Gabor-Filtering-Based Completed Local Binary Patterns for Land-Use Scene Classification
By Cheng Chen, Libing Zhou, Jianzhong Guo, Wei Li, Hongjun Su, Fangda Guo. 2015
42 citations recorded by the source corpus.
Subfields: Atmospheric Science, 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 Atmospheric Science
In Computer Vision and Pattern Recognition
- Liangpei Zhang
- Yanfei Zhong
- Keiller Nogueira
- Otávio Augusto Bizetto Penatti
- J. A. D. Santos
- Feng Fei
- Gong Cheng
- Junwei Han
In Media Technology
- Liangpei Zhang
- Yanfei Zhong
- Keiller Nogueira
- Otávio Augusto Bizetto Penatti
- J. A. D. Santos
- Feng Fei
- Bo Du
- Gong Cheng
Citations in the corpus
- Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, neutral): “dataset is the most popular and has been widely used for the task of remote sensing image scene classification and retrieval [8, 10, 11, 14-16, 18-20, 24, 26, 28-30, 36, 38, 53, 55, 59, 61, 62, 81, 82, 84-98].”
- Towards better exploiting convolutional neural networks for remote sensing scene classification (2016, methodology): “For the UCMerced dataset, we select three state-of-the-art baselines: (i) GCLBP [71], (ii) WithSal [35], and (iii) Dense-Sift [72].”
- Large patch convolutional neural networks for the scene classification of high spatial resolution imagery (2016, background): “…binary patterns (GCLBP) introduces Gabor-filter-based completed binary patterns (CLBP) to strengthen the exploitation of the spatial information; 29 MS-CLBP introduces multiscale CLBP to build up the representations of the HSR-RS scene with different resolutions; 30 and the hierarchical scheme…”