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Texture Feature Research of Multi-Spectral Remote Sensing Image

Author: BaiWenLu
Tutor: TangJiangLong
School: Harbin Institute of Technology
Course: Computer Science and Technology
Keywords: Remote sensing Multi-spectral image Texture analysis GLCM Gray level difference joint probability matrix
CLC: TP751
Type: Master's thesis
Year: 2010
Downloads: 125
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Abstract


Remote sensing is aerial photography technology as the basis of an emerging technology developed from the 1960s , is widely used in the major areas of meteorology, surveying and mapping , earth resources and environmental science , as well as agriculture , forestry , geological sciences . Remote sensing image interpretation , remote sensing image data into the information of the target objects , can be divided into artificial visual interpretation and computer automatic interpretation and human-computer interaction is interpreted in three ways . Remote sensing image only been interpreted in order to be applied to different areas . Traditional visual interpretation has been unable to meet the increasingly large amount of remote sensing data , the use of computer - interactive interpretation or automatic interpretation become an inevitable trend . Often used in the field of remote sensing image interpretation spectral features and texture features , but traditional texture analysis methods as well as texture definition are for single-band image , does not apply to multi-spectral image . Therefore , how the characteristics of multi - spectral image texture analysis becomes a very important issue . In this paper, the status quo of the current lack of remote sensing image processing theory , starting from the multi-spectral remote sensing image texture analysis study used GLCM texture analysis methods applicable to multi-spectral image texture analysis methods - gray differential joint probability matrix . Extracted texture features for classification of multi - spectral remote sensing , water body classification experiment with the different landforms of land , water and urban , mountain , etc. , using the nineties developed support vector machine as classifier , verify that the gray level difference joint probability matrix effectiveness in the field of remote sensing classification . The experimental results show that this paper, the texture analysis method than the traditional method of GLCM advantages in multi-spectral remote sensing classification . Further, in the two-dimensional image in the direction to restore the three-dimensional information is made ??the point exploratory Experimental .

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CLC: > Industrial Technology > Automation technology,computer technology > Remote sensing technology > Interpretation, identification and processing of remote sensing images > Image processing methods
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