Phaneendra Kumar B L N, Vaddi Radhesyam, Manoharan Prabukumar, Agilandeeswari L, Sangeetha V
Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India.
Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College (Deemed to be University), Vijayawada, India.
Sci Rep. 2024 Dec 30;14(1):31836. doi: 10.1038/s41598-024-83118-8.
Dimensionality Reduction (DR) is an indispensable step to enhance classifier accuracy with data redundancy in hyperspectral images (HSI). This paper proposes a framework for DR that combines band selection (BS) and effective spatial features. The conventional clustering methods for BS typically face hard encounters when we have a less data items matched to the dimensionality of the accompanying feature space. So, to fully mine the effective information, BS is established using dual partitioning and ranking. The bands from the dual partitioning have undergone informative band selection via ranking. The reduced band subset is then given to a hemispherical reflectance-based spatial filter. Then, finally, a Convolutional Neural Network (CNN) is used for effective classification by incorporating three-dimensional convolutions. On a set of three hyperspectral datasets - Indian Pines, Salinas, and KSC, the proposed method was tested with different state-of-the-art techniques. The classification results are compared using quantitative and qualitative measures. The reported overall accuracy is 99.92% on Indian Pines, 99.94% on Salinas, and 97.23% on the KSC dataset. Also, the Mean Spectral Divergence values are 42.4, 63.75, and 41.2 on the three datasets respectively, which signifies the effectiveness of band selection. The results have clearly shown the impact of the band selection proposed and can be utilized for a wide variety of applications.
降维（DR）是提高高光谱图像（HSI）中存在数据冗余情况下分类器准确性的不可或缺的一步。本文提出了一种结合波段选择（BS）和有效空间特征的降维框架。传统的波段选择聚类方法在数据项数量与伴随特征空间维度不匹配时通常会遇到困难。因此，为了充分挖掘有效信息，采用双重划分和排序来建立波段选择。双重划分得到的波段通过排序进行信息性波段选择。然后将降维后的波段子集输入基于半球反射率的空间滤波器。最后，使用卷积神经网络（CNN）通过合并三维卷积进行有效分类。在印度松树、萨利纳斯和肯尼迪航天中心这三个高光谱数据集上，该方法与不同的先进技术进行了测试。使用定量和定性指标对分类结果进行了比较。在印度松树数据集上报告的总体准确率为99.92%，在萨利纳斯数据集上为99.94%，在肯尼迪航天中心数据集上为97.23%。此外，这三个数据集的平均光谱散度值分别为42.4、63.75和41.2，这表明了波段选择的有效性。结果清楚地显示了所提出的波段选择的影响，可用于广泛的应用。
