Deep neural networks-based vehicle detection in satellite images
Abstract
Vehicle detection in satellite images is an Challenging task, but meaningful at the same time. This paper propose a vehicle detection method in satellite images using Deep Convolutional Neural Network(DNN). DNN is a model of deep learning and it has a high learning capacity when dealing with images. DNN consist of several convolution layers and pooling layers, the last layer is full connection with output(this can be considered as neural network). DNN can automatically learn rich features from trainning dataset, and has achieved excellent performance in many applications such as image classification and object recognition. To benefit from this method, we propose a vehicle detection framework. Firstly we use a graph-based superpixel segmentation to extract a set of image patches, which can help us locate vehicle effectively. And then we train a DNN network to classify these pathes into vehicle and non-vehicle. Experimental results indicate that the proposed method has a good performance, with high detection rates and very few false alarms for all test road segment.
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Vehicles can be detected in high-resolution satellite images with high accuracy, and image counts can estimate average vehicle traffic.
“Vehicles can also be detected in high-resolution satellite images with high accuracy [177,390,566,747], and image counts can serve to estimate average vehicle traffic [405].”
From Tackling climate change with machine learning (2023) · cites this paper as [390]
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- Authors
- Jiang, Q., Cao, L., Cheng, M., Wang, C., & Li, J.
- Affiliations
- Xiamen University (CN)
- Venue
- 2015 International Symposium on Bioelectronics and Bioinformatics (ISBB)
- Publisher
- IEEE
- Published
- 2015-10
- Pages
- 184-187
- OpenAlex
- W2181480346
- Cited by
- 45
- References
- 16
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 2 · 2022: 7 · 2021: 4 · 2020: 4 · 2019: 12 · 2018: 4 · 2017: 7 · 2016: 2
- FWCI
- 1.78
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Symposium on Bioelectronics and Bioinformatics (ISBB) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Engineering › Ocean Engineering › Automated Road and Building Extraction
- Keywords
- vehicle detection, satellite imagery, deep convolutional neural networks, road scene analysis, image patch classification, false alarm reduction
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