scholarly journals LAND COVER CLASSIFICATION OF REMOTELY SENSED IMAGERY USING A METHOD BASED ON TOPOGRAPHICAL RESTRICTIVE FEATURES: A CASE STUDY OF THE JINHE WATERSHED

2005 ◽  
Vol 29 (6) ◽  
pp. 927-933
Author(s):  
HONG Jun ◽  
◽  
GE Jian-Ping ◽  
CAI Ti-Jiu ◽  
NIE Yi-Huang
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Remotely Sensed ◽  
Remotely Sensed Imagery

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.

scholarly journals Fusing High-Spatial-Resolution Remotely Sensed Imagery and OpenStreetMap Data for Land Cover Classification Over Urban Areas

2019 ◽  
Vol 11 (1) ◽  
pp. 88 ◽  
Author(s):  
Nianxue Luo ◽  
Taili Wan ◽  
Huaixu Hao ◽  
Qikai Lu
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Urban Areas ◽  
Spatial Information ◽  
Land Cover Classification ◽  
Remotely Sensed ◽  
Geographical Information ◽  
Remotely Sensed Imagery ◽  
Training Samples ◽  
Comparison Results

Land cover classification of urban areas is critical for understanding the urban environment. High-resolution remotely sensed imagery provides abundant, detailed spatial information for urban classification. In the meantime, OpenStreetMap (OSM) data, as typical crowd-sourced geographical information, have been an emerging data source for obtaining urban information. In this context, a land cover classification method that fuses high-resolution remotely sensed imagery and OSM data is proposed. Training samples were generated by integrating the OSM data and multiple information indexes. OSM data, which contain class attributes and location information of urban objects, served as the labels of initial training samples. Multiple information indexes that reflect spectral and spatial characteristics of different classes were utilized to improve the training set. Morphological attribute profiles were used because the structural and contextual information of images was effective in distinguishing the classes with similar spectral characteristics. Moreover, a road superimposition strategy that considers road hierarchy was developed because OSM data provide road information with high completeness in the urban area. Experiments were conducted on the data captured over Wuhan city, and three state-of-the-art approaches were adopted for comparison. Results show that the proposed approach obtains satisfactory results and outperforms the other comparative approaches.

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