Land-use scene classification using multi-scale completed local binary patterns

By Baochang Zhang, Hongjun Su, Wei Li. 2016

152 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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 Artificial Intelligence

1. Liangpei Zhang
2. Qian Du
3. Wei Li
4. Anne Humeau-Heurtier
5. Xiang Bai
6. Junwei Han
7. Longhui Huang
8. Yanfei Zhong

In Computer Vision and Pattern Recognition

1. Junwei Han
2. Gong Cheng
3. Liangpei Zhang
4. Xiaoqiang Lu
5. Xiwen Yao
6. Gui-Song Xia
7. Yanfei Zhong
8. Lei Guo

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Liangpei Zhang
4. Xiaoqiang Lu
5. Lei Guo
6. Xiwen Yao
7. Yanfei Zhong
8. Gui-Song Xia

Citations in the corpus

• Scene classification using local and global features with collaborative representation fusion (2016, methodology): “We follow the same experimental setup in [4] .”
• Fusion of Deep Convolutional Neural Networks for Land Cover Classification of High-Resolution Imagery (2017, unknown): “[20] achieved a 93.”
• Large patch convolutional neural networks for the scene classification of high spatial resolution imagery (2016, methodology): “…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 for multiple feature fusion (HMFF) employs a hierarchical multifeature fusion scheme to collect multiple feature maps…”
• AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification (2016, background): “In [39], a multi-scale completed local binary patterns (MS-CLBP) was proposed for land-use scene classification and achieved the state-of-the-art performance among low-level methods.”
• When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs (2018, background): “crafted features, color feature, texture feature [16]–[18], [23], and histogram of oriented gradients-based shape descrip-”
• Hierarchical Coding Vectors for Scene Level Land-Use Classification (2016, methodology): “Although the MS-CLBP described in [27] achieves comparable performance with HCV, the Extreme Learning Machine (ELM) and Radial Basis Function (RBF) nonlinear kernel were used in their approach.”
• Training Deep Convolutional Neural Networks for Land–Cover Classification of High-Resolution Imagery (2017, background): “A few noteworthy recent results include [5] with 90.”
• Rotation-Insensitive and Context-Augmented Object Detection in Remote Sensing Images (2018, background): “opposed to hand-engineered low-level features such as scale invariant feature transform [33], histogram of oriented gradients (HOG) [34], and local binary patterns [35], [36], and there are also some frameworks that try to improve image resolution or combine/fuse several features to improve image re”
• Texture Feature Extraction Methods: A Survey (2019, methodology): “Furthermore, other variants have been developed to improve the discriminative power of the method [85]–[87], including multiscale approaches [88], [89].”
• Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors (2016, methodology): “There are two implementations for the MS-CLBP descriptor [17].”