Urban-Area and Building Detection Using SIFT Keypoints and Graph Theory

By Beril Sirmaçek, Cem Ünsalan. IEEE Transactions on Geoscience and Remote Sensing, 2009

355 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/54155120a8801ed3afcc3bd3d4c6eb0fb4d028c7

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. Peicheng Zhou
4. Lei Guo
5. Shuhui Bu
6. Liangpei Zhang
7. Xiaoqiang Lu
8. Dingwen Zhang

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Peicheng Zhou
4. Lei Guo
5. Shuhui Bu
6. Xiaoqiang Lu
7. Zhenbao Liu
8. Xiwen Yao

In Ocean Engineering

1. Gong Cheng
2. Junwei Han
3. Peicheng Zhou
4. Lei Guo
5. Shuhui Bu
6. Xintao Hu
7. Yann Gousseau
8. Lionel Gueguen

Citations in the corpus

• SAR-SIFT: A SIFT-Like Algorithm for SAR Images (2015, methodology): “Single buildings are detected on very high resolution optical images by Sirmacek and Unsalan [9] by using SIFT keypoints, multiple subgraph matching, and graph cut methods.”
• Classifying Compound Structures in Satellite Images: A Compressed Representation for Fast Queries (2015, methodology): “Given an overlap of 80 m between neighboring tiles, we obtain a classification with a resolution Asacomparison, we experiment the same classification while employing distributions of SIFT features as they were shown in [59] to provide good urban description.”
• PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval (2017, background): “…of local features and a number of novel methods have been proposed for image registration [19–24], change detection [25,26], building and urban region detection [27–29], image classification [30] and remote sensing image retrieval [7,31] These local and global features are hand-crafted though.”
• Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding (2014, methodology): “The establishment of the classifiers can be based on template matching (Akçay and Aksoy, 2008; Li et al., 2010b, 2012; Sirmaçek and Ünsalan, 2009, 2010) or direct application of a variety of sophisticated (SVM) (Bi et al., 2012; Cheng et al., 2013; Li et al., 2010a; Li and Itti, 2011; Sun et al.,…”
• Weakly Supervised Learning for Target Detection in Remote Sensing Images (2015, methodology): “Sirmacek [2] detected urban areas and buildings using the SIFT keypoints and graph theory.”
• Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping (2015, methodology): “Sirmacek and Unsalan (2009) utilized scale invariant feature transform (SIFT) keypoints and graph theory to detect urban areas and buildings.”
• Scale Invariant Feature Transform on the Sphere: Theory and Applications (2011, background): “Thanks to its simplicity, good results in terms of repeatability and accuracy on matching, it has been used to treat applications requiring tracking or matching of regions (Sirmacek and Unsalan 2009; Brox et al. 2010).”