Convolutional Neural Network Based Automatic Object Detection on Aerial Images

By Igor Sevo, Aleksej Avramovic. 2016

215 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Gong Cheng
2. Junwei Han
3. Xiaoqiang Lu
4. Liangpei Zhang
5. Gui-Song Xia
6. Ke Li
7. Xiaoxiang Zhu
8. Guisong Xia

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Xiaoqiang Lu
4. Liangpei Zhang
5. Xiaoxiang Zhu
6. Gui-Song Xia
7. Ke Li
8. Lichao Mou

Citations in the corpus

• Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, background): “models have shown impressive feature representation capability for a wide range of applications including remote sensing image scene classification [13, 17, 45, 50, 82, 143-158].”
• Clustered Object Detection in Aerial Images (2019, methodology): “In [28], a simple CNNs based approach is presented for automatic detection in aerial images.”
• Learning a Rotation Invariant Detector with Rotatable Bounding Box (2017, methodology): “A CNN based pioneering study upon vehicle detection in satellite images is presented in[2], followed by many approaches that focus on different types of geospatial object detection problems [14, 20, 21, 5, 26, 17].”
• Deep learning in remote sensing: a review (2017, methodology): “Ševo and Avramović [103] suggested a two-stage approach for CNN training and developed an automatic object-detection method based on a pretrained CNN, where the GoogLeNet is first fine-tuned twice on the UC Merced data set using different fine-tuning options and then the fine-tuned model is used for”
• Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks (2017, background): “Sevo and Avramovic [45] proposed a novel two-stage approach for CNN training and implemented a network-based method for automatic content-based object detection on high-resolution aerial images.”
• Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification (2017, unknown): “Let us remark that convolutional neural networks have also been used for other remote sensing applications, such as road tracking [25], object detection [26] and land use classification [27], [28].”
• Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark (2019, methodology): “Driven by the excellent performance of R ‐ CNN for natural scene image object detection, a number of earth observation researchers adopt R ‐ CNN pipeline to detect various geospatial objects in remote sensing images (Cheng et al., 2016a; Long et al., 2017; Salberg, 2015; Ševo and Avramovi ć , 2017).”