Object detection in remote sensing imagery using a discriminatively trained mixture model

By Gong Cheng, Junwei Han, Lei Guo, Xiaoliang Qian, Peicheng Zhou, Xiwen Yao, Xintao Hu. 2013

164 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. Bo Du

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. Bo Du

Citations in the corpus

• Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, background): “It has shown great success for many scene classification methods [22, 23, 27, 44, 103, 116, 117].”
• Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding (2014, background): “The detection was achieved based on the SVM training.”
• Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection (2016, methodology): “[13] used the HOG feature and a latent SVM to train deformable part-based mixture models for each object category.”
• Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images (2016, methodology): “They are two standard and widely used measures in many object detection works such as those in [1], [7], [8], [16], and [22].”
• Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark (2019, methodology): “…a variety of methods for object detection in optical remote sensing images (Aksoy, 2014; Bai et al., 2014; Cheng et al., 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.,…”
• Object detection in VHR optical remote sensing images via learning rotation-invariant HOG feature (2016, methodology): “We therefore adopt a simple and widely-used non-maximum suppression (NMS) strategy as the work of [1, 3, 4, 6, 7, 9] to eliminate the repeated detections.”
• Learning coarse-to-fine sparselets for efficient object detection and scene classification (2015, background): “In recent years, since the seminal work of [4, 5] and with the success of deformable part models (DPMs) [6, 7], part model-based methods have demonstrated impressive performance for object detection [6-13] and scene classification [14-21].”
• Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping (2015, methodology): “…Machines (SVM) (Cheng et al. 2013b; Sun et al. 2012), k-nearest neighbour (k-NN) (Cheng et al. 2013a), Sparse Coding (Han et al. 2014; Sun et al. 2012; Zhao et al. 2013), etc. Specifically, Cheng et al. (2013b) developed an object detection framework using a discriminatively trained mixture model.”
• A Survey on Object Detection in Optical Remote Sensing Images (2016, unknown): “…Zhang, 2009; Song and Civco, 2004), change detection (Bovolo et al., 2008; De Morsier et al., 2013), multi-class object detection (Bai et al., 2014; Cheng et al., 2013b; Cheng et al., 2014a, b; Li and Itti, 2011), ship detection (Bi et al., 2012; Zhu et al., 2010), airport detection (Tao et al.,…”
• 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].”
• Scene classification of high resolution remote sensing images using convolutional neural networks (2016, background): “Scene classification of high resolution remote sensing images plays an important role for a wide range of applications, such as land-use determination, natural hazards detection, urban planning, geospatial object detection and so on [4-17].”
• Ship Rotated Bounding Box Space for Ship Extraction From High-Resolution Optical Satellite Images With Complex Backgrounds (2016, methodology): “[8] detected objects (including ships) in remote sensing imagery using a discriminatively trained mixture model based on sliding windows.”