Dr. Jiayuan Fan Profile

Dr. Jiayuan Fan

at Institute for Infocomm Research

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Author

Publications (4)

Proceedings Article | 18 October 2016 Presentation + Paper

Spectral-spatial hyperspectral image classification using super-pixel-based spatial pyramid representation

Jiayuan Fan, Hui Li Tan, Maria Toomik, Shijian Lu

Proceedings Volume 10004, 100040W (2016) https://doi.org/10.1117/12.2241033

KEYWORDS: Image classification, Hyperspectral imaging, Image sensors, Spatial resolution, Associative arrays, Agriculture, Sensors, Fluctuations and noise, Image resolution, Principal component analysis

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Proceedings Article | 18 October 2016 Paper

Regions-of-interest extraction from remote sensing imageries using visual attention modelling

Hui Li Tan, Jiayuan Fan, Maria Toomik, Shijian Lu

Proceedings Volume 10004, 100040N (2016) https://doi.org/10.1117/12.2240749

KEYWORDS: Remote sensing, Target detection, Data modeling, Ecosystems, Modeling, Lutetium, Visualization, Visual process modeling, Fluctuations and noise, Image processing

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Proceedings Article | 15 October 2015 Paper

Multipath sparse coding for scene classification in very high resolution satellite imagery

Jiayuan Fan, Hui Li Tan, Shijian Lu

Proceedings Volume 9643, 96430S (2015) https://doi.org/10.1117/12.2194494

KEYWORDS: Image compression, Satellites, Remote sensing, RGB color model, Earth observing sensors, Satellite imaging, Image resolution, Spatial resolution, Sensors, Associative arrays

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With the rapid development of various satellite sensors, automatic and advanced scene classification technique is urgently needed to process a huge amount of satellite image data. Recently, a few of research works start to implant the sparse coding for feature learning in aerial scene classification. However, these previous research works use the single-layer sparse coding in their system and their performances are highly related with multiple low-level features, such as scale-invariant feature transform (SIFT) and saliency. Motivated by the importance of feature learning through multiple layers, we propose a new unsupervised feature learning approach for scene classification on very high resolution satellite imagery. The proposed unsupervised feature learning utilizes multipath sparse coding architecture in order to capture multiple aspects of discriminative structures within complex satellite scene images. In addition, the dense low-level features are extracted from the raw satellite data by using different image patches with varying size at different layers, and this approach is not limited to a particularly designed feature descriptors compared with the other related works. The proposed technique has been evaluated on two challenging high-resolution datasets, including the UC Merced dataset containing 21 different aerial scene categories with a 1 foot resolution and the Singapore dataset containing 5 land-use categories with a 0.5m spatial resolution. Experimental results show that it outperforms the state-of-the-art that uses the single-layer sparse coding. The major contributions of this proposed technique include (1) a new unsupervised feature learning approach to generate feature representation for very high-resolution satellite imagery, (2) the first multipath sparse coding that is used for scene classification in very high-resolution satellite imagery, (3) a simple low-level feature descriptor instead of many particularly designed low-level descriptor, such as SIFT descriptors and saliency, (4) evaluation on two satellite image datasets that come from different sensor sources.

Proceedings Article | 15 October 2015 Paper

L-shaped corner detector for rooftop extraction from satellite/aerial imagery

Hui Li Tan, Jiayuan Fan, Shijian Lu

Proceedings Volume 9643, 964318 (2015) https://doi.org/10.1117/12.2194279

KEYWORDS: Image segmentation, Sensors, Corner detection, Satellites, Earth observing sensors, Satellite imaging, Databases, Buildings, Image sensors, Image resolution

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