Journal
REMOTE SENSING LETTERS
Volume 4, Issue 12, Pages 1204-1213Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/2150704X.2013.858843
Keywords
-
Funding
- National Natural Science Foundation of China [41371344]
- Foundation for the Author of National Excellent Doctoral Dissertation of P.R. China (FANEDD) [201052]
Ask authors/readers for more resources
In order to capture the high-level concepts in high spatial resolution (HSR) remote sensing imagery, scene classification based on a latent Dirichlet allocation (LDA) model, a generative topic model, is a practical method to bridge the semantic gaps between the low-level features and the high-level concepts of HSR imagery. In the previous work, LDA has been considered as a scene classifier, namely C-LDA, and multiple LDA models for each scene class are built separately, where the scene class is determined by a maximum likelihood rule. The C-LDA strategy disregards the correlations between the generative topic spaces of the different scene classes. In this letter, two novel strategies of scene classification based on LDA are proposed to consider the correlations between the generative topic spaces of the different scene classes by sharing the topic spaces for all the scene classes. One of the proposed strategies utilizes LDA as part of the classifier, namely P-LDA, which generates the topic space from all the training images. A discriminative classifier (e.g., support vector machine, SVM) is also employed as the other classification part of P-LDA. The other proposed strategy employs LDA as the topic feature extractor, namely F-LDA, which generates the topic space from all the training and test images, and utilizes a discriminative classifier to classify the topic features. The experimental results using aerial orthophotographs show that the performances of the two proposed strategies for scene classification based on LDA are better than the traditional C-LDA method.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available