Feature significance-based multibag-of-visual-words model for remote sensing image scene classification

By Lijun Zhao, Ping Tang, L. Huo. 2016

126 citations recorded by the source corpus.

Subfields: 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 Computer Vision and Pattern Recognition

1. Junwei Han
2. Gong Cheng
3. Guisong Xia
4. Jocelyn Chanussot
5. Lei Guo
6. Nassim Ammour
7. Danfeng Hong
8. Xingxing Xie

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Jocelyn Chanussot
4. Guisong Xia
5. Danfeng Hong
6. Lei Guo
7. Swalpa Kumar Roy
8. Jing Yao

Citations in the corpus

• EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification (2017, background): “create novel datasets, [22], [27], [28], [30] such as the two benchmark datasets PatternNet [37] and NWPU-RESISC45 [7].”
• Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, methodology): “A single feature is insufficient to represent the whole image information, hence a combination of these features is used for image classification [47], [88], [98], [100]–[107].”
• A Review on Deep Learning in UAV Remote Sensing (2021, background): “Still, there are some known aerial repositories with labeled examples, which were presented in recent years, such as the DOTA [197], UAVDT [50], VisDrone [9], WHU-RS19 [171], RSSCN7 [220], RSC11 [209], Brazilian Co ff ee Scene [151] datasets.”
• Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, methodology): “Starting with the UCMerced data set [49], some representative data sets include WHU-RS19 [124], SAT-4&6 [126], RSSCN7 [125], Brazilian Coffee Scene [85], RSC11 [128], SIRI-WHU [127], RSCI-CB [129], AID [79], NWPU-RESISC45 [80], OPTIMAL-31 [74], EuroSAT [130], and BigEarthNet [131].”
• RemoteCLIP: A Vision Language Foundation Model for Remote Sensing (2023, unknown): “We used a total of 12 downstream datasets, including PatternNet [97], EuroSAT [98], OPTIMAL-31 [99], RSC11 [100], AID [101], MLRSNet [102], RSI-CB128 [103], RSI-CB256 [103], RESISC45 [104], WHU-earth [105], WHU-RS19 [106], and RS2800 [107].”
• Continual Learning Using Data Regeneration for Remote Sensing Scene Classification (2022, methodology): “Among these methods, we can find those based on sparse coding [1], midlevel visual elements-oriented features [2], and bag-of-visual words [3].”