Unsupervised Multilayer Feature Learning for Satellite Image Scene Classification

By Yansheng Li, Chao Tao, Yihua Tan, Ke Shang, Jinwen Tian. 2016

138 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. Chenlin Meng
2. Stefano Ermon
3. Chao Tao
4. Zhenfeng Shao
5. Shawn D. Newsam
6. Yansheng Li
7. Weixun Zhou
8. Yongjun Zhang

In Media Technology

1. Yongjun Zhang
2. Chao Tao
3. Yansheng Li
4. Zhenfeng Shao
5. Weixun Zhou
6. Hu Zhu
7. Congmin Li
8. Grant J. Scott

Citations in the corpus

• Fusion of Deep Convolutional Neural Networks for Land Cover Classification of High-Resolution Imagery (2017, methodology): “Li et al. [17] obtained a 94.3% classification accuracy using K -means clustering to train feature extractors and then K -nearest neighbors for classification.”
• Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion (2016, methodology): “As depicted in [14], the more bases that the intermediate feature layers have, the better the performance of the generated feature.”
• PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval (2017, background): “…success of deep learning, the remote sensing community has realized the potential of applying deep learning techniques for remote sensing tasks such as scene classification [34–38], object detection [39–41], semantic segmentation [42,43], image super-resolution [44] and image retrieval [45–49].”
• Training Deep Convolutional Neural Networks for Land–Cover Classification of High-Resolution Imagery (2017, methodology): “[13] applied unsupervised multilayer feature learning using two layers of FE and K -means clustering to train a deep neural network and then performed K -nearest neighbor classification.”
• Self-Supervised Learning in Remote Sensing: A review (2022, background): “, in scene classification [38], [40], [41]; semantic segmentation [42]–[44]; change detection [45], [46]; and multisensor data analysis [47], [48].”
• Geography-Aware Self-Supervised Learning (2020, background): “Most of the studies utilize small-scale datasets specific to a small geographical region [3, 16, 29, 14, 18], a few classes [24] or a highly-specific modality, i.”