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 improved residual network backbone with 3-D convolution, 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 and DeepLabv3 algorithm, which is extraordinarily suitable for HSI classification with very high spatial resolution.

中文翻译：

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一种基于编码器-解码器架构的超高分辨率超光谱图像有效分类方法

具有超高分辨率 (VHR-HSI) 的高光谱图像由于其丰富的光谱和空间细节而变得非常有价值。高光谱图像 (HSI) 分类是各种应用的基本且重要的过程。然而，VHR-HSI、阴影、行人和低信噪比中的低类间光谱可变性和高类内光谱可变性增加了不同类别的模糊性。为了解决 VHR-HSI 分类的已知挑战，提出了一种基于编码器 - 解码器架构的有效分类方法。所提出的算法是一种基于具有 3-D 卷积的改进残差网络主干的对象级上下文卷积神经网络，它充分考虑了 HSI 的空间光谱和上下文特征。两种不同空间分辨率的空中 HSI 用作实验数据。结果表明，与像素级卷积神经网络和DeepLabv3算法相比，所提方法的总体准确率分别提高了7.42%和18.82%，非常适合空间分辨率非常高的HSI分类。