Optimising remotely sensed land cover classification for habitat mapping in complex Scottish upland landscapes

2021 ◽  
Author(s):  
Alexander T. Merrington ◽  
David T. Milodowski ◽  
Mathew Williams
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Remotely Sensed ◽  
Habitat Mapping

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.

Measuring and monitoring land cover, land use, and vegetation characteristics

2010 ◽  
Author(s):  
Ned Horning ◽  
Julie A. Robinson ◽  
Eleanor J. Sterling ◽  
Woody Turner ◽  
Sacha Spector
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Deciduous Forest ◽  
Land Cover Classification ◽  
Remotely Sensed ◽  
Land Cover Mapping ◽  
Remotely Sensed Data ◽  
Soil P ◽  
Vegetation Characteristics ◽  
Classification Project

In terrestrial biomes, ecologists and conservation biologists commonly need to understand vegetation characteristics such as structure, primary productivity, and spatial distribution and extent. Fortunately, there are a number of airborne and satellite sensors capable of providing data from which you can derive this information. We will begin this chapter with a discussion on mapping land cover and land use. This is followed by text on monitoring changes in land cover and concludes with a section on vegetation characteristics and how we can measure these using remotely sensed data. We provide a detailed example to illustrate the process of creating a land cover map from remotely sensed data to make management decisions for a protected area. This section provides an overview of land cover classification using remotely sensed data. We will describe different options for conducting land cover classification, including types of imagery, methods and algorithms, and classification schemes. Land cover mapping is not as difficult as it may appear, but you will need to make several decisions, choices, and compromises regarding image selection and analysis methods. Although it is beyond the scope of this chapter to provide details for all situations, after reading it you will be able to better assess your own needs and requirements. You will also learn the steps to carry out a land cover classification project while gaining an appreciation for the image classification process. That said, if you lack experience with land cover mapping, it always wise to seek appropriate training and, if possible, collaborate with someone who has land cover mapping experience (Section 2.3). Although the terms “land cover” and “land use” are sometimes used interchangeably they are different in important ways. Simply put, land cover is what covers the surface of the Earth and land use describes how people use the land (or water). Examples of land cover classes are: water, snow, grassland, deciduous forest, or bare soil.

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