Share Email Print

Proceedings Paper

Object-oriented Markov random model for classification of high resolution satellite imagery based on wavelet transform
Author(s): Liang Hong; Cun Liu; Kun Yang; Ming Deng
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The high resolution satellite imagery (HRSI) have higher spatial resolution and less spectrum number, so there are some “object with different spectra, different objects with same spectrum” phenomena. The objective of this paper is to utilize the extracted features of high resolution satellite imagery (HRSI) obtained by the wavelet transform(WT) for segmentation. WT provides the spatial and spectral characteristics of a pixel along with its neighbors. The object-oriented Markov random Model in the wavelet domain is proposed in order to segment high resolution satellite imagery (HRSI). The proposed method is made up of three blocks: (1) WT-based feature extrcation.the aim of extraction of feature using WT for original spectral bands is to exploit the spatial and frequency information of the pixels; (2) over-segmentation object generation. Mean-Shift algorithm is employed to obtain over-segmentation objects; (3) classification based on Object-oriented Markov Random Model. Firstly the object adjacent graph (OAG) can be constructed on the over-segmentation objects. Secondly MRF model is easily defined on the OAG, in which WT-based feature of pixels are modeled in the feature field model and the neighbor system, potential cliques and energy functions of OAG are exploited in the labeling model. Experiments are conducted on one HRSI dataset-QuickBird images. We evaluate and compare the proposed approach with the well-known commercial software eCognition(object-based analysis approach) and Maximum Likelihood(ML) based pixels. Experimental results show that the proposed the method in this paper obviously outperforms the other methods.

Paper Details

Date Published: 19 July 2013
PDF: 6 pages
Proc. SPIE 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013), 887838 (19 July 2013); doi: 10.1117/12.2031103
Show Author Affiliations
Liang Hong, Central South Univ. (China)
Yunnan Normal Univ. (China)
Cun Liu, Yunnan Normal Univ. (China)
Kun Yang, Yunnan Normal Univ. (China)
Ming Deng, Central South Univ. (China)

Published in SPIE Proceedings Vol. 8878:
Fifth International Conference on Digital Image Processing (ICDIP 2013)
Yulin Wang; Xie Yi, Editor(s)

© SPIE. Terms of Use
Back to Top