22 August 2017 Spectral–spatial feature learning for hyperspectral imagery classification using deep stacked sparse autoencoder
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J. of Applied Remote Sensing, 11(4), 042604 (2017). doi:10.1117/1.JRS.11.042604
Abstract
Classification of hyperspectral remote sensing imagery is one of the most popular topics because of its intrinsic potential to gather spectral signatures of materials and provides distinct abilities to object detection and recognition. In the last decade, an enormous number of methods were suggested to classify hyperspectral remote sensing data using spectral features, though some are not using all information and lead to poor classification accuracy; on the other hand, the exploration of deep features is recently considered a lot and has turned into a research hot spot in the geoscience and remote sensing research community to enhance classification accuracy. A deep learning architecture is proposed to classify hyperspectral remote sensing imagery by joint utilization of spectral–spatial information. A stacked sparse autoencoder provides unsupervised feature learning to extract high-level feature representations of joint spectral–spatial information; then, a soft classifier is employed to train high-level features and to fine-tune the deep learning architecture. Comparative experiments are performed on two widely used hyperspectral remote sensing data (Salinas and PaviaU) and a coarse resolution hyperspectral data in the long-wave infrared range. The obtained results indicate the superiority of the proposed spectral–spatial deep learning architecture against the conventional classification methods.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Ghasem Abdi, Farhad Samadzadegan, Peter Reinartz, "Spectral–spatial feature learning for hyperspectral imagery classification using deep stacked sparse autoencoder," Journal of Applied Remote Sensing 11(4), 042604 (22 August 2017). http://dx.doi.org/10.1117/1.JRS.11.042604 Submission: Received 1 May 2017; Accepted 26 July 2017
Submission: Received 1 May 2017; Accepted 26 July 2017
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KEYWORDS
Hyperspectral imaging

Image classification

Data modeling

Remote sensing

Long wavelength infrared

Principal component analysis

Error analysis

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