Journal
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
Volume 85, Issue -, Pages 32-43Publisher
ELSEVIER
DOI: 10.1016/j.isprsjprs.2013.08.001
Keywords
Object detection; Remote sensing imagery; Part-based model; Mixture model
Categories
Funding
- National Science Foundation of China [61005018, 91120005, 61103061]
- Program for New Century Excellent Talents in University [NCET-10-0079]
- China Postdoctoral Science Foundation [20110490174,]
- China-Postdoctoral Science Foundation [2012T50819]
- [NPU-FFR-JC20120237]
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Automatically detecting objects with complex appearance and arbitrary orientations in remote sensing imagery (RSI) is a big challenge. To explore a possible solution to the problem, this paper develops an object detection framework using a discriminatively trained mixture model. It is mainly composed of two stages: model training and object detection. In the model training stage, multi-scale histogram of oriented gradients (HOG) feature pyramids of all training samples are constructed. A mixture of multi-scale deformable part-based models is then trained for each object category by training a latent Support Vector Machine (SVM), where each part-based model is composed of a coarse root filter, a set of higher resolution part filters, and a set of deformation models. In the object detection stage, given a test imagery, its multi-scale HOG feature pyramid is firstly constructed. Then, object detection is performed by computing and thresholding the response of the mixture model. The quantitative comparisons with state-of-the-art approaches on two datasets demonstrate the effectiveness of the developed framework. (C) 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.
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