Journal Article

Accuracy of pixel-based classification: application of different algorithms to landscapes of Western Iran

Azadi, Hossein, Khoshnood, Sajad, Yaghobi, Soraya, Daneshi, Alireza

2023

Overview

Scenarios for monitoring land cover on a large scale, involving large volumes of data, are becoming more prevalent in remote sensing applications. The accuracy of algorithms is important for environmental monitoring and assessments. Because they performed equally well throughout the various research regions and required little human involvement during the categorization process, they appear to be resilient and accurate for automated, big area change monitoring. Malekshahi City is one of the important and at the same time critical areas in terms of land use change and forest area reduction in Ilam Province. Therefore, this study aimed to compare the accuracy of nine different methods for identifying land use types in Malekshahi City located in Western Iran. Results revealed that the artificial neural network (ANN) algorithm with back-propagation algorithms could reach the highest accuracy and efficiency among the other methods with kappa coefficient and overall accuracy of approximately 0.94 and 96.5, respectively. Then, with an overall accuracy of about 91.35 and 90.0, respectively, the methods of Mahalanobis distance (MD) and minimum distance to mean (MDM) were introduced as the next priority to categorize land use. Further investigation of the classified land use showed that good results can be provided about the area of the land use classes of the region by applying the ANN algorithm due to high accuracy. According to those results, it can be concluded that this method is the best algorithm to extract land use maps in Malekshahi City because of high accuracy.

• Publisher: Springer International Publishing,Springer Nature B.V,Springer Science and Business Media Deutschland GmbH
• Subject: Accuracy / Agriculture & agronomie / Agriculture & agronomy / Algorithms / Artificial neural network algorithm / Artificial neural networks / Atmospheric Protection/Air Quality Control/Air Pollution / automation / Back propagation networks / Cities / Classification / Codes / Distance / Earth and Environmental Science / Ecology / Ecotoxicology / Environment / Environmental Management / Environmental Monitoring / Environmental Monitoring - methods / Environmental science / Environmental Science (all) / forests / General Environmental Science / General Medicine / High-accuracy / Humans / Iran / Land cover / Land use / land use change / Land use classification / Landsat imagery / Landuse classifications / Large-scales / Life sciences / Management, Monitoring, Policy and Law / Methods / Monitoring/Environmental Analysis / Neural networks / Neural Networks, Computer / Overall accuracies / Pixel based classifications / Pollution / Protected areas / Remote sensing / Sciences du vivant / Statistical analysis / Support vector machines / Western Iran