Multicriteria classification method for dimensionality reduction adapted to hyperspectral images
Multicriteria classification method for dimensionality reduction adapted to hyperspectral images
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DOI：
10.1117/1.jrs.11.025001
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发表时间：
2017-04-06
影响因子：
1.7
通讯作者：
Younes, Rafic
中科院分区：
文献类型：
--
作者：
Khoder, Mahdi;Kashana, Serge;Younes, Rafic
由于高维数据集的惊人增长，我们解决了对经历不同变化（如噪声退化）敏感的无监督方法和保留稀有信息的问题。因此，研究人员现在被迫开发技术来满足需要的要求。在这项工作中，我们介绍了一种降维方法，该方法主要关注来自多个频段的多幅图像的多目标，从而形成高光谱图像。多标准分类算法技术基于多个相似度标准对这些图像进行比较和分类，从而可以从整个图像集中选择特定的图像。所选图像是在尊重一定质量阈值的情况下用来表示原始数据集的图像。知道高光谱图像中的图像数量表示其维数，选择较少数量的图像来表示数据会导致降维。最后给出了该算法在多幅高光谱图像样本上的测试结果。稍后的比较研究将显示该技术与降维领域中使用的其他常用方法相比的优势。(C) 2017年中国光学仪器工程师学会（SPIE）
Due to the incredible growth of high dimensional datasets, we address the problem of unsupervised methods sensitive to undergoing different variations, such as noise degradation, and to preserving rare information. Therefore, researchers nowadays are forced to develop techniques to meet the needed requirements. In this work, we introduce a dimensionality reduction method that focuses on the multiobjectives of multiple images taken from multiple frequency bands, which form a hyperspectral image. The multicriteria classification algorithm technique compares and classifies these images based on multiple similarity criteria, which allows the selection of particular images from the whole set of images. The selected images are the ones chosen to represent the original set of data while respecting certain quality thresholds. Knowing that the number of images in a hyperspectral image signifies its dimension, choosing a smaller number of images to represent the data leads to dimensionality reduction. Also, results of tests of the developed algorithm on multiple hyperspectral image samples are shown. A comparative study later on will show the advantages of this technique compared to other common methods used in the field of dimensionality reduction. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)