NDLI: Sparse coding-based topic model for remote sensing image segmentation

Content Provider	IEEE Xplore Digital Library
Author	Jun Shi Zhiguo Jiang Hao Feng Yibing Ma
Copyright Year	2013
Description	Author affiliation: Image Process. Center, Beijing Univ. of Aeronaut. & Astronaut., Beijing, China (Jun Shi; Zhiguo Jiang; Hao Feng; Yibing Ma)
Abstract	Land cover segmentation can be viewed as topic assignment that the pixels are grouped into homogeneous regions according to different semantic topics in topic model. In this paper, we propose a novel topic model based on sparse coding for segmenting different kinds of land covers. Different from conventional topic models which generally assume each local feature descriptor is related to only one visual word of the codebook, our method utilizes sparse coding to characterize the potential correlation between the descriptor and multiple words. Therefore each descriptor can be represented by a small set of words. Furthermore, in this paper probabilistic Latent Semantic Analysis (pLSA) is applied to learn the latent relation among word, topic and document due to its simplicity and low computational cost. Experimental results on remote sensing image segmentation demonstrate the excellent superiority of our method over k-means clustering and conventional pLSA model.
Sponsorship	IEEE Geosci. Remote Sens. Soc.
Starting Page	4122
Ending Page	4125
File Size	625264
Page Count	4
File Format	PDF
ISBN	9781479911141
ISSN	21536996
DOI	10.1109/IGARSS.2013.6723740
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-21
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Semantics Remote sensing Image segmentation Encoding Image coding Probabilistic logic Image color analysis land cover segmentation remote sensing sparse coding pLSA
Content Type	Text
Resource Type	Article

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