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

By Chen Chen, Baochang Zhang, Hongjun Su, Wei Li, Lu Wang. 2015

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, methodology): “This dataset has also been widely adopted to evaluate various different scene classification methods [9, 16, 18, 33, 37, 83-87, 97].”
Fusion of Deep Convolutional Neural Networks for Land Cover Classification of High-Resolution Imagery (2017, methodology): “Chen et al. [20] achieved a 93.4% accuracy using their local binary patterns and extreme learning machine technique, while Sheng et al. [21] obtained 94.6% average classiﬁcation accuracy.”
Hierarchical Coding Vectors for Scene Level Land-Use Classification (2016, methodology): “…[35] 82.7 ˘ 1.2 Concentric circle-structured BOVW [2] 86.6 ˘ 0.8 Multifeature concatenation [36] 89.5 ˘ 0.8 Pyramid-of-spatial-relations [3] 89.1 CLBP [27] 85.5 ˘ 1.9 MS-CLBP [27] 90.6 ˘ 1.4 HCV 90.5 ˘ 1.1 Our method 91.8 ˘ 1.3 The comparison results for RSSCN7 database are listed in Table 4.”
Rotation-Insensitive and Context-Augmented Object Detection in Remote Sensing Images (2018, background): “…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 representation…”