Application of texture analysis in coastal object classification

Jun Fu; Dongqi Gu; Huiliang Yang

doi:10.1117/12.902009

23 November 2011 Application of texture analysis in coastal object classification

Jun Fu, Dongqi Gu, Huiliang Yang

Proceedings Volume 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 800612 (2011) https://doi.org/10.1117/12.902009
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

Abstract

Texture feature of image is one of the most important factors in the processing of information extraction from satellite scene image. In this paper the texture feature analysis was introduced in the processing of the classification of the objects in coastal zone. During the texture analysis process, how to extract effectively the texture features is the key factor. In the experiment of coastal classification, this paper introduced a method of a set of texture features selection based on step-by-step discriminance. Texture is described by Gray level co-occurrence matrix in this study, and there are 192 texture features to describe the characteristics of coastal objects. With the features selection method presented by this paper, five values were chosen as the representatives to classify the object texture feature. By means of the neural networks the object classification mode based on the texture features was defined and the object classifications of the southern coast of Laizhou Bay were carried out. Results show the step-by-step discriminance not only can decrease the dimension of the texture feature database, but also ensure and improve the accuracy of the classification, and the classification accuracy was up to 83.4%. The neural networks mode is the most effective method to account for the classification of the typical objects in coastal zone.

Citation Download Citation

Jun Fu, Dongqi Gu, and Huiliang Yang "Application of texture analysis in coastal object classification", Proc. SPIE 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 800612 (23 November 2011); https://doi.org/10.1117/12.902009

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available