A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Unsupervised Feature Learning for Land-Use Scene Recognition
This paper proposes a novel unsupervised feature learning algorithm for land-use scene recognition on very high resolution remote sensing imagery. The proposed technique utilizes a multipath sparse coding architecture in order to capture multiple aspects of discriminative structures within complex remote sensing sceneries. Unlike the previous sparse coding and bag-of-visual-words-based techniques that rely on the handcrafted feature descriptors such as scale-invariant feature transform, the proposed technique extracts dense low-level features from the raw data, including the visual (RGB) data and near-infrared (NIR) data, using image patches of varying sizes at different layers. The proposed technique has been evaluated on three data sets, including the 21-category UC Merced land-use \texttt {RGB} data set with a 1-ft spatial resolution, the 9-category ground scene RGB-NIR data set, and the 10-category Singapore land-use \texttt {RGB-NIR} data set with a 0.5-m spatial resolution. The experimental results show that the proposed technique outperforms the state-of-the-art methods.
Unsupervised Feature Learning for Land-Use Scene Recognition
This paper proposes a novel unsupervised feature learning algorithm for land-use scene recognition on very high resolution remote sensing imagery. The proposed technique utilizes a multipath sparse coding architecture in order to capture multiple aspects of discriminative structures within complex remote sensing sceneries. Unlike the previous sparse coding and bag-of-visual-words-based techniques that rely on the handcrafted feature descriptors such as scale-invariant feature transform, the proposed technique extracts dense low-level features from the raw data, including the visual (RGB) data and near-infrared (NIR) data, using image patches of varying sizes at different layers. The proposed technique has been evaluated on three data sets, including the 21-category UC Merced land-use \texttt {RGB} data set with a 1-ft spatial resolution, the 9-category ground scene RGB-NIR data set, and the 10-category Singapore land-use \texttt {RGB-NIR} data set with a 0.5-m spatial resolution. The experimental results show that the proposed technique outperforms the state-of-the-art methods.
Unsupervised Feature Learning for Land-Use Scene Recognition
Fan, Jiayuan (author) / Chen, Tao / Lu, Shijian
2017
Article (Journal)
English
Local classification TIB:
770/3710/5670
BKL:
38.03
Methoden und Techniken der Geowissenschaften
/
74.41
Luftaufnahmen, Photogrammetrie
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