A comparative analysis of PlanetScope 4-band and 8-band imageries for land use land cover classification

被引：0

作者：

Basheer, Sana ^{[1
,2
]}

Wang, Xiuquan ^{[1
,2
,3
]}

Nawaz, Rana Ali ^{[1
,2
]}

Pang, Tianze ^{[2
,3
]}

Adekanmbi, Toyin ^{[2
,3
]}

Mahmood, Muhammad Qasim ^{[2
,3
]}

机构：

[1] Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, C1A 4P3, PE

[2] Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St. Peter's Bay, C0A 2A0, PE

[3] School of Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, C1A 4P3, PE

来源：

Geomatica | 2024年 / 76卷 / 02期

基金：

加拿大创新基金会; 加拿大自然科学与工程研究理事会;

关键词：

Classification; GIS; Machine Learning; PlanetScope; Remote Sensing;

D O I：

10.1016/j.geomat.2024.100023

中图分类号：

学科分类号：

摘要：

Earth-observing satellites have become essential in comprehending human impacts on the landscape. Satellite-based imagery is indispensable for mapping Earth's features, managing resources, and studying environmental changes. Readily available remote sensing data with improved radiometric, spectral, spatial, and temporal resolution presents opportunities for advanced data analysis. Precise and accurate land use land cover (LULC) information is essential for the surveillance of environmental conditions and the effective management of natural resources. This research assesses the performance of PlanetScope product SuperDove sensor (PSB.SD), having two different band combinations, including 4-band (Red, Blue, Green and Near-Infrared (NIR)) and 8-band (Blue, Green II, Red, NIR, Coastal Blue, Green, Yellow, Red-Edge) imagery in ArcGIS Pro for the month of July 2021. Four different supervised classifiers, including support vector machine (SVM), k-nearest neighbours (KNN), random forest (RF), and maximum likelihood (ML) classifiers. This study was carried out for the three major areas, i.e., City of Summerside, City of Charlottetown, and Town of Three Rivers in Prince Edward Island (PEI), Canada and LULC classification scheme consists of six major classes, which include Agriculture, Forest, Vegetation, Bare Land, Urban and Water bodies. For accuracy assessment, overall accuracy as well as kappa coefficient were estimated to identify the most accurate combination of LULC classifier and different band combination imagery from PlanetScope. Results show that the highest overall accuracy of 0.94 for Town of Three Rivers and 0.93 for City of Summerside and City of Charlottetown were observed using 8-band imagery with SVM classifier. The lowest overall accuracy of 0.78 for Town of Three Rivers, 0.83 for City of Charlottetown, and 0.82 for City of Summerside was observed using 4-band imagery using ML classifier. Further, the SVM classifier performs well in accuracy with 8-band imagery of PlanetScope, showcasing its potential in LULC classification compared to previous PlanetScope 4-band imagery. © 2024 The Authors

引用

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