Boosting With Multiple Clustering Memberships For Hyperspectral Image Classification
Boosting With Multiple Clustering Memberships For Hyperspectral Image Classification
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DOI：
10.1109/southeastcon51012.2023.10115209
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Giovanni Bellio;R. Russell;Olcay Kursun
中科院分区：
文献类型：
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作者：
Giovanni Bellio;R. Russell;Olcay Kursun
提出了一种新的高光谱图像分类算法，并在基准高光谱图像上进行了实验验证。我们还介绍了我们正在收集的用于检测云量和类型的高光谱天空成像数据集。该算法旨在应用于这样的系统，可以提高云信息的空间和时间分辨率，这对了解地球气候至关重要。我们讨论了我们收集的HSI-Cloud数据集的性质，以及我们提出的使用分类提升方法处理数据集的算法。该方法利用多聚类来扩充数据集，实现了较高的像素分类精度。通过聚类创建分类特征丰富了数据表示，并改进了增强集成。在本文使用的实验数据集上，梯度提升算法的性能优于基准算法。
A novel hyperspectral image classification algorithm is proposed and demonstrated on benchmark hyperspectral images. We also introduce a hyperspectral sky imaging dataset that we are collecting for detecting the amount and type of cloudiness. The algorithm is designed to be applied to the Such systems could improve the spatial and temporal resolution of cloud information vital to understanding Earth’s climate. We discuss the nature of our HSI-Cloud dataset being collected and an algorithm we propose for processing the dataset using a categorical-boosting method. The proposed method utilizes multiple clusterings to augment the dataset and achieves higher pixel classification accuracy. Creating categorical features via clustering enriches the data representation and improves boosting ensembles. For the experimental datasets used in this paper, gradient boosting methods performed favorably to the benchmark algorithms.