Artificial neural networks (ANNs) have been widely used for the analysis of remotely sensed imagery. In particular, convolutional neural networks (CNNs) are gaining more and more attention in this field. CNNs have proved to be very effective in areas such as image recognition and classification, especially for the classification of large sets composed by two-dimensional images. However, their application to multispectral and hyperspectral images faces some challenges, especially related to the processing of the high-dimensional information contained in multidimensional data cubes. This results in a significant increase in computation time. In this paper, we present a new CNN architecture for the classification of hyperspectral images. The proposed CNN is a 3-D network that uses both spectral and spatial information. It also implements a border mirroring strategy to effectively process border areas in the image, and has been efficiently implemented using graphics processing units (GPUs). Our experimental results indicate that the proposed network performs accurately and efficiently, achieving a reduction of the computation time and increasing the accuracy in the classification of hyperspectral images when compared to other traditional ANN techniques.

人工神经网络（ANN）已被广泛用于遥感影像的分析。特别地，卷积神经网络（CNN）在该领域越来越受到关注。CNN已被证明在图像识别和分类等领域非常有效，特别是对于由二维图像组成的大型集合的分类。然而，它们在多光谱和高光谱图像中的应用面临一些挑战，特别是与多维数据立方体中包含的高维信息的处理有关。这导致计算时间显着增加。在本文中，我们提出了一种用于高光谱图像分类的新CNN体系结构。提出的CNN是使用频谱和空间信息的3-D网络。它还实现了边界镜像策略以有效处理图像中的边界区域，并且已使用图形处理单元（GPU）进行了有效实现。我们的实验结果表明，与其他传统的ANN技术相比，拟议的网络性能准确高效，减少了计算时间，提高了高光谱图像分类的准确性。