A method of salt-affected soil information extraction based on a support vector machine with texture features

Cai, Simin; Rongqun, Zhang; Li, Liming; Zhou, Deliang

doi:10.1016/j.mcm.2009.10.037

Cited by 40 publications

(18 citation statements)

References 5 publications

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“…In addition, some remote sensing indices derived from the Advanced SpaceborneThermal Emission and Reflectance Radiometer (ASTER, National Aeronautics and Space Administration, Washington, DC, USA) imagery (i.e., b12 × b14 3 /b13 4 , b13/b12) were also used as supplementary information for selecting samples of gabbro and quartzose rock. In fact, the method of choosing rock samples in this study is the same as that used in previous studies [11,49], and is detailed in Ding et al [48].…”

Section: Dataset and Processingmentioning

confidence: 99%

“…The SVM has the ability to deal with multiple effective image features for optimal classification with a small training sample set [48,68], which is an appropriate method to integrate textural and spectral features for lithological classification in this study.…”

Section: Support Vector Machine Classifiermentioning

confidence: 99%

“…In future studies, other surface parameters related to the physical or chemical properties of rocks should be considered for lithological identification, such as permittivity, thermal characteristics, albedo, and indices related to rock chemical components. In addition, the geographical environment, especially land surface temperature and atmospheric moisture content, may influence the surface morphology of rocks [48], such as changed surface structural characteristics, which may result from sedimentation occurring within weathered fractures. The geographic environment in a specific study area may limit the robustness of this strategy, which would also limit the accuracy of image classification.…”

Section: Lithological Classification Using Selected Featuresmentioning

confidence: 99%

“…Support vector machine (SVM), a popular approach used in classification, is proficient at fusing effective and distinctive image features with spectral features to improve classification accuracy [27,48]. In this paper, we attempt to integrate multiple spectral and textural features for lithological classification.…”

Section: Introductionmentioning

confidence: 99%

“…N) are support vectors, a i and b are specific parameters of the hyperplane, and K (X, X i ) is the kernel function used to construct machines with different types of decision surfaces in the feature space. The SVM has the ability to deal with multiple effective image features for optimal classification with a small training sample set [48,68], which is an appropriate method to integrate textural and spectral features for lithological classification in this study.…”

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confidence: 99%

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Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data

2016

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Abstract:Texture as a measure of spatial features has been useful as supplementary information to improve image classification in many areas of research fields. This study focuses on assessing the ability of different textural vectors and their combinations to aid spectral features in the classification of silicate rocks. Texture images were calculated from Landsat 8 imagery using a fractal dimension method. Different combinations of texture images, fused with all seven spectral bands, were examined using the Jeffries-Matusita (J-M) distance to select the optimal input feature vectors for image classification. Then, a support vector machine (SVM) fusing textural and spectral features was applied for image classification. The results showed that the fused SVM classifier achieved an overall classification accuracy of 83.73%. Compared to the conventional classification method, which is based only on spectral features, the accuracy achieved by the fused SVM classifier is noticeably improved, especially for granite and quartzose rock, which shows an increase of 38.84% and 7.03%, respectively. We conclude that the integration of textural and spectral features is promising for lithological classification when an appropriate method is selected to derive texture images and an effective technique is applied to select the optimal feature vectors for image classification.

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Section: Dataset and Processingmentioning

confidence: 99%

Section: Support Vector Machine Classifiermentioning

confidence: 99%

Section: Lithological Classification Using Selected Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data

2016

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Soil salinization of cultivated land in Shandong Province, China—Dynamics during the past 40 years

Xiao

Zhao

Li³

et al. 2018

Land Degrad Dev

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An objective requirement of the sustainable utilization of soil resources and agricultural production management is the determination of the status of soil salinization and an understanding of its dynamics. This study selected the coastal plain, the Yellow River flood plain, and key inland areas with salinization in Shandong Province. Sampling and analysis of soil salinization in cultivated lands were conducted in 2015–2016, and 3,551 valid samples were obtained. Statistical and spatial analyses were performed and compared with the data from the Second Soil Survey (1979–1985). The degree, area, distribution, and dynamic characteristics of soil salinization in Shandong Province were analyzed. The results showed that the total salt content of the study area was mainly slight to moderate, and the salinization was dominated by chloride salts. At present, the land area of salinized cultivated land was 384.00 thousand ha, accounting for 11.30% of the total cultivated land area. Soil salinization showed a concentrated distribution along the coastal plain that was scattered in the Yellow River flood plain and inland areas, and the salinity decreased from the coast to the inland area. Compared with the Second Soil Survey period, the salinized cultivated land area had decreased by 431.59 thousand ha, a decrease of 52.9%. The degree of salinization, especially in coastal area, was substantial, and the gravity center moved northeastward. In this study, the characteristics and dynamics of the present state of soil salinization in Shandong Province were examined, providing a scientific basis for the utilization and management of cultivated land resources in saline areas.

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A Brief Review on Soil Salinity Mapping by Optical and Radar Remote Sensing

2019

Research Developments in Saline Agriculture

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A method of salt-affected soil information extraction based on a support vector machine with texture features

Cited by 40 publications

References 5 publications

Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data

Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data

Soil salinization of cultivated land in Shandong Province, China—Dynamics during the past 40 years

A Brief Review on Soil Salinity Mapping by Optical and Radar Remote Sensing

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