期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 11, 期 10, 页码 1797-1801出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2014.2309695
关键词
Deep convolutional neural networks (DNNs); hybrid DNNs (HDNNs); remote sensing; vehicle detection
类别
资金
- National Natural Science Foundation of China under Grant [91338202]
- National Basic Research Program of China (973 Program) [2012CB316300]
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDA06030300]
Detecting small objects such as vehicles in satellite images is a difficult problem. Many features (such as histogram of oriented gradient, local binary pattern, scale-invariant feature transform, etc.) have been used to improve the performance of object detection, but mostly in simple environments such as those on roads. Kembhavi et al. proposed that no satisfactory accuracy has been achieved in complex environments such as the City of San Francisco. Deep convolutional neural networks (DNNs) can learn rich features from the training data automatically and has achieved state-of-the-art performance in many image classification databases. Though the DNN has shown robustness to distortion, it only extracts features of the same scale, and hence is insufficient to tolerate large-scale variance of object. In this letter, we present a hybrid DNN (HDNN), by dividing the maps of the last convolutional layer and the maxpooling layer of DNN into multiple blocks of variable receptive field sizes or max-pooling field sizes, to enable the HDNN to extract variable-scale features. Comparative experimental results indicate that our proposed HDNN significantly outperforms the traditional DNN on vehicle detection.
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