Journal ArticleDOI
Saliency-Guided Unsupervised Feature Learning for Scene Classification
Fan Zhang,Bo Du,Liangpei Zhang +2 more
TLDR
The proposed unsupervised-feature-learning-based scene classification method provides more accurate classification results than the other latent-Dirichlet-allocation-based methods and the sparse coding method.Abstract:
Due to the rapid technological development of various different satellite sensors, a huge volume of high-resolution image data sets can now be acquired. How to efficiently represent and recognize the scenes from such high-resolution image data has become a critical task. In this paper, we propose an unsupervised feature learning framework for scene classification. By using the saliency detection algorithm, we extract a representative set of patches from the salient regions in the image data set. These unlabeled data patches are exploited by an unsupervised feature learning method to learn a set of feature extractors which are robust and efficient and do not need elaborately designed descriptors such as the scale-invariant-feature-transform-based algorithm. We show that the statistics generated from the learned feature extractors can characterize a complex scene very well and can produce excellent classification accuracy. In order to reduce overfitting in the feature learning step, we further employ a recently developed regularization method called “dropout,” which has proved to be very effective in image classification. In the experiments, the proposed method was applied to two challenging high-resolution data sets: the UC Merced data set containing 21 different aerial scene categories with a submeter resolution and the Sydney data set containing seven land-use categories with a 60-cm spatial resolution. The proposed method obtained results that were equal to or even better than the previous best results with the UC Merced data set, and it also obtained the highest accuracy with the Sydney data set, demonstrating that the proposed unsupervised-feature-learning-based scene classification method provides more accurate classification results than the other latent-Dirichlet-allocation-based methods and the sparse coding method.read more
Citations
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Journal ArticleDOI
Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art
Liangpei Zhang,Lefei Zhang,Bo Du +2 more
TL;DR: A general framework of DL for RS data is provided, and the state-of-the-art DL methods in RS are regarded as special cases of input-output data combined with various deep networks and tuning tricks.
Journal ArticleDOI
Remote Sensing Image Scene Classification: Benchmark and State of the Art
TL;DR: A large-scale data set, termed “NWPU-RESISC45,” is proposed, which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU).
Journal ArticleDOI
Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data
TL;DR: A multilevel DL architecture that targets land cover and crop type classification from multitemporal multisource satellite imagery outperforms the one with MLPs allowing us to better discriminate certain summer crop types.
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.
References
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Proceedings Article
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