Abstract Aiming at solving the problem of image size limiting in the traditional Random Projection (RP) algorithm, a novel Tighter Random Projection (TRP), which combines the scheme with Minimal Intra-class Variance (TRP-MIV) for hyperspectral remote sensing image classification is proposed. First, a new tighter dimensional boundary for expanding image size with the TRP-MIV matrix selected by multiple sampling for improving the class separability is defined to reduce dimension. Then the proposed algorithm is implemented, which integrates TRP-MIV for dimensionality reduction and Minimum Distance (MD) classifier for image classification. Finally, the image size and dimensionality reduction are evaluated by the number of spectral pixels under different theorems, and the spectral difference before and after dimensionality reduction, respectively. Classification performance is evaluated by kappa coefficient, Overall Accuracy (OA), Average Accuracy (AA), Average Precision Rate (APR) and running time. Classification results are obtained from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) scanner and the Reflective Optics System Imaging Spectrometer (ROSIS) scanner, which indicate that the proposed algorithm is efficient and promising.

中文翻译：

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基于最小类内方差算法的更紧随机投影的高光谱遥感图像分类

摘要 针对传统随机投影（RP）算法中图像尺寸限制的问题，提出了一种新的紧随机投影（TRP）方案，该方案与最小类内方差（TRP-MIV）相结合，用于高光谱遥感图像分类。被提议。首先，定义了一个新的更紧密的维度边界，用于通过多次采样选择的 TRP-MIV 矩阵来扩展图像大小以提高类可分离性，以减少维度。然后实现了所提出的算法，该算法集成了用于降维的 TRP-MIV 和用于图像分类的最小距离 (MD) 分类器。最后，图像大小和降维分别通过不同定理下的光谱像素数和降维前后的光谱差异来评估。分类性能通过 kappa 系数、总体准确率 (OA)、平均准确率 (AA)、平均准确率 (APR) 和运行时间进行评估。从机载可见光/红外成像光谱仪（AVIRIS）扫描仪和反射光学系统成像光谱仪（ROSIS）扫描仪获得分类结果，表明所提出的算法是有效和有前途的。