Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA

By Gong Cheng, Lei Guo, Tian-yun Zhao, Junwei Han, Huihui Li, Jun Fang. 2013

267 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Management, Monitoring, Policy and Law, 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 Computer Vision and Pattern Recognition

1. Junwei Han
2. Gong Cheng
3. Liangpei Zhang
4. Peicheng Zhou
5. Xiwen Yao
6. Lei Guo
7. Yanfei Zhong
8. Gui-Song Xia

In Management, Monitoring, Policy and Law

1. Peicheng Zhou
2. Gong Cheng
3. Junwei Han

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Liangpei Zhang
4. Peicheng Zhou
5. Lei Guo
6. Yanfei Zhong
7. Xiwen Yao
8. Gui-Song Xia

In Remote Sensing

1. Gong Cheng
2. Junwei Han
3. Liangpei Zhang
4. Peicheng Zhou
5. Yanfei Zhong
6. Gui-Song Xia
7. Lijun Zhao
8. Ping Tang

Citations in the corpus

• Scene Classification With Recurrent Attention of VHR Remote Sensing Images (2019, background): “increasingly available and bringing us the opportunity to try more research in military and civilian applications, such as natural disaster detection [3], [4], land-cover/land-use classification [5], [6] geographic space object detection [7], [8], geographic image retrieval [9], [10], urban planning”
• Land-Use Scene Classification in High-Resolution Remote Sensing Images Using Improved Correlatons (2015, methodology): “Recently, the Bag-of-Visual-Words (BoVW) model initially proposed for text categorization has been successfully applied to image classification [4]–[6], natural scene categorization [7]–[9], and land-use scene classification using HRIs [10], [11].”
• Semantic Classification of High-Resolution Remote-Sensing Images Based on Mid-level Features (2016, background): “It has attracted such intensive attention in visual categorization that the model is introduced into many fields due to its good performance and flexibility, such as scene classification [7], [8], object detection [9], and cloud extraction [10].”
• Scene classification of high resolution remote sensing images using convolutional neural networks (2016, background): “However, most of them are based on handcrafted or shallow learning-based features, such as bag-of-visual-words (BOVW) model [23] and its variations [4, 20, 21], sparse coding-based features [12, 19], midlevel visual elements-oriented features [9, 11, 15, 18] and so on.”
• Object detection in VHR optical remote sensing images via learning rotation-invariant HOG feature (2016, background): “More recently, with the advances of machine learning, especially the powerful feature representations and classifiers, many approaches regarded object detection as a classification problem [1-22].”
• Continual Contrastive Learning for Cross-Dataset Scene Classification (2022, background): “The fundamental scene classification task has led to a wide range of applications such as urban planning [1], environmental monitoring [2], disaster detection [3], and object recognition [4,5].”
• Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark (2019, unknown): “…made in the past few years to develop a variety of methods for object detection in optical remote sensing images (Aksoy, 2014; Bai et al., 2014; Cheng et al., 2013a; Cheng and Han, 2016; Cheng et al., 2013b; Cheng et al., 2014; Cheng et al., 2019; Cheng et al., 2016a; Das et al., 2011; Han et…”
• Land-Use Scene Classification Using a Concentric Circle-Structured Multiscale Bag-of-Visual-Words Model (2014, methodology): “Although these variants of the original BOVW model have a good descriptive ability to depict spatial layout information of scene images, they are designed mainly for natural image scene classification, and seldom have they been applied in the field of remote sensing image scene classification, excep”
• Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, methodology): “Usually they are used to construct bag-of-visualwords (BoVW) models [30], [31], [34], [62], [66]–[71] and HOG”
• Scalable multi-class geospatial object detection in high-spatial-resolution remote sensing images (2014, methodology): “A number of recent works in literatures have proposed various methods for different object detection tasks [1-10].”
• Dirichlet-Derived Multiple Topic Scene Classification Model for High Spatial Resolution Remote Sensing Imagery (2016, methodology): “(PTM) [4], [13]–[15] are commonly used to mine the high-level”