Land use/cover Classification and Accuracy Assessment in Envi5.1 #Geo-InfoNepal
10.4 هزار بار بازدید -
5 سال پیش
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1.Land use/Land cover:Identifying the physical
1.Land use/Land cover:Identifying the physical aspect of the earth’s surface (Land cover) as well as how we exploit the land (Land use) is a challenging problem in environment monitoring and many other subdomains. This can be done through field surveys or analyzing satellite images(Remote Sensing). While carrying out field surveys is more comprehensive and authoritative, it is an expensive project and mostly takes a long time to update.With recent developments in the Space industry and the increased availability of satellite images (both free and commercial), deep learning and Convolutional Neural Networks has shown a promising result in land use classification.
2.Accuracy Assessment :In the context of information extraction by image analysis, accuracy “measures the agreement between a standard assumed to be correct and a classified image of unknown quality.” (Campbell, 2007) Precision defines the level of detail found within the classification. It is possible to increase the accuracy of a classification by decreasing the amount of detail or by generalizing to broad classes rather than very specific ones. For example, a scheme which generally categorizes trees vs. crops presents less opportunity for classification error than one that distinguishes many types of trees and many types of crops. In this case, lower precision provides the potential for higher accuracy. However, the user of the map that offers only general classes cannot make precise statements about any given point on the map.
Classification error occurs when a pixel (or feature) belonging to one category is assigned to another category. Errors of omission occur when a feature is left out of the category being evaluated; errors of commission occur when a feature is incorrectly included in the category being evaluated. An error of omission in one category will be counted as an error in commission in another category.
source :https://www.e-education.psu.edu/geog8...
:https://bit.ly/2YuwkhM
2.Accuracy Assessment :In the context of information extraction by image analysis, accuracy “measures the agreement between a standard assumed to be correct and a classified image of unknown quality.” (Campbell, 2007) Precision defines the level of detail found within the classification. It is possible to increase the accuracy of a classification by decreasing the amount of detail or by generalizing to broad classes rather than very specific ones. For example, a scheme which generally categorizes trees vs. crops presents less opportunity for classification error than one that distinguishes many types of trees and many types of crops. In this case, lower precision provides the potential for higher accuracy. However, the user of the map that offers only general classes cannot make precise statements about any given point on the map.
Classification error occurs when a pixel (or feature) belonging to one category is assigned to another category. Errors of omission occur when a feature is left out of the category being evaluated; errors of commission occur when a feature is incorrectly included in the category being evaluated. An error of omission in one category will be counted as an error in commission in another category.
source :https://www.e-education.psu.edu/geog8...
:https://bit.ly/2YuwkhM
5 سال پیش
در تاریخ 1398/01/06 منتشر شده
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