Abstract: In recent years, deep learning methods have gradually come to be used in hyperspectral imaging domains. Because of the peculiarity of hyperspectral imaging, a mass of information is contained in the spectral dimensions of hyperspectral images. Also, different objects on a land surface are sensitive to different ranges of wavelength. To achieve higher accuracy in classification, we propose a structure that combines spectral sensitivity with a convolutional neural network by adding spectral weights derived from predicted outcomes before the final classification layer. First, samples are divided into visible light and infrared, with a portion of the samples fed into networks during training. Then, two key parameters, unrecognized rate (δ) and wrongly recognized rate (γ), are calculated from the predicted outcome of the whole scene. Next, the spectral weight, derived from these two parameters, is calculated. Finally, the spectral weight is added and an improved structure is constructed. The improved structure not only combines the features in spatial and spectral dimensions, but also gives spectral sensitivity a primary status. Compared with inputs from the whole spectrum, the improved structure attains a nearly 2% higher prediction accuracy. When applied to public data sets, compared with the whole spectrum, on the average we achieve approximately 1% higher accuracy.

Key words: Hyperspectral imaging; Deep learning; Convolutional neural network (CNN); Spectral sensitivity

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