Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks
Top Cited Papers
- 25 March 2014
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Geoscience and Remote Sensing Letters
- Vol. 11 (10), 1797-1801
- https://doi.org/10.1109/lgrs.2014.2309695
Abstract
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 max-pooling 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.Keywords
Funding Information
- National Natural Science Foundation of China (91338202)
- National Basic Research Program of China (973 Program) (2012CB316300)
- Strategic Priority Research Program of the Chinese Academy of Sciences (XDA06030300)
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