A Vector Median Filter For Hyperspectral Images Based On Lexicographic Ordering of Estimated Auto-Correlation Functions

Ivanovici, Mihai; Marincaş, Maria; Coliban, Radu-Mihai

doi:10.1109/whispers52202.2021.9483963

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…Feature extraction methods include: feature extraction method based on binary discrete wavelet transform [10], fast dimensionality reduction method based on dynamic programming [11]. In addition to dimensionality reduction of the data [12], another method for high-dimensional problems is band selection, such as independent component analysis (ICA) for band selection [13]; The bands containing more information are selected by evaluating the average weight coefficient of each band, using an adaptive band weight measurement method based on information entropy [14]. These methods of deleting redundant bands reduce the computational complexity of hyperspectral image classification and ease the high-dimensional problem to a certain extent [15].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification Based on Unsupervised Regularization

Liu

Zhang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Due to the powerful feature expression ability of deep learning and its end-to-end non-linear mapping relationship, deep learning-based methods have become the mainstream method for hyperspectral image classification tasks. However, the accuracy of deep learning methods greatly depends on the use of a large number of labeled samples to train the model. Also, hyperspectral images have few labeled samples and unbalanced categories, which makes the depth model prone to overfitting, which seriously affects the classification accuracy. Therefore, how to alleviate the overfitting phenomenon caused by small samples in the classification problem based on deep learning is still a problem that needs to be solved. Considering that it is relatively easier to obtain a large number of unlabeled samples in the field of remote sensing, making full use of the unsupervised information learned from unlabeled data can regularize the supervised classification model, which can effectively alleviate the overfitting phenomenon caused by the small samples problem. In the supervised training process, unsupervised information from the overall distribution of the sample is introduced to guide the regularization of the model, so as to realize the effective classification of the data in the case of a small number of labeled samples. Experimental results demonstrate the effectiveness of the proposed method in terms of hyperspectral image (HSI) classification with few training samples.

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