ABSTRACT

Hyperspectral image (HSI) classification is one of the growing research areas in Remote Sensing. The high dimensionality of the data cube and a high correlation among the hyperspectral bands, poses a difficulty in HSI processing, and selection is one of the promising solutions to address the issue. In this research paper, a whale optimization-based band selection is proposed. Initially, the informative bands are retrieved via a search mechanism which resembles the hunting behaviour of the humpback whales. Later, a hybrid filter is proposed to extract the intrinsic and edge preserving spatial features. Finally, the smoothened bands are trained using a non-linear support vector machine for efficient classification. The exploitation and exploration in the local and global search for the selection of whale hyperspectral bands from the search space proved to be useful in achieving the high-quality classification maps. The overall accuracy reported on the three benchmark data sets – Indian Pines, University of Pavia and Salinas are 99.05, 97.88, and 97.56%, respectively. The proposed method accuracy is achieved with 5% fewer bands on Indian Pines, 70% on University of Pavia and 50% on Salinas Datasets over the other competing methods, which show the effectiveness of the whale optimization-based band selection.

摘要

高光谱图像（HSI）分类是遥感领域中不断发展的研究领域之一。数据立方体的高维数和高光谱带之间的高度相关性给HSI处理带来了困难，选择是解决该问题的有希望的解决方案之一。在本文中，提出了一种基于鲸鱼优化的波段选择方法。最初，信息带是通过类似于座头鲸的狩猎行为的搜索机制获取的。后来，提出了一种混合滤波器来提取固有的和保留边缘的空间特征。最后，使用非线性支持向量机对平滑带进行训练，以进行有效分类。事实证明，在本地和全局搜索中从搜索空间中选择鲸鱼高光谱波段的探索和探索对于实现高质量分类图很有用。在三个基准数据集（印度松树，帕维亚大学和萨利纳斯岛）上报告的总体准确性分别为99.05、97.88和97.56％。与其他竞争方法相比，印度松树上的谱带减少了5％，帕维亚大学上的谱带减少了5％，萨利纳斯数据集上的谱带减少了50％，从而达到了建议的方法准确性，这表明基于鲸鱼优化的谱带选择非常有效。