Hyperspectral satellite imagery (HSI) is an advanced technology for object detection because it provides a large amount of information. Thus, the classification of HSIs is very complicated, so the methods of reducing spectral or spatial information generally degrade the quality of classification. In order to solve this problem and guarantee faster and more efficient processing, we propose a smart feature extraction (SFE) and classification by convolutional neural network (2D-CNN) method made up of two parts. The first consists in reducing spectral information by a probabilistic method based on the Softmax function. The second is classification by processing batches of data in the proposed CNN network. The method was tested on two public hyperspectral images (Indian Pines and SalinasA) to prove its effectiveness in increasing classification accuracy and reducing computing time.

中文翻译：

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基于卷积神经网络的高光谱图像智能特征提取与分类

高光谱卫星图像（HSI）是一种用于对象检测的高级技术，因为它提供了大量信息。因此，HSI的分类非常复杂，因此，减少频谱或空间信息的方法通常会降低分类的质量。为了解决该问题并保证更快，更有效的处理，我们提出了一种由两部分组成的智能特征提取（SFE）和卷积神经网络（2D-CNN）分类方法。首先是通过基于Softmax函数的概率方法来减少光谱信息。第二种是通过在建议的CNN网络中处理一批数据进行分类。