Gravitational self-organizing map-based seismic image classification with an adaptive spectral-textural descriptor

Yanling Hao; Genyun Sun

doi:10.1117/12.2241272

18 October 2016 Gravitational self-organizing map-based seismic image classification with an adaptive spectral-textural descriptor

Yanling Hao, Genyun Sun

Author Affiliations +

Proceedings Volume 10004, Image and Signal Processing for Remote Sensing XXII; 100041X (2016) https://doi.org/10.1117/12.2241272
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom

Abstract

Seismic image classification is of vital importance for extracting damage information and evaluating disaster losses. With the increasing availability of high resolution remote sensing images, automatic image classification offers a unique opportunity to accommodate the rapid damage mapping requirements. However, the diversity of disaster types and the lack of uniform statistical characteristics in seismic images increase the complexity of automated image classification. This paper presents a novel automatic seismic image classification approach by integrating an adaptive spectral-textural descriptor into gravitational self-organizing map (gSOM). In this approach, seismic image is first segmented into several objects based on mean shift (MS) method. These objects are then characterized explicitly by spectral and textural feature quantization histograms. To objectify the image object delineation adapt to various disaster types, an adaptive spectral-textural descriptor is developed by integrating the histograms automatically. Subsequently, these objects as classification units are represented by neurons in a self-organizing map and clustered by adjacency gravitation. By moving the neurons around the gravitational space and merging them according to the gravitation, the object-based gSOM is able to find arbitrary shape and determine the class number automatically. Taking advantage of the diversity of gSOM results, consensus function is then conducted to discover the most suitable classification result. To confirm the validity of the presented approach, three aerial seismic images in Wenchuan covering several disaster types are utilized. The obtained quantitative and qualitative experimental results demonstrated the feasibility and accuracy of the proposed seismic image classification method.

Citation Download Citation

Yanling Hao and Genyun Sun "Gravitational self-organizing map-based seismic image classification with an adaptive spectral-textural descriptor", Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100041X (18 October 2016); https://doi.org/10.1117/12.2241272

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