Land Cover Classification of Huixian Wetland Based on SAR and Optical Image Fusion

2020 ◽  
Author(s):  
Jianming Xiao ◽  
Yu Xiao ◽  
Xiyan Sun ◽  
Jianhua Huang ◽  
Haokun Wang
Keyword(s):  
Land Cover ◽  
Image Fusion ◽  
Land Cover Classification ◽  
Optical Image

scholarly journals Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

2015 ◽  
Vol 45 (2) ◽  
pp. 195-202 ◽  
Author(s):  
Luiz Felipe de Almeida FURTADO ◽  
Thiago Sanna Freire SILVA ◽  
Pedro José Farias FERNANDES ◽  
Evelyn Márcia Leão de Moraes NOVO
Keyword(s):  
Land Cover ◽  
Image Fusion ◽  
Land Cover Classification ◽  
Classification Method ◽  
Data Mining Algorithm ◽  
Automated Classification ◽  
Kappa Index ◽  
Optical Images ◽  
Fusion Methods

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

Sugeno Fuzzy-Inference-System-Based Land Cover Classification of Remotely Sensed Images

2018 ◽  
pp. 326-363
Author(s):  
Jenicka S
Keyword(s):  
Land Cover ◽  
Fuzzy Inference ◽  
Land Cover Classification ◽  
Remotely Sensed ◽  
Inference System ◽  
Texture Model ◽  
Remotely Sensed Image ◽  
Land Covers ◽  
Remotely Sensed Images

Accuracy of land cover classification in remotely sensed images relies on the features extracted and the classifier used. Texture features are significant in land cover classification. Traditional texture models capture only patterns with discrete boundaries whereas fuzzy patterns need to be classified by assigning due weightage to uncertainty. When remotely sensed image contains noise, the image may have fuzzy patterns characterizing land covers and fuzzy boundaries separating land covers. So a fuzzy texture model is proposed for effective classification of land covers in remotely sensed images and the model uses Sugeno Fuzzy Inference System (FIS). Support Vector Machine (SVM) is used for precise and fast classification of image pixels. Hence it is proposed to use a hybrid of fuzzy texture model and SVM for land cover classification of remotely sensed images. In this chapter, land cover classification of IRS-P6, LISS-IV remotely sensed image is performed using multivariate version of the proposed texture model.

Sugeno Fuzzy-Inference-System-Based Land Cover Classification of Remotely Sensed Images

2019 ◽  
pp. 1247-1283
Author(s):  
Jenicka S.
Keyword(s):  
Land Cover ◽  
Fuzzy Inference ◽  
Land Cover Classification ◽  
Remotely Sensed ◽  
Inference System ◽  
Texture Model ◽  
Remotely Sensed Image ◽  
Land Covers ◽  
Remotely Sensed Images

Accuracy of land cover classification in remotely sensed images relies on the features extracted and the classifier used. Texture features are significant in land cover classification. Traditional texture models capture only patterns with discrete boundaries whereas fuzzy patterns need to be classified by assigning due weightage to uncertainty. When remotely sensed image contains noise, the image may have fuzzy patterns characterizing land covers and fuzzy boundaries separating land covers. So a fuzzy texture model is proposed for effective classification of land covers in remotely sensed images and the model uses Sugeno Fuzzy Inference System (FIS). Support Vector Machine (SVM) is used for precise and fast classification of image pixels. Hence it is proposed to use a hybrid of fuzzy texture model and SVM for land cover classification of remotely sensed images. In this chapter, land cover classification of IRS-P6, LISS-IV remotely sensed image is performed using multivariate version of the proposed texture model.

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