Abstract
This paper summarizes some preliminary results of applying deep belief network (DBN) to land classification using hyperspectral images. The performance of DBN is then compared with several conventional classification approaches. A fusion approach is also proposed to combine spatial and spectral information in the classification process. Actual hyperspectral image data were used in our investigations. Based on the particular data and experiments, it was found that DBN has slightly better classification performance if only spectral information is used and has slightly inferior performance than a conventional method if both spatial and spectral information are used.
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Ayhan, B., Kwan, C. (2017). Application of Deep Belief Network to Land Cover Classification Using Hyperspectral Images. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_32
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DOI: https://doi.org/10.1007/978-3-319-59072-1_32
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