Hyperspectral images with very high resolution (VHR-HSI) have become considerably valuable due to their abundant spectral and spatial details. Classification of hyperspectral images (HSIs) is a basic and important pro...
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Hyperspectral images with very high resolution (VHR-HSI) have become considerably valuable due to their abundant spectral and spatial details. Classification of hyperspectral images (HSIs) is a basic and important procedure for diverse applications. However, low interclass spectral variability and high intraclass spectral variability in VHR-HSI, shadows, pedestrians, and low signal-to-noise ratio increase the fuzziness of different categories. To address the known challenges of VHR-HSI classification, an effective classification method based on encoder-decoder architecture is proposed. The proposed algorithm is an object-level contextual convolution neural network based on an improvedresidualnetwork backbone with 3-dconvolution, which fully considers the spatial-spectral and contextual features of HSIs. Two different spatial resolution aerial HSIs are used as experimental data. The results show that the overall accuracy of the proposed method is improved by 7.42% and 18.82%, respectively, compared to the pixelwise convolution neural network anddeepLabv3 algorithm, which is extraordinarily suitable for HSI classification with very high spatial resolution.
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