Modeling and Detection of Geospatial Objects Using Texture Motifs

By S. Bhagavathy, Bangalore S. Manjunath. 2006

105 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, 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. Gong Cheng
2. Junwei Han
3. Peicheng Zhou
4. Liangpei Zhang
5. Lei Guo
6. Xiwen Yao
7. Xiaoqiang Lu
8. Gui-Song Xia

In Media Technology

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

In Remote Sensing

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

Citations in the corpus

• Object Classification of Aerial Images With Bag-of-Visual Words (2010, methodology): “Texture motifs are used for modeling and detecting compound geospatial objects by manual annotation [5].”
• Strathprints Institutional Repository Shuhui and Ren, Jinchang (2015) Effective and Efficient Midlevel Visual Elements-oriented Land-use Classification Using Vhr Remote Sensing Images. Ieee Transactions on Geoscience and Remote Sensing, 53 (8). Pp. 4238-4249. Issn 0196-2892 Effective and Efficient M (unknown): “Most previous works [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] mainly focus on classifying pixels or objects (or rather, grouping of local homogeneous pixels) into their thematic class by extracting low-level image features (e.g. texture feature [9] [10], color feature [8], spatial and…”
• Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding (2014, background): “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., 20”
• Deep learning in remote sensing: a review (2017, background): “As a key problem in the interpretation of satellite images, it has widespread applications, including object detection [75], [76], change detection [77], urban planning, and land resource management.”
• A coarse-to-fine model for airport detection from remote sensing images using target-oriented visual saliency and CRF (2015, methodology): “In [15], a method is proposed to learn a texture-motif model from object examples to perform object detection.”
• Urban-Area and Building Detection Using SIFT Keypoints and Graph Theory (2009, background): “Bhagavathy and Manjunath [2] used texture motifs for modeling and detecting regions (such as golf parks, harbors) in satellite images.”
• Bag of Lines (BoL) for Improved Aerial Scene Representation (2015, background): “texture motifs [5] have been extensively studied earlier for representing aerial scenes, these techniques do not directly address the variations in the spatial resolution of the aerial imagery.”
• Scene Classification With Recurrent Attention of VHR Remote Sensing Images (2019, background): “Color histograms and texture descriptors [8], [27] are global features that can be sent to the classifier directly.”
• A Survey on Object Detection in Optical Remote Sensing Images (2016, unknown): “…ship (Bi et al., 2012; Corbane et al., 2010; Shi et al., 2014; Tang et al., 2015; Zhu et al., 2010); tree (Malek et al., 2014); land-use/land-cover (Bhagavathy and Manjunath, 2006; Fauvel et al., 2006; Pacifici et al., 2009; Zhong and Wang, 2007); vehicle (Eikvil et al., 2009; Grabner et al.,…”