Interactive learning and probabilistic retrieval in remote sensing image archives

By Michael Schröder, H. Rehrauer, K. Seidel, Mihai Datcu. 2000

153 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Retrieval & Search, Signal Processing

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. Xiaohua Tong
2. Gong Cheng
3. E. Aptoula
4. Ritendra Datta
5. James Ze Wang
6. L. Zhang
7. Jia Li
8. Dhiraj Joshi

In Computer Vision and Pattern Recognition

1. Junwei Han
2. Gong Cheng
3. Xiaoqiang Lu
4. Jia Li
5. Dacheng Tao
6. James Ze Wang
7. Ritendra Datta
8. Dhiraj Joshi

In Information Retrieval & Search

1. Gong Cheng
2. E. Aptoula
3. Ritendra Datta
4. James Ze Wang
5. L. Zhang
6. Jia Li
7. Dhiraj Joshi
8. Junwei Han

In Signal Processing

1. Gong Cheng
2. E. Aptoula
3. Ritendra Datta
4. James Ze Wang
5. L. Zhang
6. Jia Li
7. Dhiraj Joshi
8. Junwei Han

Citations in the corpus

• Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, background): “wide range of applications, such as natural hazards detection [5-7], LULC determination [8-43], geospatial object detection [27, 44-52], geographic image retrieval [53-63], vegetation mapping [64-68], environment monitoring, and urban planning.”
• Remote Sensing Image Retrieval With Global Morphological Texture Descriptors (2014, methodology): “[28] have developed a probabilistic retrieval scheme based on interactive learning, while Ferecatu and Boujemaa [29] have developed an active relevance feedback solution based on support vector machines, using weighted histograms as descriptors.”
• A Three-Layered Graph-Based Learning Approach for Remote Sensing Image Retrieval (2016, background): “[38] developed a probabilistic retrieval methodology based on interactive learning.”
• Image retrieval: Ideas, influences, and trends of the new age (2008, background): “It is also interesting to note that CBIR technology is being applied to domains as diverse as family album management, Botany, Astronomy, Mineralogy, and Remote sensing [Zhang et al. 2003; Wang et al. 2002; Csillaghy et al. 2000; Painter et al. 2003; Schroder et al. 2000].”
• Content-based image retrieval: approaches and trends of the new age (2005, unknown): “For brevity of space we are unable to discuss them in details, but it is interesting to note that CBIR has been applied to fields as diverse as Botany, Astronomy, Mineralogy, and Remote sensing [105, 22, 80, 88].”