Dirichlet-Derived Multiple Topic Scene Classification Model for High Spatial Resolution Remote Sensing Imagery

By Bei Zhao, Yanfei Zhong, Gui-Song Xia, Liangpei Zhang. 2016

322 citations recorded by the source corpus.

Subfields: Atmospheric Science, 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 Atmospheric Science

1. Liangpei Zhang
2. Bo Du
3. Lefei Zhang
4. Yanfei Zhong
5. Liqin Cao
6. Xiaobing Han
7. Antonio J. Plaza
8. Fan Zhang

In Computer Vision and Pattern Recognition

1. Liangpei Zhang
2. Bo Du
3. Yanfei Zhong
4. Lefei Zhang
5. Fan Zhang
6. Ying Li
7. Haokui Zhang
8. Qiang Shen

In Media Technology

1. Liangpei Zhang
2. Bo Du
3. Lefei Zhang
4. Yanfei Zhong
5. Antonio J. Plaza
6. Liqin Cao
7. Xiaobing Han
8. Ying Li

Citations in the corpus

• Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, unknown): “image classification [30], [66], [76], [78]–[84].”
• Large patch convolutional neural networks for the scene classification of high spatial resolution imagery (2016, background): “6 12 – 14 In general, BoVW can be summarized into three major steps: (1) feature learning; (2) feature representation (using the learned feature descriptors); and (3) feature classification.”
• CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification (2020, background): “SIRI-WHU [34] 12 2400 2 Google Earth mainly covers urban areas in China No”
• Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding (2019, background): “Most of the benchmark archives in RS (UC Merced Land Use Dataset [2], WHU-RS19 [3], RSSCN7 [4], SIRI-WHU [5],”
• Deep Unsupervised Embedding for Remotely Sensed Images Based on Spatially Augmented Momentum Contrast (2020, methodology): “This is the case of the works presented in [37], [50], and [51], which make use of probabilistic topic models to represent the RS data as probability distributions of feature patterns.”
• 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].”
• Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection (2016, methodology): “By heavily relying on the human-labeled training examples and manually designed feature descriptors, the aforementioned supervised learning methods can achieve promising performances, when there is a large amount of training data and the feature representation is efficient [14].”
• Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art (2016, methodology): “UnsUPerVised hierarchicaL FeatUre-LearninG-Based methods As indicated in the “General Framework” section, there is some work that focuses on unsupervised feature-learning techniques for RS images scene classification, such as sparse coding [58], k-means clustering [60], [109], and topic model [110],”
• Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, unknown): “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],”
• Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification (2017, methodology): “The Google image dataset of SIRI-WHU covering urban areas in China was collected by the RSIDEA (Intelligent Data Extraction, Analysis and Applications of Remote Sensing) group, LIESMARS, Wuhan University [10,14,47].”
• Deep learning for remote sensing image classification: A survey (2018, background): “…pixel-level image classifications which interprets HSIs with a bottom-up manner, scene classification aims to automatically assign a semantic label to each scene image (Hu et al., 2017; Hu, Xia, Hu, Zhong, & Xu, 2016; Xia et al., 2016; Yang, Yin, & Xia, 2015; Zhao, Zhong, Xia, & Zhang, 2016).”