J
Jingwen Hu
Researcher at Wuhan University
Publications - 11
Citations - 3092
Jingwen Hu is an academic researcher from Wuhan University. The author has contributed to research in topics: Statistical classification & Feature learning. The author has an hindex of 7, co-authored 11 publications receiving 2141 citations.
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Journal ArticleDOI
AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification
Gui-Song Xia,Jingwen Hu,Fan Hu,Baoguang Shi,Xiang Bai,Yanfei Zhong,Liangpei Zhang,Xiaoqiang Lu +7 more
TL;DR: The Aerial Image Data Set (AID) as mentioned in this paper is a large-scale data set for aerial scene classification, which contains more than 10,000 aerial images from remote sensing images.
Journal ArticleDOI
Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery
TL;DR: This paper proposes two scenarios for generating image features via extracting CNN features from different layers and reveals that the features from pre-trained CNNs generalize well to HRRS datasets and are more expressive than the low- and mid-level features.
Journal ArticleDOI
AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification
TL;DR: The Aerial Image data set (AID), a large-scale data set for aerial scene classification, is described to advance the state of the arts in scene classification of remote sensing images and can be served as the baseline results on this benchmark.
Journal ArticleDOI
A Comparative Study of Sampling Analysis in the Scene Classification of Optical High-Spatial Resolution Remote Sensing Imagery
TL;DR: The experimental results obtained on two commonly-used datasets using different feature learning methods show that random sampling can provide comparable and even better performance than all of the saliency-based strategies.
Journal ArticleDOI
Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery
TL;DR: A fast binary coding (FBC) method, inspired by the unsupervised feature learning technique and the binary feature descriptions, to effectively generate efficient discriminative scene representations of HRRS images is proposed.