Automatic Annotation of Satellite Images via Multifeature Joint Sparse Coding With Spatial Relation Constraint

By Xinwei Zheng, Xian Sun, Kun Fu, Hongqi Wang. 2013

80 citations recorded by the source corpus.

Subfields: Atmospheric Science, Computer Vision and Pattern Recognition, Media Technology, Remote Sensing

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

1. Liangpei Zhang
2. Qian Du
3. Yanfei Zhong
4. Wei Li
5. Cheng Chen
6. Hongjun Su
7. Xiaoqiang Lu
8. Gui-Song Xia

In Computer Vision and Pattern Recognition

1. Liangpei Zhang
2. Xiaoqiang Lu
3. Yanfei Zhong
4. Gong Cheng
5. Junwei Han
6. Gui-Song Xia
7. Xiang Bai
8. Qian Du

In Media Technology

1. Liangpei Zhang
2. Xiaoqiang Lu
3. Yanfei Zhong
4. Qian Du
5. Gui-Song Xia
6. Gong Cheng
7. Wei Li
8. Xiang Bai

In Remote Sensing

1. Liangpei Zhang
2. Yanfei Zhong
3. Gui-Song Xia
4. Jingwen Hu
5. Fan Hu
6. Xiaoqiang Lu
7. Qian Du
8. Xiang Bai

Citations in the corpus

• Dirichlet-Derived Multiple Topic Scene Classification Model for High Spatial Resolution Remote Sensing Imagery (2016, methodology): “In order to combine multiple features, a method with two cascading classifiers [24], [29] and multifeature joint sparse coding [25] have been proposed to fuse multiple features.”
• Strathprints Institutional Repository Shuhui and Ren, Jinchang (2015) Effective and Efficient Midlevel Visual Elements-oriented Land-use Classification Using Vhr Remote Sensing Images. Ieee Transactions on Geoscience and Remote Sensing, 53 (8). Pp. 4238-4249. Issn 0196-2892 Effective and Efficient M (unknown): “…on pixel or object-level thematic classification, the scene-level land-use classification using very high resolution (VHR) remote sensing images has attracted more and more attentions [3] [4] [5] [6] [7] [19] [20] because of its foundation and importance in automated image content understanding.”
• Deep Feature Fusion for VHR Remote Sensing Scene Classification (2017, background): “in [26] and [27] combined more than three feature descriptors to represent the images.”
• Semantic Classification of High-Resolution Remote-Sensing Images Based on Mid-level Features (2016, methodology): “To further demonstrate the superiority of the mid-level representation, multifeature joint sparse coding (MFJSC) [27], grid-specified frequent local histograms (GRID-FLH) [28], and partlets-based method [29] are compared to MS-BOV.”
• Land-use scene classification using multi-scale completed local binary patterns (2016, unknown): “, [15,16].”
• Scene Classification Based on the Multifeature Fusion Probabilistic Topic Model for High Spatial Resolution Remote Sensing Imagery (2015, methodology): “[37] also used four features and concatenated the quantized vector by k-means clustering for each feature to form a vocabulary.”
• Gabor-Filtering-Based Completed Local Binary Patterns for Land-Use Scene Classification (2015, methodology): “In [11], four types of features consist of DAISY [12], geometric blur [13], SIFT [10], and self-similarity [14] were used within the framework of multifeature joint sparse coding with spatial relation constraint.”
• Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors (2016, methodology): “In [10], four types of features consisting of raw pixel intensity values, oriented filter responses, SIFT-based feature descriptors, and self-similarity were used within the framework of unsupervised feature learning.”
• AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification (2016, methodology): “This data set is widely used for the task of aerial image classification [2]–[4], [7], [8], [10], [13], [15], [16], [18]–[20], [22]–[27], [30], [32]–[39], [58].”