Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine
By Jiexiong Tang, Chenwei Deng, G. Huang, Baojun Zhao. IEEE Transactions on Geoscience and Remote Sensing, 2015
378 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/66fc1552c6fb6608c703baaf499686180593d80e
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Citations in the corpus
- Vessel detection and classification from spaceborne optical images: A literature survey (2018, methodology): “…algorithms (Bi et al., 2010; Ding et al., 2012; Dong et al., 2013; Guang et al., 2011; Ji-yang, Dan, Lu-yuan, Jian, Yan-hua, 2016; S. Li et al., 2016; Lin et al., 2012; Tang et al., 2015; X. Wang et al., 2016; Xiaoyang et al., 2016; Xu and Liu, 2016; Yang et al., 2017, 2014; Yao et al., 2016).”
- Object Detection in 20 Years: A Survey (2019, methodology): “In candidate extraction stage, some frequently used methods include gray value filtering based methods [383, 384], visual saliencybased methods [385–388], wavelet transform based methods [389], anomaly detection based methods [390], etc.”
- A survey of deep neural network architectures and their applications (2017, background): “two shortcomings, the DNNs are combined with the extreme lea rning machines (ELMs) in [150].”
- Trends in extreme learning machines: A review (2015, background): “…Butcher et al. (2013), Sovilj et al. (2010), Wang and Han (2012), Zhang and Wang (2011a, 2011b), Remote sensing Pal et al. (2013), Pal (2009), Tang et al. (2014) five years, we have witnessed its successful applications in text analysis, control system design, chemical process monitor, etc.…”
- Using convolutional features and a sparse autoencoder for land-use scene classification (2016, background): “In another remote-sensing context, Tang et al. (2015) proposed a compressed-domain framework for fast ship detection on space-borne optical imagery.”
- A High Resolution Optical Satellite Image Dataset for Ship Recognition and Some New Baselines (2017, methodology): “The data sources include SPOT-5 (Corbane and Petit, 2008; Corbane et al., 2010; Proia and Page, 2010; Yang et al., 2014; Tang et al., 2015), WorldView-2 (Yokoya and Iwasaki, 2015), Quickbird (Liu et al.”
- Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art (2016, methodology): “A number of these approaches achieved state-ofthe-art performance in target recognition by use of a DL method [30], [48], [49], [52], [56], [93]–[96].”
- Ship Rotated Bounding Box Space for Ship Extraction From High-Resolution Optical Satellite Images With Complex Backgrounds (2016, methodology): “We fix sea pixels by Otsu algorithm in [5].”
- A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification (2017, background): “…so-called “deep learning” methods that 73 represent the state-of-the-art in a variety of domains, including target detection (Chen 74 et al., 2016; Tang et al., 2015), image recognition (Farabet et al., 2013; Krizhevsky et 75 al., 2012) and robotics (Bezak et al., 2014; Lenz et al., 2015; Yu et…”
- Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community (2017, methodology): “Tang et al.291 used a hybrid approach in both feature extraction and machine learning.”
- Deep learning in remote sensing: a review (2017, methodology): “[106] offered a compressed-domain ship detection framework combined with SDA and an extreme learning machine (ELM) [107] for optical spaceborne images.”