Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks
Wuhan University · State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing
Abstract
In this paper, we focus on tackling the problem of automatic accurate localization of detected objects in high-resolution remote sensing images. The two major problems for object localization in remote sensing images caused by the complex context information such images contain are achieving generalizability of the features used to describe objects and achieving accurate object locations. To address these challenges, we propose a new object localization framework, which can be divided into three processes: region proposal, classification, and accurate object localization process. First, a region proposal method is used to generate candidate regions with the aim of detecting all objects of interest within these…
Citation impact
- FWCI
- 20.79
- Percentile
- 100%
- References
- 65
Authors
4- YLYang LongCorresponding
Wuhan University, State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing
- YGYiping Gong
Wuhan University, State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing
- ZXZhifeng Xiao
Wuhan University, State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing
- QLQing Liu
Topics & keywords
- Computer science
- Artificial intelligence
- Robustness (evolution)
- Convolutional neural network
- Pattern recognition (psychology)
- Minimum bounding box
- Feature extraction
- Histogram