Spectral–spatial hyperspectral classification based on multi-center SAM and MRF

Tang, Bo; Li, Zhi; Xiao, Xiaoyan; Nie, Mingyu; Chang, Jun; Jiang, Wei; Li, Xiaomei; Zheng, Chengyun

doi:10.1007/s10043-015-0139-9

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…Unlike the previous methods that only consider the test pixel and make decision by thresholding the spectral angle or choose the class with minimum angle, our approach jointly minimizes the spectral angle and promotes spatial homogeneity across the image. Recently, the study [11] by Tang et al have combined SAM and MRF using multi-center model and Gaussian normalization, but our method is different in that it directly uses the minimum spectral angle as the unary energy.…”

Section: Spectral Angle Mapper-markov Random Field (Sam-mrf)mentioning

confidence: 99%

Spectral angle based unary energy functions for spatial-spectral hyperspectral classification using Markov random fields

Gewali¹,

Monteiro

2016

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)

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In this paper, we propose and compare two spectral angle based approaches for spatial-spectral classification. Our methods use the spectral angle to generate unary energies in a grid-structured Markov random field defined over the pixel labels of a hyperspectral image. The first approach is to use the exponential spectral angle mapper (ESAM) kernel/covariance function, a spectral angle based function, with the support vector machine and the Gaussian process classifier. The second approach is to directly use the minimum spectral angle between the test pixel and the training pixels as the unary energy. We compare the proposed methods with the state-of-the-art Markov random field methods that use support vector machines and Gaussian processes with squared exponential kernel/covariance function. In our experiments with two datasets, it is seen that using minimum spectral angle as unary energy produces better or comparable results to the existing methods at a smaller running time.

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Section: Spectral Angle Mapper-markov Random Field (Sam-mrf)mentioning

confidence: 99%

Spectral angle based unary energy functions for spatial-spectral hyperspectral classification using Markov random fields

Gewali¹,

Monteiro

2016

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)

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“…Xu Y et al optimized the model by inserting the watershed algorithm in the process of combining SVM and MRF [39]. Tang B et al proposed a classification framework based on the Spectral Angle Mapper (SAM) to obtain a more accurate classification by introducing the multi-center model and MRF into the probabilistic decision framework [40]. For the problem that shallow MRF cannot fully utilize the spatial information of HSIs, Cao X et al proposed a cascaded MRF model, which further improved the classification performance of the model [41].…”

Section: Introductionmentioning

confidence: 99%

An SVM-Based Nested Sliding Window Approach for Spectral–Spatial Classification of Hyperspectral Images

et al. 2020

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This paper proposes a Nested Sliding Window (NSW) method based on the correlation between pixel vectors, which can extract spatial information from the hyperspectral image (HSI) and reconstruct the original data. In the NSW method, the neighbourhood window constructed with the target pixel as the centre contains relevant pixels that are spatially adjacent to the target pixel. In the neighbourhood window, a nested sliding sub-window contains the target pixel and a part of the relevant pixels. The optimal sub-window position is determined according to the average value of the Pearson correlation coefficients of the target pixel and the relevant pixels, and the target pixel can be reconstructed by using the pixels and the corresponding correlation coefficients in the optimal sub-window. By combining NSW with Principal Component Analysis (PCA) and Support Vector Machine (SVM), a classification model, namely NSW-PCA-SVM, is obtained. This paper conducts experiments on three public datasets, and verifies the effectiveness of the proposed model by comparing with two basic models, i.e., SVM and PCA-SVM, and six state-of-the-art models, i.e., CDCT-WF-SVM, CDCT-2DCT-SVM, SDWT-2DWT-SVM, SDWT-WF-SVM, SDWT-2DCT-SVM and Two-Stage. The proposed approach has the following advantages in overall accuracy (OA)—take the experimental results on the Indian Pines dataset as an example: (1) Compared with SVM (OA = 53.29%) and PCA-SVM (OA = 58.44%), NSW-PCA-SVM (OA = 91.40%) effectively utilizes the spatial information of HSI and improves the classification accuracy. (2) The performance of the proposed model is mainly determined by two parameters, i.e., the window size in NSW and the number of principal components in PCA. The two parameters can be adjusted independently, making parameter adjustment more convenient. (3) When the sample size of the training set is small (20 samples per class), the proposed NSW-PCA-SVM approach achieves 2.38–18.40% advantages in OA over the six state-of-the-art models.

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“…The SAM algorithm is, however, just one of the classical methods used for hyperspectral image classification. Researchers have proposed both algorithm optimizations (Galal et al, 2012;Tang et al, 2015) and alternative approaches (Kakhani and Mokhtarzade, 2019). The package modularity allows the user to add new processing algorithms by calling other R packages.…”

Section: Software Functionalitiesmentioning

confidence: 99%

Development and application of tests for microplastic detection in soil

Santander¹,

Alfonso²

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China 20 Plastic mulch Sludge Flotation (NaCl) Visual sorting & μ-FTIR 78 ±13 63 ±13 PP PE PET * Polyethylene (PE), polystyrene (PS), polypropylene (PP), polyethylene terephthalate (PET), polyamide (PA), polybutylene (PB), ethylene vinyl acetate (EVA), high density polyethylene (HDPE), polyvinyl chloride (PVC), and polyurethane (PUR). ** The 'visual sorting' test requires a stereomicroscope to look for microplastics. 1 Time-of-flight secondary ion mass spectrometry. 2 Ding et al. (2020) did the visual sorting with a metallographic microscope. 3 Transmission electronic microscopy. 4 Pyrolysis coupled to gas chromatography and mass spectrometry.

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Spectral–spatial hyperspectral classification based on multi-center SAM and MRF

Cited by 9 publications

References 15 publications

Spectral angle based unary energy functions for spatial-spectral hyperspectral classification using Markov random fields

Spectral angle based unary energy functions for spatial-spectral hyperspectral classification using Markov random fields

An SVM-Based Nested Sliding Window Approach for Spectral–Spatial Classification of Hyperspectral Images

Development and application of tests for microplastic detection in soil

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