用于遥感图像分类的裂脑自编码器色彩空间量化分析

Vladan Stojnić, V. Risojevic
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引用次数: 5

摘要

本文研究了裂脑自编码器的不同参数对遥感场景分类学习图像表征性能的重要性。我们研究了LAB色彩空间的使用,以及使用PCA创建的色彩空间应用于RGB像素值。我们发现这两个空间给出了几乎相等的结果,稍微偏向LAB色彩空间。我们还研究了不同颜色目标量化方法的选择和量化箱数。我们发现,使用k-means聚类进行量化的效果略好于使用均匀量化。我们还表明,即使使用非常少的箱子,也可能只得到稍微差一点的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Color Space Quantization in Split-Brain Autoencoder for Remote Sensing Image Classification
This paper investigates the importance of different parameters of split-brain autoencoder to performance of learned image representations for remote sensing scene classification. We investigate the usage of LAB color space as well as color space created using PCA applied to RGB pixel values. We show that these two spaces give almost equal results, with slight favor towards the LAB color space. We also investigate choices of different quantization methods of color targets and number of quantization bins. We have found that using k-means clustering for quantization works slightly better than using uniform quantization. We also show that even when using really small number of bins it is possible to get only slightly worse results.
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