Object Detection in High Resolution Remote Sensing Imagery Based on Convolutional Neural Networks With Suitable Object Scale Features

Object detection in high spatial resolution remote sensing images (HSRIs) is an important part of image information automatic extraction, analysis, and understanding. The region of interest (ROI) scale of object detection and the object feature representation are two vital factors in HSRI object det...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2020-03, Vol.58 (3), p.2104-2114
Main Authors: Dong, Zhipeng, Wang, Mi, Wang, Yanli, Zhu, Ying, Zhang, Zhiqi
Format: Article
Language:English
Subjects:
Airplanes
Artificial neural networks
Convolutional neural network (CNN)
deep learning
Detection
Feature extraction
Frameworks
High resolution
high-resolution remote sensing image
Image detection
Image processing
Image resolution
Imagery
Marine vehicles
Meteorological satellites
Neural networks
Object detection
Object recognition
object scare
Product development
Proposals
Remote sensing
Resolution
Spatial resolution
Statistical analysis
Statistical methods
Telematics
Training
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Summary:Object detection in high spatial resolution remote sensing images (HSRIs) is an important part of image information automatic extraction, analysis, and understanding. The region of interest (ROI) scale of object detection and the object feature representation are two vital factors in HSRI object detection. With respect to these two issues, this article presents a novel HSRI object detection method based on convolutional neural networks (CNNs) with suitable object scale features. First, the suitable ROI scale of object detection is obtained by compiling statistics for the scale range of objects in HSRIs. Then, a CNN framework for object detection in HSRIs is designed using a suitable ROI scale of object detection. The object features obtained using a CNN have good universality and robustness. Finally, a CNN framework with a suitable ROI scale of object detection is trained and tested. Using the WHU-RSONE data set, the proposed method is compared with the faster region-based CNN (Faster-RCNN) framework. The experimental results show that the proposed method outperforms the Faster-RCNN framework and provides good object detection results in HSRIs.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2019.2953119