Advances in hyperspectral remote sensing have instigated multitude of applications for better understanding of our planet through remote data acquisition and observation of natural phenomena such as weather monitoring and prediction to include tornado, wild fires, global warming, etc. For this, data analysis methods that exploit the rich spectral and spatial information in hyperspectral data are often employed to gain insights about the natural phenomenon. This work presents a new deep learning based hyperspectral data analysis framework, which efficiently utilizes both spatial and spectral information present in the data to achieve superior classification performance. Gabor filtering is used for spatial feature extraction in conjunction with sparse random projections for spectral feature extraction and dimensionality reduction. Finally, supervised classification using a 3-D convolutional neural network was employed to perform a volumetric hyperspectral data analysis. Experimental results reveal that the proposed spatial–spectral hyperspectral data analysis frameworks outperform the conventional 2-D convolution neural network-based spectral–spatial feature extraction techniques.

中文翻译：

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用于稳健高光谱影像分类的空间光谱特征提取框架与 3D 卷积神经网络的研究

高光谱遥感的进步激发了多种应用，通过远程数据采集和自然现象观察，如天气监测和预测，包括龙卷风、野火、全球变暖等，从而更好地了解我们的星球。为此，数据分析方法利用高光谱数据中丰富的光谱和空间信息通常被用来深入了解自然现象。这项工作提出了一种新的基于深度学习的高光谱数据分析框架，它有效地利用数据中存在的空间和光谱信息来实现卓越的分类性能。Gabor 滤波结合稀疏随机投影用于空间特征提取，用于光谱特征提取和降维。最后，使用 3-D 卷积神经网络的监督分类被用来执行体积高光谱数据分析。实验结果表明，所提出的空间光谱高光谱数据分析框架优于传统的基于二维卷积神经网络的光谱空间特征提取技术。