论文标题
一个新型的空间光谱框架，用于分类高光谱卫星图像
A Novel Spatial-Spectral Framework for the Classification of Hyperspectral Satellite Imagery
论文作者
论文摘要
现在，超光谱卫星图像已被广泛用于准确的灾难预测和地形特征分类。但是，在此类分类任务中，目前的大多数方法仅使用图像中包含的光谱信息。因此，在本文中，我们提出了一个新颖的框架，该框架考虑了数据覆盖分类中包含的光谱和空间信息。为此，我们将高斯最大可能性（GML）和卷积神经网络方法用于像素频谱分类，然后使用分水岭算法生成的分割图，我们将空间上下文信息纳入我们的模型中，并通过修改的主要投票技术将其结合到我们的模型中。对两个基准数据集进行的实验分析表明，我们所提出的方法的表现比早期方法的表现更好，分别在帕维亚大学和印度派恩斯数据集中获得了99.52％和98.31％的精度。此外，我们的基于GML的方法是一种非深度学习算法，显示出与最先进的深度学习技术相当的性能，这表明所提出的方法对执行高光谱图像的计算有效分类的重要性。
Hyper-spectral satellite imagery is now widely being used for accurate disaster prediction and terrain feature classification. However, in such classification tasks, most of the present approaches use only the spectral information contained in the images. Therefore, in this paper, we present a novel framework that takes into account both the spectral and spatial information contained in the data for land cover classification. For this purpose, we use the Gaussian Maximum Likelihood (GML) and Convolutional Neural Network methods for the pixel-wise spectral classification and then, using segmentation maps generated by the Watershed algorithm, we incorporate the spatial contextual information into our model with a modified majority vote technique. The experimental analyses on two benchmark datasets demonstrate that our proposed methodology performs better than the earlier approaches by achieving an accuracy of 99.52% and 98.31% on the Pavia University and the Indian Pines datasets respectively. Additionally, our GML based approach, a non-deep learning algorithm, shows comparable performance to the state-of-the-art deep learning techniques, which indicates the importance of the proposed approach for performing a computationally efficient classification of hyper-spectral imagery.
