Deep learning framework aids the researchers in learning different application areas to a greater extent. Deep learning framework is preferred over machine learning since it helps to learn the input from end to end, whereas latter one require the inputs to be cut into pieces according to the need. This paper proposes a Multi-Variant Deep Learning framework for learning and classifying the hyperspectral images. Multi-task Feature Leverage is incorporated by doing two-ordered feature extraction. The first order feature extraction was done by using Two Dimensional Empirical Wavelet Transforms (2D-EWT) and the second-order feature extraction was done by using Stacked Autoencoder (SAE) and Convolutional Neural Network (CNN) for the approximation image of 2D-EWT. Because of the possibility of working on prominent feature, the proposed work uses approximation image than the raw image. The classification was carried out by using Random Forest (RF), Multi- Support Vector Machine (MSVM) and Extreme learning machine (ELM).

深度学习框架可帮助研究人员更大程度地学习不同的应用领域。深度学习框架比机器学习更受青睐，因为它有助于从头到尾学习输入，而深度学习框架则要求根据需要将输入切成小块。本文提出了一种多变量深度学习框架，用于学习和分类高光谱图像。通过执行二阶特征提取来合并多任务特征杠杆。使用二维经验小波变换（2D-EWT）完成一阶特征提取，使用堆叠自动编码器（SAE）和卷积神经网络（CNN）完成2D-EWT近似图像的二阶特征提取。 。由于可以进行突出功能，建议的工作使用近似图像而不是原始图像。使用随机森林（RF），多支持向量机（MSVM）和极限学习机（ELM）进行分类。