Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding

By Junwei Han, Peicheng Zhou, Dingwen Zhang, Gong Cheng, Lei Guo, Zhenbao Liu, Shuhui Bu, Jun Wu. 2014

215 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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. Xiaoqiang Lu
5. Liangpei Zhang
6. Xiwen Yao
7. Lei Guo
8. Yuan Yuan

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Peicheng Zhou
4. Xiaoqiang Lu
5. Liangpei Zhang
6. Xiwen Yao
7. Lei Guo
8. Yuan Yuan

Citations in the corpus

• Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, background): “The coefficient vectors [26, 33, 37, 95, 128] or reconstruction residuals [20, 47] for each sample form their new feature representations.”
• Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping (2015, methodology): “For a given RSI, the candidate windows can be obtained by saliency-based self-adaptive segmentation method following previous work (Han et al. 2014; Zhang et al. 2015).”
• High-resolution remote-sensing imagery retrieval using sparse features by auto-encoder (2015, unknown): “Some other similar works can be found in Cheng et al. (2015) and Han et al. (2014, 2015).”
• Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning (2015, unknown): “We also compared the proposed WSL approach with several existing supervised learning based object detection methods including a baseline method from Xu [26], and Han ’ s method [3].”
• Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images (2016, methodology): “We therefore apply a nonmaximum suppression strategy as in [1], [7], [16], [22], [42], and [50] to eliminate”
• Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark (2019, unknown): “…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 al., 2015; Han et al., 2014; Li et al., 2018; Long et al., 2017; Tang et al., 2017b; Yang et al., 2017; Zhang et al., 2016; Zhang et al., 2017; Zhou et al.,…”
• When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs (2018, background): “a set of semantic classes according to the image contents, has been attracting more and more research interest driven by its broad applications such as object detection [3]–[5].”
• Cosaliency Detection Based on Intrasaliency Prior Transfer and Deep Intersaliency Mining (2016, background): “Cosaliency detection can serve as a more promising preprocessing step for many high-level visual information understanding tasks, such as video foreground extraction [7], image retrieval [8], object detection [9], [52], and image matching [10].”
• Deep learning in remote sensing: a review (2017, methodology): “Due to the powerful ability of learning high-level (more abstract and semantically meaningful) feature representations, deep CNNs are being explored in object-detection systems in contrast to the more traditional methods followed by a classifier based on handcrafted features [94], [95].”
• Weakly Supervised Learning for Target Detection in Remote Sensing Images (2015, methodology): “The work in [4] applied the saliency model and discriminative sparse coding for multiclass geospatial target detection.”
• Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks (2017, background): “feature transform (SIFT) [1], histogram of oriented gradients (HOG) [2], and Saliency [3], [4].”
• Scalable multi-class geospatial object detection in high-spatial-resolution remote sensing images (2014, unknown): “A number of recent works in literatures have proposed various methods for different object detection tasks [1-10].”
• A Survey on Object Detection in Optical Remote Sensing Images (2016, methodology): “015), hyperspectral image classification (Chen et al., 2011a, 2013; Qian et al., 2013; Tao et al., 2012), and object detection in RSIs (Chen et al., 2011b, c; Cheng et al., 2014c; Du and Zhang, 2014; Han et al., 2014; Liu and Shi, 2014; Yokoya and Iwasaki, 2015; Zhang et al., 2014a; Zhang et al., 20”