Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors

By Longhui Huang, Wei Li, Qian Du. 2016

146 citations recorded by the source corpus.

An effective remote sensing image scene classification approach using patch-based multi-scale completed local binary pattern (MS-CLBP) features and a Fisher vector (FV) is proposed. The approach extracts a set of local patch descriptors by partitioning an image and its multi-scale versions into dense patches and using the CLBP descriptor to characterize local rotation invariant texture information. Then, Fisher vector encoding is used to encode the local patch descriptors (i.e., patch-based CLBP features) into a discriminative representation. To improve the discriminative power of feature representation, multiple sets of parameters are used for CLBP to generate multiple FVs that are concatenated as the final representation for an image. A kernel-based extreme learning machine (KELM) is then employed for classification. The proposed method is extensively evaluated on two public benchmark remote sensing image datasets (i.e., the 21-class land-use dataset and the 19-class satellite scene dataset) and leads to superior classification performance (93.00% for the 21-class dataset with an improvement of approximately 3% when compared with the state-of-the-art MS-CLBP and 94.32% for the 19-class dataset with an improvement of approximately 1%).

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. Danfeng Hong
2. Jocelyn Chanussot
3. Jing Yao
4. Xin Wu
5. Swalpa Kumar Roy
6. Ke Li
7. Gong Cheng
8. Xiong You

In Computer Vision and Pattern Recognition

1. Jocelyn Chanussot
2. Gong Cheng
3. Danfeng Hong
4. Xin Wu
5. Swalpa Kumar Roy
6. Ke Li
7. Jing Yao
8. Shuhui Bu

In Media Technology

1. Danfeng Hong
2. Jocelyn Chanussot
3. Jing Yao
4. Gong Cheng
5. Swalpa Kumar Roy
6. Xin Wu
7. Ke Li
8. Shuhui Bu

Citations in the corpus

• Rotation-Insensitive and Context-Augmented Object Detection in Remote Sensing Images (2018, background): “The probabilistic model-based RBM can handle the problem of missing values and generate samples in a natural way [36].”
• Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, methodology): “Some popular feature encoding or pooling strategies to enhance the performance of BoVW are Fisher vector coding [51], [73], [74], Spatial Pyramid Matching (SPM) [75], and Probabilistic Topic Model (PTM) [71], [76]– [78].”