Multi‐scale information extraction from high resolution remote sensing imagery and region partition methods based on GMRF–SVM

This paper proposes the work flow of multi‐scale information extraction from high resolution remote sensing images based on features: rough classification – parcel unit extraction (subtle segmentation) – expression of features – intelligent illation – information extraction or target recognition. This paper then analyses its theoretical and practical significance for information extraction from enormous amounts of data on a large scale. Based on the spectrum and texture of images, this paper presents a region partition method for high resolution remote sensing images based on Gaussian Markov Random Field (GMRF)–Support Vector Machine (SVM), that is the image classification based on GMRF–SVM. This method integrates the advantages of GMRF‐based texture classification and SVM‐based pattern recognition with small samples and makes it convenient to utilize a priori knowledge. Finally, the paper reports tests on Ikonos images. The experimental results show that the method used here is superior to GMRF‐based segmentation in terms of both the time expenditure and processing effect. In addition, it is actually meaningful for the stage of information extraction and target recognition.

[1]  H.,et al.  Unsupervised segmentation of textured images using a hierarchical neural structure , 2004 .

[2]  Emmanuel P. Baltsavias,et al.  Object extraction and revision by image analysis using existing geodata and knowledge: current status and steps towards operational systems☆ , 2004 .

[3]  Andrew Blake,et al.  Visual Reconstruction , 1987, Deep Learning for EEG-Based Brain–Computer Interfaces.

[4]  Thomas C. Henderson,et al.  CAGD-Based Computer Vision , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Rama Chellappa,et al.  Unsupervised Texture Segmentation Using Markov Random Field Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Andreu Català,et al.  K-SVCR. A Multi-class Support Vector Machine , 2000, ECML.

[7]  Richard Hoffman,et al.  CAD-driven machine vision , 1989, IEEE Trans. Syst. Man Cybern..

[8]  J FlynnPatrick,et al.  CAD-Based Computer Vision , 1991 .

[9]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[10]  Vladimir Cherkassky,et al.  Learning from Data: Concepts, Theory, and Methods , 1998 .

[11]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[12]  DeLiang Wang,et al.  Texture segmentation using Gaussian-Markov random fields and neural oscillator networks , 2001, IEEE Trans. Neural Networks.

[13]  Vladimir Naumovich Vapni.  The Nature of Statistical Learning Theory , 1995 .

[14]  Thomas M. Strat,et al.  Context-Based Vision: Recognizing Objects Using Information from Both 2D and 3D Imagery , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Bernhard Schölkopf,et al.  Improving the Accuracy and Speed of Support Vector Machines , 1996, NIPS.

[16]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[17]  Zhihua Zhang,et al.  Texture classification by multi-model feature integration using Bayesian networks , 2003, Pattern Recognit. Lett..

[18]  R. Chellappa.  Two-Dimensional Discrete Gaussian Markov Random Field Models for Image Processing , 1989 .

[20]  J. Strobl,et al.  Object-Oriented Image Processing in an Integrated GIS/Remote Sensing Environment and Perspectives for Environmental Applications , 2000 .

[21]  Jake K. Aggarwal,et al.  CAD-based vision: object recognition in cluttered range images using recognition strategies , 1993 .

[22]  Philippe Andrey,et al.  Unsupervised Segmentation of Markov Random Field Modeled Textured Images Using Selectionist Relaxation , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Bernhard Schölkopf,et al.  Comparing support vector machines with Gaussian kernels to radial basis function classifiers , 1997, IEEE Trans. Signal Process..

[24]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .