High-resolution remote-sensing imagery retrieval using sparse features by auto-encoder

By Weixun Zhou, Zhenfeng Shao, C. Diao, Qimin Cheng. 2015

75 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. Gong Cheng
2. Junwei Han
3. Xiwen Yao
4. Lei Guo
5. Peicheng Zhou
6. Chao Tao
7. Yansheng Li
8. Liangpei Zhang

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Xiwen Yao
4. Lei Guo
5. Chao Tao
6. Peicheng Zhou
7. Yansheng Li
8. Zhenfeng Shao

Citations in the corpus

• When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs (2018, methodology): “Typical unsupervised feature learning methods include, but not limited to, principal component analysis, k-means clustering, sparse coding [26]–[28], [33], [44] and autoencoder [29], [31].”
• PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval (2017, background): “Since then, a number of unsupervised feature learning approaches have been proposed for various remote sensing applications like RSIR (Zhou et al., 2015; Wang et al., 2016; Li et al., 2016).”
• Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion (2016, methodology): “As depicted in Table 10, the extraction time of our proposed unsupervised features is longer than that of LIF [2] or UFLF [6].”