Hyperspectral Dimensionality Reduction Based on Multiscale Superpixelwise Kernel Principal Component Analysis

Zhang, Lan; Su, Hongjun; Shen, Jingwei

doi:10.3390/rs11101219

Cited by 25 publications

(15 citation statements)

References 49 publications

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“…The PCA is difficult to deal with the nonlinear characteristics due to the influence of various complex factors in the imaging process. Therefore, block PCA (Jiang et al, 2018) and block KPCA (Zhang et al, 2019) were used to extract the characteristics of each representative pattern area in the hyperspectral image data of murals.…”

Section: Block Dimension Reduction Algorithmmentioning

confidence: 99%

“…They extracted hidden information and draft line information of the calligraphy and painting. Zhang used hyperspectral technology to extract color patterns on colored pottery from the principal component images obtained after PCA (Zhang et al, 2019). Salerno evaluated the performance of PCA and independent component analysis (ICA) technology to extract and enhance Archimedes text from hyperspectral image data (Salerno et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Mural Linear Feature Enhancement Algorithm Combining Semi-Supervised Image Segmentation and Dimensionality Reduction

Lyu

Mao

Hou

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Due to the influence of natural and human factors, the linear features in the murals are partially blurred, which brings great challenges to the digital preservation and virtual restoration of cultural heritage. Taking the advantages of non-invasive measurement as well as the rich image and spectral information of hyperspectral technology, we proposed a linear feature enhancement method by combining semi-supervised superpixel segmentation with block dimension reduction. The main research work includes: (1) The true color composite image was segmented to obtain the label data by using the local spatial information of the superpixel image and the global feature information extracted by fuzzy c-means (FCM) clustering.(2) According to the label data, the preprocessed hyperspectral data were divided into homogeneous regions, whose dimensionality was reduced by principal component analysis (PCA) and kernel principal component analysis (KPCA). (3) The principal component images with the largest gradient after dimensionality reduction were respectively selected and normalized. The optimal principal component images normalized by the block PCA and block KPCA dimensionality reduction algorithms are superimposed to produce the linear feature enhancement images of murals. The hyperspectral images of some murals in Qutan Temple, Qinghai Province, China were used to verify the method. The results show that the spatial information and the spectral information of different pattern areas in the hyperspectral image can be fully used by combining the superpixel FCM image segmentation algorithm with the dimensionality reduction algorithm. of. It can highlight the linear information in the hyperspectral images of fades murals.

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Section: Block Dimension Reduction Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mural Linear Feature Enhancement Algorithm Combining Semi-Supervised Image Segmentation and Dimensionality Reduction

Lyu

Mao

Hou

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Experimental results demonstrate that these superpixel-level approaches can effectively explore spectral-spatial information of hyperspectral data and achieve satisfactory results on typical benchmarks even for limited training samples. Some superpixel-based dimensionality reduction methods were also investigated by combining superpixel with classic dimensionality reduction techniques [33][34][35]. These methods make full use of spatial information provided by superpixels to improve the dimensionality reduction performance of classic methods.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification Based on Superpixel Pooling Convolutional Neural Network with Transfer Learning

et al. 2021

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Deep learning-based hyperspectral image (HSI) classification has attracted more and more attention because of its excellent classification ability. Generally, the outstanding performance of these methods mainly depends on a large number of labeled samples. Therefore, it still remains an ongoing challenge how to integrate spatial structure information into these frameworks to classify the HSI with limited training samples. In this study, an effective spectral-spatial HSI classification scheme is proposed based on superpixel pooling convolutional neural network with transfer learning (SP-CNN). The suggested method includes three stages. The first part consists of convolution and pooling operation, which is a down-sampling process to extract the main spectral features of an HSI. The second part is composed of up-sampling and superpixel (homogeneous regions with adaptive shape and size) pooling to explore the spatial structure information of an HSI. Finally, the hyperspectral data with each superpixel as a basic input rather than a pixel are fed to fully connected neural network. In this method, the spectral and spatial information is effectively fused by using superpixel pooling technique. The use of popular transfer learning technology in the proposed classification framework significantly improves the training efficiency of SP-CNN. To evaluate the effectiveness of the SP-CNN, extensive experiments were conducted on three common real HSI datasets acquired from different sensors. With 30 labeled pixels per class, the overall classification accuracy provided by this method on three benchmarks all exceeded 93%, which was at least 4.55% higher than that of several state-of-the-art approaches. Experimental and comparative results prove that the proposed algorithm can effectively classify the HSI with limited training labels.

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“…Lately, spatial structure information has been gradually taken into account in some pixel-based classification approaches [13][14][15][16][17][18][19][20], aiming at getting better classification results. Generally speaking, the purpose of introducing spatial information into the process of classification can be roughly understood as denoising HSI in preprocessing [21][22][23][24], defining the novel similarity between a pair of pixels [25,26], reducing dimensionality [27], improving the classification map in post-processing [28][29][30], or their combinations. From the point of view of classification accuracy, the existing literatures show that some spectral-spatial HSI classification methods do have good classification performance.…”

Section: Introductionmentioning

confidence: 99%

A Novel Spectral–Spatial Classification Method for Hyperspectral Image at Superpixel Level

et al. 2020

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Although superpixel segmentation provides a powerful tool for hyperspectral image (HSI) classification, it is still a challenging problem to classify an HSI at superpixel level because of the characteristics of adaptive size and shape of superpixels. Furthermore, these characteristics of superpixels along with the appearance of noisy pixels makes it difficult to appropriately measure the similarity between two superpixels. Under the assumption that pixels within a superpixel belong to the same class with a high probability, this paper proposes a novel spectral–spatial HSI classification method at superpixel level (SSC-SL). Firstly, a simple linear iterative clustering (SLIC) algorithm is improved by introducing a new similarity and a ranking technique. The improved SLIC, specifically designed for HSI, can straightly segment HSI with arbitrary dimensionality into superpixels, without consulting principal component analysis beforehand. In addition, a superpixel-to-superpixel similarity is newly introduced. The defined similarity is independent of the shape of superpixel, and the influence of noisy pixels on the similarity is weakened. Finally, the classification task is accomplished by labeling each unlabeled superpixel according to the nearest labeled superpixel. In the proposed superpixel-level classification scheme, each superpixel is regarded as a sample. This obviously greatly reduces the data volume to be classified. The experimental results on three real hyperspectral datasets demonstrate the superiority of the proposed spectral–spatial classification method over several comparative state-of-the-art classification approaches, in terms of classification accuracy.

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Hyperspectral Dimensionality Reduction Based on Multiscale Superpixelwise Kernel Principal Component Analysis

Cited by 25 publications

References 49 publications

Mural Linear Feature Enhancement Algorithm Combining Semi-Supervised Image Segmentation and Dimensionality Reduction

Mural Linear Feature Enhancement Algorithm Combining Semi-Supervised Image Segmentation and Dimensionality Reduction

Hyperspectral Image Classification Based on Superpixel Pooling Convolutional Neural Network with Transfer Learning

A Novel Spectral–Spatial Classification Method for Hyperspectral Image at Superpixel Level

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