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Dr. Ma's paper Accepted!

21 Mar 2023

Congrats Dr. Ma for her new paper accepted in IEEE Transactions on Geoscience and Remote Sensing!

Dr. Ma's paper entitled "Multiscale Superpixelwise Prophet Model for NoiseRobust Feature Extraction in Hyperspectral Image" is accepted in IEEE Transactions on Geoscience and Remote Sensing. This paper was a collaboration of Dr. Ping Ma with Prof. Jinchang Ren, Prof. Genyun Sun, Prof. Huimin Zhao, Dr. Xiuping Jia, Dr. Yijun Yan, and Dr. Jaime Zabalza.


Abstract:

Despite of various approaches proposed to smooth the hyperspectral images (HSIs) before feature extraction, the efficacy is still affected by the noise, even using the corrected dataset with the water absorption bands and noise bands discarded. In this study, a novel spectral-spatial feature mining framework, Multiscale Superpixelwise Prophet Model (MSPM), is proposed for noise-robust feature extraction and effective classification of the HSI. The prophet model is highly noise-robust for deeply digging into the complex structured features thus enlarging interclass diversity and improving intraclass similarity. First, a pseudo color image is generated from the first three principal components of an HSI, from which the superpixelwise segmentation is produced to group pixels into regions with adaptively determined sizes and shapes. A multiscale prophet model is then utilized to extract the multiscale informative trend components from the average spectrum of each superpixel. Taking the multiscale trend signal as the input feature, the HSI data are classified superpixelwisely, which is further refined by majority voting based decision fusion. Comprehensive experiments on three publicly available datasets have fully validated the efficacy and robustness of our MSPM model when benchmarked with eleven state-of-the-art algorithms, including six spectral-spatial methods and five deep learning ones. Besides, MSPM also shows superiority under limited training samples, due to the combined strategies of superpixelwise fusion and multiscale fusion. Our model has provided a useful solution for noise-robust feature extraction as it achieves superior HSI classification even from the uncorrected dataset without prefiltering the water absorption and noisy bands.

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