban planning, resource monitoring, and environmental studies.
  • Remote sensing (RS) images are crucial for change detection (CD), with high-resolution (HR) imagery presenting new challenges and opportunities.
  • Traditional CD methods struggle with HR images, necessitating advancements in object-based approaches, particularly for building change detection.

Purpose of the Study:

  • To propose a novel automatic approach for detecting building changes in high-resolution remote sensing images.
  • To combine pixel-based and object-based strategies for enhanced building change detection.
  • To leverage extended morphological attribute profiles (EMAPs) and random forest (RF) classification for improved accuracy.

Main Methods:

Keywords:
change detectionextended morphological attribute profilesmorphological building indexremote sensingsaliency

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  • Extraction of geometrical features using extended morphological attribute profiles (EMAPs) at multiple resolutions.
  • Pixel-based post-classification on EMAPs via hierarchical fuzzy clustering, followed by object formation using simple linear iterative clustering (SLIC) segmentation.
  • Object-based semi-supervised classification using random forest (RF) on a pseudo training set generated from saliency and morphological building index (MBI) on difference images.

Main Results:

  • The proposed method successfully detected most significant building changes in experimental evaluations.
  • Extended Morphological Attribute Profiles (EMAPs) effectively captured structural geometrical features at various scales.
  • The combination of pixel-based and object-based methods, along with RF classification, yielded accurate building change detection results.

Conclusions:

  • The developed automatic approach effectively detects building changes in high-resolution remote sensing data.
  • The integration of EMAPs, SLIC segmentation, and RF classification offers a robust solution for geospatial object change detection.
  • This study demonstrates the effectiveness of hybrid pixel- and object-based methods for monitoring urban environments through remote sensing.