Object detection in remote sensing imagery using a discriminatively trained mixture model
Object detection in remote sensing imagery using a discriminatively trained mixture model
复制标题
使用有区别训练的混合模型进行遥感图像中的目标检测
DOI：
10.1016/j.isprsjprs.2013.08.001
复制
发表时间：
2013-11-01
影响因子：
12.7
通讯作者：
Hu, Xintao
中科院分区：
文献类型：
--
作者：
Cheng, Gong;Han, Junwei;Hu, Xintao
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.
