Next Article in Journal
Autonomous Point Cloud Acquisition of Unknown Indoor Scenes
Previous Article in Journal
Spatial Characterization and Mapping of Gated Communities
Open AccessArticle

Vehicle Object Detection in Remote Sensing Imagery Based on Multi-Perspective Convolutional Neural Network

1
School of Resources and Environmental Engineering, Industrial Safety Engineering Technology Research Center of Hubei Province, Wuhan University of Science and Technology, Wuhan 430081, China
2
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(7), 249; https://doi.org/10.3390/ijgi7070249
Received: 7 March 2018 / Revised: 23 May 2018 / Accepted: 18 June 2018 / Published: 25 June 2018
Most traditional object detection approaches have a deficiency of features, slow detection speed, and high false-alarm rate. To solve these problems, we propose a multi-perspective convolutional neural network (Multi-PerNet) to extract remote sensing imagery features. Regions with CNN features (R-CNN) is a milestone in applying CNN method to object detection. With the help of the great feature extraction and classification performance of CNN, the transformation of object detection problem is realized by the Region Proposal method. Multi-PerNet trains a vehicle object detection model in remote sensing imagery based on Faster R-CNN. During model training, sample images and the labels are inputs, and the output is a detection model. First, Multi-PerNet extracts the feature map. Meanwhile, the area distribution and object-area aspect ratio in the sample images are obtained by k-means clustering. Then, the Faster R-CNN region proposal network generates the candidate windows based on the k-means clustering results. Features of candidate windows can be obtained by mapping candidate windows to the feature map. Finally, the candidate window and its features are inputted to the classifier to be trained to obtain the detection model. Experiment results show that the Multi-PerNet model detection accuracy is improved by 10.1% compared with the model obtained by ZF-net and 1.6% compared with the model obtained by PVANet. Moreover, the model size is reduced by 21.3%. View Full-Text
Keywords: multi-perspective convolutional neural network; object detection; remote sensing imagery; Faster R-CNN multi-perspective convolutional neural network; object detection; remote sensing imagery; Faster R-CNN
Show Figures

Figure 1

MDPI and ACS Style

Yang, C.; Li, W.; Lin, Z. Vehicle Object Detection in Remote Sensing Imagery Based on Multi-Perspective Convolutional Neural Network. ISPRS Int. J. Geo-Inf. 2018, 7, 249.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop