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.