Noise Robust Hyperspectral Image Classification With MNF-Based Edge Preserving Features
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 o...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Slovenian Society for Stereology and Quantitative Image Analysis
2023-07-01
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| Series: | Image Analysis and Stereology |
| Subjects: | |
| Online Access: | https://www.ias-iss.org/ojs/IAS/article/view/2928 |
| _version_ | 1797783771221590016 |
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| author | Guangyi Chen Adam Krzyzak Shen-en Qian |
| author_facet | Guangyi Chen Adam Krzyzak Shen-en Qian |
| author_sort | Guangyi Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T00:30:35Z |
| format | Article |
| id | doaj.art-0746d4947d5644ed90ce3bb5cac90a46 |
| institution | Directory Open Access Journal |
| issn | 1580-3139 1854-5165 |
| language | English |
| last_indexed | 2024-03-13T00:30:35Z |
| publishDate | 2023-07-01 |
| publisher | Slovenian Society for Stereology and Quantitative Image Analysis |
| record_format | Article |
| series | Image Analysis and Stereology |
| spelling | doaj.art-0746d4947d5644ed90ce3bb5cac90a462023-07-10T14:42:40ZengSlovenian Society for Stereology and Quantitative Image AnalysisImage Analysis and Stereology1580-31391854-51652023-07-01422939910.5566/ias.29281094Noise Robust Hyperspectral Image Classification With MNF-Based Edge Preserving FeaturesGuangyi Chen0Adam KrzyzakShen-en Qian1Concordia UniversityCanadian Space AgencyHyperspectral 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.https://www.ias-iss.org/ojs/IAS/article/view/2928edge preserving features (epfs)hyperspectral image (hsi) classificationminimum noise fraction (mnf)principal component analysis (pca)support vector machine (svm) |
| spellingShingle | Guangyi Chen Adam Krzyzak Shen-en Qian Noise Robust Hyperspectral Image Classification With MNF-Based Edge Preserving Features Image Analysis and Stereology edge preserving features (epfs) hyperspectral image (hsi) classification minimum noise fraction (mnf) principal component analysis (pca) support vector machine (svm) |
| title | Noise Robust Hyperspectral Image Classification With MNF-Based Edge Preserving Features |
| title_full | Noise Robust Hyperspectral Image Classification With MNF-Based Edge Preserving Features |
| title_fullStr | Noise Robust Hyperspectral Image Classification With MNF-Based Edge Preserving Features |
| title_full_unstemmed | Noise Robust Hyperspectral Image Classification With MNF-Based Edge Preserving Features |
| title_short | Noise Robust Hyperspectral Image Classification With MNF-Based Edge Preserving Features |
| title_sort | noise robust hyperspectral image classification with mnf based edge preserving features |
| topic | edge preserving features (epfs) hyperspectral image (hsi) classification minimum noise fraction (mnf) principal component analysis (pca) support vector machine (svm) |
| url | https://www.ias-iss.org/ojs/IAS/article/view/2928 |
| work_keys_str_mv | AT guangyichen noiserobusthyperspectralimageclassificationwithmnfbasededgepreservingfeatures AT adamkrzyzak noiserobusthyperspectralimageclassificationwithmnfbasededgepreservingfeatures AT shenenqian noiserobusthyperspectralimageclassificationwithmnfbasededgepreservingfeatures |