Noise Robust Hyperspectral Image Classification With MNF-Based Edge Preserving Features
DOI:
https://doi.org/10.5566/ias.2928Keywords:
Edge preserving features (EPFs), hyperspectral image (HSI) classification, minimum noise fraction (MNF), principal component analysis (PCA), support vector machine (SVM)Abstract
Hyperspectral image (HSI) classification is an important topic in remote sensing. In this paper, we improve the principal component analysis (PCA)-based edge preserving features (EPFs) for HSI classification. We select to use minimum noise fraction (MNF) instead of PCA to reduce the dimensionality of the hyperspectral data cube to be classified. We keep all the rest steps from the PCA-based EPFs for HSI classification. Since MNF can preserve fine features of a HSI data cube better than PCA, our new method can outperform PCA-EPFs for HSI classification significantly. Experimental results show that our new method performs better than the PCA-based EPFs under such noisy environment as Gaussian white noise and shot noise. In addition, our MNF+EPFs outperform the PCA+EPFs even when no noise is added to the HSI data cubes for most testing cases, which is very desirable in remote sensing.
References
Camps-Valls G and Bruzzone L (2005), Kernel-based methods for hyperspectral image classification, IEEE T GEOSCI REMOTE, 43:1351-62.
Chen GY, Bui TD, Quach KG and Qian SE (2014), Denoising hyperspectral imagery using principal component analysis and block matching 4D filtering, CAN J REMOTE SENS, 40:60-7.
Chen GY and Qian SE (2011), Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage, IEEE T GEOSCI REMOTE, 49:973-80.
Chen Y, Lin Z, Zhao X, Wang G and Gu Y (2014), Deep learning-based classification of hyperspectral data, IEEE J-STARS, 7:2094-107.
Chen Y, Nasrabadi NM and Tran TD (2011), Hyperspectral image classification using dictionary-based sparse representation, IEEE T GEOSCI REMOTE, 49:3973-85.
Chen Y, Nasrabadi NM and Tran TD (2013), Hyperspectral image classification via kernel sparse representation, IEEE T GEOSCI REMOTE, 51:217-31.
Cheng G, Zhu, F, Xiang S, Wang Y and Pan X (2016), Semisupervised hyperspectral image classification via discriminant analysis and robust regression, IEEE J-STARS, 9:595-608.
Cortes C and Vapnik VN (1995), Support-vector networks, Machine Learning, 20:273–97.
Green AA, Berman M, Switzer P and Craig MD (1988), A transformation for ordering multispectral data in terms of image quality with implications for noise removal, IEEE T GEOSCI REMOTE, 26:65-74.
Jolliffe IT (2002), Principal Component Analysis, second edition (Springer).
Kang XD, Li S and Benediktsson JA (2014), Spectral-spatial hyperspectral image classification with edge-preserving filtering, IEEE T GEOSCI REMOTE, 52:2666-77.
Kang X, Xiang X, Li S and Benediktsson JA (2017), PCA-Based Edge-Preserving Features for Hyperspectral Image Classification, IEEE T GEOSCI REMOTE, 55:7140-51.
M. Fauvel, J. Chanussot and J. A. Benediktsson, “A spatial–spectral kernel-based approach for the classification of remote-sensing images,” PATTERN RECOGN, vol. 45, no. 1, pp. 381-392, 2012.
Li J, Bioucas-Dias JM and Plaza A (2013), Spectral-spatial classification of hyperspectral data using loopy belief propagation and active learning, IEEE T GEOSCI REMOTE, 51:844-56.
Li J, Marpu PR, Plaza A, Bioucas-Dias JM and Benediktsson JA (2013), Generalized composite kernel framework for hyperspectral image classification, IEEE T GEOSCI REMOTE, 51:4816-29.
Liu H, Xia K, Li T, Ma J and Owoola E (2020). Dimensionality reduction of hyperspectral images based on improved spatial–spectral weight manifold embedding, Sensors 20:4413.
Luo GC, Chen GY, Tian L, Qin K and Qian SE (2016), Minimum noise fraction versus principal component analysis as a preprocessing step for hyperspectral imagery denoising, CAN J REMOTE SENS, 42:106-16.
Melgani F and Bruzzone I (2004), Classification of hyperspectral remote sensing images with support vector machines, IEEE T GEOSCI REMOTE, 42:1778-90.
Zhou F, Hang R, Liu Q and Yuan X (2019), Hyperspectral image classification using spectral-spatial LSTMs, Neurocomputing, 328:39-47.
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Copyright (c) 2023 Guangyi Chen, Adam Krzyzak, Shen-en Qian
This work is licensed under a Creative Commons Attribution 4.0 International License.
