Hyperspectral Image Classification Using Discrete Space Model and Support Vector Machines

Xie, Li; Li, Guangyao; Xiao, Mang; Peng, Lei; Chen, Qiaochuan

doi:10.1109/lgrs.2016.2643686

Cited by 23 publications

(12 citation statements)

References 13 publications

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“…According to the scientific literature, the SVMs method has been used in several studies such as those developed by Mountrakis et al [27]; Lu et al [28]; and Xie et al [29] with good results on image classification, similar to the results obtained in this work where the SVMs method registered the best fit. Some works have compared different supervised classification methods; for example, Pal and Mather [35] used the same three classification methods that were applied in this study (MLC, ANNs and SVMs), obtaining the best fit results through SVMs.…”

Section: Analysis Of Land Use Changessupporting

confidence: 79%

“…This automatic learning algorithm trains linear and non-linear learning functions by transforming the original data into a different space with a function (kernel) to obtain the hyperplane which maximizes the margin of separation between two or more classes to be classified [26]. Currently, the SVMs algorithm is among the most reliable methods; therefore, it is used in many works [27][28][29] with satisfactory results. For the classification of images the Radial Basis Function (RBF) for not-linearly separable data was used.…”

Section: Processingmentioning

confidence: 99%

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Analysis of the Land Use and Cover Changes in the Metropolitan Area of Tepic-Xalisco (1973–2015) through Landsat Images

et al. 2018

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Land use and cover changes (LUCC) have been identified as one of the main causes of biodiversity loss and deforestation in the world. Fundamentally, the urban land use has replaced agricultural and forest cover causing loss of environmental services. Monitoring and quantifying LUCC are essential to achieve a proper land management. The objective of this study was to analyze the LUCC in the metropolitan area of Tepic-Xalisco during the period 1973-2015. To find the best fit and obtain the different land use classes, supervised classification techniques were applied using Maximum Likelihood Classification (MLC), Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs). The results were validated with control points (ground truth) through cross tabulation. The best results were obtained from the SVMs method with kappa indices above 85%. The transition analysis infers that urban land has grown significantly during 42 years, increasing 62 km 2 and replacing agricultural areas at a rate of 1.48 km 2 /year. Forest loss of 5.78 km 2 annually was also identified. The results show the different land uses distribution and the dynamics developed in the past. This information may be used to simulate future LUCC and modeling different scenarios.

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Section: Analysis Of Land Use Changessupporting

confidence: 79%

Section: Processingmentioning

confidence: 99%

Analysis of the Land Use and Cover Changes in the Metropolitan Area of Tepic-Xalisco (1973–2015) through Landsat Images

et al. 2018

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“…The SVM classifier implements with the radial basis functions (RBF) as the kernel function, and the variance parameter and the penalization factor are obtained via crossvalidation [36]. Overall Accuracy (OA) [37], Average Accuracy (AA) [38], and Kappa Coefficient (KC) [39] are used to quantify the classification accuracy.…”

Section: B Parameter Tuningmentioning

confidence: 99%

A Label Similarity Probability Filter for Hyperspectral Image Postclassification

Sun

Yang

Ren

et al. 2021

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

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This paper presents a Label Similarity Probability Filter (LSPF) to make hyperspectral image post-classification. The LSPF is inspired from the first law of geography and proposes a class label probability function to quantify the probability of both centered and its neighboring pixels in belonging to the same class. It first classifies the hyperspectral data using the regular support vector machine classifier. Then, it binarizes the posterior classification result to obtain the binary label maps of each class. After that, it traverses all spatial windows centered by each pixel and calculates the cumulative probability of all pixels in each class. Finally, the cumulative probabilities are used to make reclassification to obtain the refined classification map. Experiments on Indian Pines, Pavia University and ZY1-02D Yellow River Estuary data show that LSPF greatly improves the classification accuracy of spectral signatures and outperforms other state-ofthe-art spectral-spatial methods IndexTerms-hyperspectral; post-classification; label similarity probability filter; spectral-spatial methods.

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“…Support vector machine (SVM), which is used widely in the literature, was chosen [32][33][34][35][36][37][38][39][40]. We employed SVM due to its ability to reduce classification errors and creating LULC maps with higher accuracy, overcoming the limitations of parametric classification and because it was recommended by several researchers [38][39][40]. The algorithm starts by transforming the original data into space to hyperplane that maximises classes to be classified [35,36].…”

Section: Data Classification and Processingmentioning

confidence: 99%

Land Use/Land Cover Changes Impact on Groundwater Level and Quality in the Northern Part of the United Arab Emirates

2020

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This study aims to develop an integrated approach for mapping and monitoring land use/land cover (LULC) changes and to investigate the impacts of LULC changes and population growth on groundwater level and quality using Landsat images and hydrological information in a Geographic information system (GIS) environment. All Landsat images (1990, 2000, 2010, and 2018) were classified using a support vector machine (SVM) and spectral analysis mapper (SAM) classifiers. The result of validation metrics, including precision, recall, and F1, indicated that the SVM classier has a better performance than SAM. The obtained LULC maps have an overall accuracy of more than 90%. Each pair of enhanced LULC maps (1990–2000, 2000–2010, 2010–2018, and 1990–2018) were used as input data for an image difference algorithm to monitor LULC changes. Maps of change detection were then imported into a GIS environment and spatially correlated against the spatiotemporal maps of groundwater level and groundwater quality. The results also show that the approximate built-up area increased from 227.26 km2 (1.39%) to 869.77 km2 (7.41%), while vegetated areas (farmlands, parks and gardens) increased from about 76.70 km2 (0.65%) to 290.70 km2 (2.47%). The observed changes in LULC are highly linked to the depletion in groundwater level and quality across the study area from the Oman Mountains to the coastal areas.

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Hyperspectral Image Classification Using Discrete Space Model and Support Vector Machines

Cited by 23 publications

References 13 publications

Analysis of the Land Use and Cover Changes in the Metropolitan Area of Tepic-Xalisco (1973–2015) through Landsat Images

Analysis of the Land Use and Cover Changes in the Metropolitan Area of Tepic-Xalisco (1973–2015) through Landsat Images

A Label Similarity Probability Filter for Hyperspectral Image Postclassification

Land Use/Land Cover Changes Impact on Groundwater Level and Quality in the Northern Part of the United Arab Emirates

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