Abstract
Hyperspectral Satellite Images (HSI) presents a very interesting technology for mapping, environmental protection, and security. HSI is very rich in spectral and spatial characteristics, which are non-linear and highly correlated which makes classification difficult. In this paper, we propose a new approach to the reduction and classification of HSI. This deep approach consisting of a dual Convolutional Neural Networks (DCNN), which aims to improve precision and computing time. This approach involves two main steps; the first is to extract the spectral data and reduce it by CNN until a single value representing the active pixel is displayed. The second consists in classifying the only remaining spatial band on CNN until the class of each pixel is obtained. The tests were applied to three different hyperspectral data sets and showed the effectiveness of the proposed method.
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Notes
- 1.
Batch: Group of pixels containing the active pixel surrounded by its spatial neighbors.
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This contribution was supported by the Ministry of Higher Education and Scientific Research of Tunisia.
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Hamouda, M., Bouhlel, M.S. (2020). Dual Convolutional Neural Networks for Hyperspectral Satellite Images Classification (DCNN-HSI). In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_42
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