%0 Conference Proceedings
%T Analysis of Land Cover Type Using Landsat-8 Data
%+ Department of Computer Science, Amrita Vishwa Vidyapeetham, Bangalore, India
%A Samuktha, V.
%A Sabeshnav, M.
%A Krishna Sameera, A.
%A Aravinth, J.
%A Veni, S.
%Z Part 1: Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT)
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 4th International Conference on Computational Intelligence in Data Science (ICCIDS)
%C Chennai, India
%Y Vallidevi Krishnamurthy
%Y Suresh Jaganathan
%Y Kanchana Rajaram
%Y Saraswathi Shunmuganathan
%I Springer International Publishing
%3 Computational Intelligence in Data Science
%V AICT-611
%P 78-89
%8 2021-03-18
%D 2021
%R 10.1007/978-3-030-92600-7_8
%K USGS
%K Landsat 8
%K GEE
%K CART algorithm
%K Reflectance
%Z Computer Science [cs]Conference papers
%X Classification of images attributes to categorizing of images into various predefined groups. A particular image can be grouped into several diverse classes. Examining and ordering the images manually is a tiresome job particularly when they are abundant and therefore, automating the entire process using image processing and computer vision would be very efficient and useful. In this study, the Classifier and Regression trees (CART) algorithm is used to create a classifier model that classifies a region based on the feature specified. The Google Earth Engine (GEE) platform is utilized to conduct the study. The Tier 1 USGS Landsat 8 surface reflectance dataset is employed and is sorted according to the cloud cover. The features are then extracted and are merged to obtain a feature collection. This input imagery is further sampled using particular bands from the Landsat imagery to get a renewed feature collection of training data and the classifier model is trained using the CART Algorithm. An accuracy assessment is further performed to determine the exactness of the proposed model and the results are plotted using a confusion matrix. By applying the CART algorithm for image classification, an accuracy of 83% is achieved which was found to be better than the existing results.
%G English
%Z TC 12
%2 https://inria.hal.science/hal-03772946/document
%2 https://inria.hal.science/hal-03772946/file/512058_1_En_8_Chapter.pdf
%L hal-03772946
%U https://inria.hal.science/hal-03772946
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC12
%~ IFIP-ICCIDS
%~ IFIP-AICT-611