Hierarchical classification of land-cover types using RAG-based merging

2007 ◽  
Author(s):  
Sang-Hoon Lee
Keyword(s):  
Land Cover ◽  
Hierarchical Classification ◽  
Land Cover Types

scholarly journals ADVANCED CLASSIFICATION OF OPTICAL AND SAR IMAGES FOR URBAN LAND COVER MAPPING

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1417-1421
Author(s):  
D. Amarsaikhan
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Landsat 8 ◽  
Data Sets ◽  
Contextual Knowledge ◽  
Rule Based ◽  
Land Cover Types ◽  
Urban Land Cover ◽  
Land Cover Map

Abstract. The aim of this research is to classify urban land cover types using an advanced classification method. As the input bands to the classification, the features derived from Landsat 8 and Sentinel 1A SAR data sets are used. To extract the reliable urban land cover information from the optical and SAR features, a rule-based classification algorithm that uses spatial thresholds defined from the contextual knowledge is constructed. The result of the constructed method is compared with the results of a standard classification technique and it indicates a higher accuracy. Overall, the study demonstrates that the multisource data sets can considerably improve the classification of urban land cover types and the rule-based method is a powerful tool to produce a reliable land cover map.

scholarly journals IMPROVING CLASSIFICATION OF MULTISPECTRAL IMAGES BASED ON SELECTED RELATIONS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 377-381
Author(s):  
L. Cohen ◽  
O. Almog ◽  
M. Shoshany
Keyword(s):  
Land Cover ◽  
Spectral Band ◽  
Research Area ◽  
Support Vector ◽  
Land Cover Types ◽  
Spectral Bands ◽  
Definition Of ◽  
Spectral Representations ◽  
The City

Abstract. A novel classification technique based on definition of unique spectral relations (such as slopes among spectral bands) for all land cover types named (SSF Significant Spectral Features) is presented in the article.A large slopes combination between spectral band pairs is calculated and spectral characterizations that emphasizes the best spectral land cover separation is sought. Increasing in dimensionality of spectral representations is balanced by the simplicity of calculations. The technique has been examined on data acquired by a flown hyperspectral scanner (AISA). The spectral data was narrowed into the equivalent 8 world-view2 channels. The research area was in the city of “Hadera”, Israel, which included 10 land cover types in an urban area, open area and road infrastructure. The comparison between the developed SSF technique and common techniques such as: SVM (Support Vector Machine) and ML (Maximum Likelihood) has shown a clear advantage over ML technique, while produced similar results as SVM. The poorest results of using SSF technique was achieved in an herbaceous area (70%). However, the simplicity of the method, the well-defined parameters it produces for interpreting the results, makes it intuitive over using techniques such as SVM, which is considered as a not explicit classifier.

scholarly journals A Hierarchical Classification Framework of Satellite Multispectral/Hyperspectral Images for Mapping Coastal Wetlands

2019 ◽  
Vol 11 (19) ◽  
pp. 2238 ◽  
Author(s):  
Leilei Jiao ◽  
Weiwei Sun ◽  
Gang Yang ◽  
Guangbo Ren ◽  
Yinnian Liu
Keyword(s):  
Land Cover ◽  
Coastal Wetlands ◽  
Hierarchical Classification ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Landsat 8 ◽  
Land Cover Types ◽  
Multispectral Data ◽  
Classification Framework ◽  
Land Covers

Mapping different land cover types with satellite remote sensing data is significant for restoring and protecting natural resources and ecological services in coastal wetlands. In this paper, we propose a hierarchical classification framework (HCF) that implements two levels of classification scheme to identify different land cover types of coastal wetlands. The first level utilizes the designed decision tree to roughly group land covers into four rough classes and the second level combines multiple features (i.e., spectral feature, texture feature and geometric feature) of each class to distinguish different subtypes of land covers in each rough class. Two groups of classification experiments on Landsat and Sentinel multispectral data and China Gaofen (GF)-5 hyperspectral data are carried out in order to testify the classification behaviors of two famous coastal wetlands of China, that is, Yellow River Estuary and Yancheng coastal wetland. Experimental results on Landsat data show that the proposed HCF performs better than support vector machine and random forest in classifying land covers of coastal wetlands. Moreover, HCF is suitable for both multispectral data and hyperspectral data and the GF-5 data is superior to Landsat-8 and Sentinel-2 multispectral data in obtaining fine classification results of coastal wetlands.

scholarly journals Classification of rare land cover types: Distinguishing annual and perennial crops in an agricultural catchment in South Korea

PLoS ONE ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. e0190476 ◽  
Author(s):  
Christina Bogner ◽  
Bumsuk Seo ◽  
Dorian Rohner ◽  
Björn Reineking
Keyword(s):  
Land Cover ◽  
South Korea ◽  
Perennial Crops ◽  
Land Cover Types

scholarly journals Assessment of the X- and C-Band Polarimetric SAR Data for Plastic-Mulched Farmland Classification

2019 ◽  
Vol 11 (6) ◽  
pp. 660 ◽  
Author(s):  
Chang-An Liu ◽  
Zhongxin Chen ◽  
Di Wang ◽  
Dandan Li
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Agricultural Land ◽  
Test Site ◽  
Decomposition Methods ◽  
Bare Soil ◽  
Classification Error ◽  
Land Cover Types ◽  
Land Covers

We present a classification of plastic-mulched farmland (PMF) and other land cover types using full polarimetric RADARSAT-2 data and dual polarimetric (HH, VV) TerraSAR-X data, acquired from a test site in Hebei, China, where the main land covers include PMF, bare soil, winter wheat, urban areas and water. The main objectives were to evaluate the outcome of using high-resolution TerraSAR-X data for classifying PMF and other land covers and to compare classification accuracies based on different synthetic aperture radar bands and polarization parameters. Initially, different polarimetric indices were calculated, while polarimetric decomposition methods were used to obtain the polarimetric decomposition components. Using these polarimetric components as input, the random forest supervised classification algorithm was applied in the classification experiments. Our results show that in this study full-polarimetric RADARSAT-2 data produced the most accurate overall classification (94.81%), indicating that full polarization is vital to distinguishing PMF from other land cover types. Dual polarimetric data had similar levels of classification error for PMF and bare soil, yielding mapping accuracies of 53.28% and 59.48% (TerraSAR-X), and 59.56% and 57.1% (RADARSAT-2), respectively. We found that Shannon entropy made the greatest contribution to accuracy in all three experiments, suggesting that it has great potential to improve agricultural land use classifications based on remote sensing.

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