A coarse-to-fine model for airport detection from remote sensing images using target-oriented visual saliency and CRF

By Xiwen Yao, Junwei Han, Lei Guo, Shuhui Bu, Zhenbao Liu. 2015

87 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. Liangpei Zhang
4. Peicheng Zhou
5. Xiwen Yao
6. Bo Du
7. Xiaoqiang Lu
8. Chao Yao

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Liangpei Zhang
4. Xiwen Yao
5. Peicheng Zhou
6. Bo Du
7. Chao Yao
8. Xiaoqiang Lu

Citations in the corpus

• Semantic Annotation of High-Resolution Satellite Images via Weakly Supervised Learning (2016, unknown): “In contrast, rather than directly using low-level features, shallow unsupervised feature learning methods, such as sparse coding [6], [53] and sparse autoencoders [2], [54], [55], were exploited to learn midlevel features [36] for scene classification or target detection [37].”
• Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images (2016, background): “can be performed by learning a classifier, such as support vector machine (SVM) [1], [7], [8], [12], [13], [20]–[24], AdaBoost [2]–[5], k-nearest neighbors [15], [17], conditional random field [6], [19], and sparse-coding-based classifier [9]–[11], [14], [16], which captures the variation in object ”
• Object detection in VHR optical remote sensing images via learning rotation-invariant HOG feature (2016, background): “More recently, with the advances of machine learning, especially the powerful feature representations and classifiers, many approaches regarded object detection as a classification problem [1-22].”
• Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection (2016, background): “[12] proposed to detect aircraft based on a target-oriented saliency model and discriminative learning of sparse coding.”
• A Survey on Object Detection in Optical Remote Sensing Images (2016, unknown): “…instead of only providing crisp decisions and hence has been applied successfully to various visual recognition tasks such as building detection (Li et al., 2015a; Wegner et al., 2011a; Wegner et al., 2011b), urban area detection (Zhong and Wang, 2007), and airport detection (Yao et al., 2015).”