Rotation-Invariant Object Detection in High-Resolution Satellite Imagery Using Superpixel-Based Deep Hough Forests
Rotation-Invariant Object Detection in High-Resolution Satellite Imagery Using Superpixel-Based Deep Hough Forests
复制标题
使用基于超像素的深霍夫森林进行高分辨率卫星图像中的旋转不变目标检测
DOI：
10.1109/lgrs.2015.2432135
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发表时间：
2015-09
影响因子：
4.8
通讯作者：
Zheng Ji
中科院分区：
文献类型：
--
作者：
Yongtao Yu;Haiyan Guan;Zheng Ji
This letter presents a rotation-invariant method for detecting geospatial objects from high-resolution satellite images. First, a superpixel segmentation strategy is proposed to generate meaningful and nonredundant patches. Second, a multilayer deep feature generation model is developed to generate high-level feature representations of patches using deep learning techniques. Third, a set of multiscale Hough forests with embedded patch orientations is constructed to cast rotation-invariant votes for estimating object centroids. Quantitative evaluations on the images collected from Google Earth service show that an average completeness, correctness, quality, and F1- measure values of 0.958, 0.969, 0.929, and 0.963, respectively, are obtained. Comparative studies with three existing methods demonstrate the superior performance of the proposed method in accurately and correctly detecting objects that are arbitrarily oriented and of varying sizes.
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影响因子：
10.6
作者：
Zhicheng Li;L. Itti
通讯作者：
Zhicheng Li;L. Itti
DOI：
10.1016/j.isprsjprs.2008.01.005
发表时间：
2008-07-01
影响因子：
12.7
作者：
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Nelson, Andrew
DOI：
10.1109/tpami.2011.70
发表时间：
2011-11-01
影响因子：
23.6
作者：
Gall, Juergen;Yao, Angela;Lempitsky, Victor
通讯作者：
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影响因子：
2.9
作者：
Salakhutdinov, Ruslan;Hinton, Geoffrey
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影响因子：
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