Saliency and Gist Features for Target Detection in Satellite Images

By Zhicheng Li, Laurent Itti. 2011

185 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, 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. Lei Guo
5. Shuhui Bu
6. Zhenbao Liu
7. Dingwen Zhang
8. Xiwen Yao

In Electrical and Electronic Engineering

1. Harm Greidanus
2. Gong Cheng
3. Kristof Ostir
4. Urska Kanjir
5. Junwei Han

In Remote Sensing

1. Gong Cheng
2. Peicheng Zhou
3. Junwei Han
4. Lei Guo
5. Harm Greidanus
6. Kristof Ostir
7. Dingwen Zhang
8. Urska Kanjir

Citations in the corpus

• A Survey on Object Detection in Optical Remote Sensing Images (2016, unknown): “…airplane, ship, storage tank, etc.) (Bai et al., 2014; Cheng et al., 2013b; Cheng et al., 2014a, b; Han et al., 2015; Han et al., 2014; Li and Itti, 2011; Yokoya and Iwasaki, 2015; Zhang et al., 2015a; Zhou et al., 2015a); road (Das et al., 2011; Huang and Zhang, 2009; Inglada, 2007;…”
• Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding (2014, methodology): “…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., 2012; Tao et al., 2011), Kernel-Based Density Estimation (Sirmaçek and Ünsalan, 2011), Gaussian Mixture Models…”
• Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning (2015, unknown): “In recent years, it is also employed for the analysis in the domain of remote sensing [31, 32].”
• Ieee Geoscience and Remote Sensing Letters Fusion of Global and Local Descriptors for Remote Sensing Image Classification (unknown): “…between Gabor coefficients at different scales and the same orientation have been shown to correspond to center-surround organization (opponency) of the cells in human retina and used for texture [12] and satellite image classification [13], as well as target detection in satellite images [14].”
• Vessel detection and classification from spaceborne optical images: A literature survey (2018, methodology): “The most often used method for vessel classification (Table 2) is support vector machine (SVM) – a supervised classification model (Bi et al., 2010, 2012; Guo and Zhu, 2012; Kumar and Selvi, 2011; Li and Itti, 2011; Satyanarayana and Aparna, 2012; Song et al., 2014; Xia et al., 2011).”