Classification of very high spatial resolution imagery based on a new pixel shape feature set

Hua Zhang, Wen Zhong Shi, Yunjia Wang, Ming Hao, Zelang Miao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)

Abstract

This letter presents a novel spatial features extraction method for the high spatial resolution multispectral imagery (HSRMI) classification. First, Canny filter algorithm is applied to extract the edge information to obtain the fuzzy edge map. Secondly, adaptive threshold value for each pixel's homogeneous region (PHR) calculation is determined based on the fuzzy edge map and original image. Next, the PHR for every pixel is obtained based on the fuzzy edge map, adaptive threshold value and original image. And then, the pixel shape feature set (PSFS) is extracted based on the PHR. Lastly, SVM classifier is applied to classify the hybrid spectral and PSFS. Two different experiments were performed to evaluate the performance of PSFS, in comparison with spectral, gray level co-occurrence matrix (GLCM) and the existing pixel shape index (PSI). Experimental results indicate that the PSFS achieved the highest accuracy, hence, providing an effective spectral-spatial classification method for the HSRMI.
Original languageEnglish
Article number6627928
Pages (from-to)940-944
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume11
Issue number5
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

  • Classification
  • High spatial resolution multispectral imagery (HSRMI)
  • Pixel shape feature set (PSFS)
  • Spatial feature extraction

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering

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