Open AccessCombination of visible and thermal remotely sensed data for enhancement of Land Cover Classification by using satellite imagery
Author(s) -
Aysar Jameel Abdalkadhum,
Mohammad Mejbel Salih,
Oday Zakariya Jasim
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/737/1/012226
Subject(s) - remote sensing , land cover , multispectral image , ground truth , environmental science , support vector machine , thematic mapper , thematic map , satellite , contextual image classification , cohen's kappa , scale (ratio) , computer science , spectral bands , satellite imagery , land use , geography , cartography , artificial intelligence , image (mathematics) , machine learning , civil engineering , aerospace engineering , engineering
On a large scale, the land cover classification has been investigated throughout the world in remote sensing for different kinds of applications such as water resources, agricultural, environmental, as well as ecological and hydrological applications. In order enhance accuracy of the classification results, Landsat and multispectral bands are used to study the numerous classification methods. Remote sensing thermal data provides valuable information in order to examine the effectiveness of applying the thermal bands to extract useful land cover thematic maps. In this research, Landsat-8 satellite data captured by Operational Land Imager (OLI) and the Thermal Infrared (TIRS) Sensors, with using remotely sensing data and Geographic Information System (GIS) analysis with using ground truth data collect from fieldwork in same time of imagery capturing by using infrared thermometer camera. In 2018, single date Landsat-8 image of the study area in Iraq was captured in winter. This image is used to estimate Land Surface Temperature (LST) by split window algorithm and performing Land Cover (LC) classification after image noise removal by using supervised classification algorithms Support Vector Machine (SVM) with multi-spectral and thermal bands combinations to find out which one has more accuracy. Result shows the effective and efficiency of the proposed method compared by traditional classification methods. The overall accuracy and Kappa coefficient are 94.25%, 64.43% and 0.93, 0.63, respectively.