A region-based high spatial resolution remotely sensed imagery classification algorithm based on multiscale fusion and feature weighting

Leiguang Wang; Tiancan Mei; Qianqin Qin

doi:10.1117/12.832824

30 October 2009 A region-based high spatial resolution remotely sensed imagery classification algorithm based on multiscale fusion and feature weighting

Leiguang Wang, Tiancan Mei, Qianqin Qin

Author Affiliations +

Proceedings Volume 7494, MIPPR 2009: Multispectral Image Acquisition and Processing; 749411 (2009) https://doi.org/10.1117/12.832824
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

With the availability of high resolution multispectral imagery from sensors, it is possible to identify small-scale features in urban environment. Given attributes of image structure such as color, texture, have the character of highly scale dependency, a hierarchy segment fusion algorithm based on region deviation is proposed to extract more robust features and benefit single semantic level land cover classification. The fusion algorithm proposed is divided into in two successive sub-tasks: mean shift (MS) filtering based pre-segmentation and hierarchical segment optimization. Presegmentation is applied to get boundary- preserved and spectrally homogeneous initial regions, and then, a family of nested image partitions with ascending region areas is constructed by iteratively merging procedure. In every scale, regions of the corresponding critical size are evaluated according to potential region merge risk, which is measured by the region standard deviation change before and after a virtual merge. If a region measurement is larger than a specified change threshold, the region will be preserved to the next level and labeled as a candidate segment for following regionbased classification. Otherwise the segment will be merged to the next scale level. After fusing segments in different scales, a novel weighted minimum distance classifier is employed to get supervised classification result, in which every feature band's deviation is used to calculate its own weight. We show results for classification of a HR image over Washington DC Mall area taken by the HYDICE sensor. Different features combined with designed classifier have proved that fused segments provided a robust feature extraction and improve classification accuracy.

Citation Download Citation

Leiguang Wang, Tiancan Mei, and Qianqin Qin "A region-based high spatial resolution remotely sensed imagery classification algorithm based on multiscale fusion and feature weighting", Proc. SPIE 7494, MIPPR 2009: Multispectral Image Acquisition and Processing, 749411 (30 October 2009); https://doi.org/10.1117/12.832824

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