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Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery 被引量:5
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作者 Chinsu Lin Chao-Cheng Wu +2 位作者 Khongor Tsogt Yen-Chieh Ouyang Chein-I Chang 《Information Processing in Agriculture》 EI 2015年第1期25-36,共12页
Changes of Land Use and Land Cover(LULC)affect atmospheric,climatic,and biological spheres of the earth.Accurate LULC map offers detail information for resources management and intergovernmental cooperation to debate ... Changes of Land Use and Land Cover(LULC)affect atmospheric,climatic,and biological spheres of the earth.Accurate LULC map offers detail information for resources management and intergovernmental cooperation to debate global warming and biodiversity reduction.This paper examined effects of pansharpening and atmospheric correction on LULC classification.Object-Based Support Vector Machine(OB-SVM)and Pixel-Based Maximum Likelihood Classifier(PB-MLC)were applied for LULC classification.Results showed that atmospheric correction is not necessary for LULC classification if it is conducted in the original multispectral image.Nevertheless,pansharpening plays much more important roles on the classification accuracy than the atmospheric correction.It can help to increase classification accuracy by 12%on average compared to the ones without pansharpening.PB-MLC and OB-SVM achieved similar classification rate.This study indicated that the LULC classification accuracy using PB-MLC and OB-SVM is 82%and 89%respectively.A combination of atmospheric correction,pansharpening,and OB-SVM could offer promising LULC maps from WorldView-2 multispectral and panchromatic images. 展开更多
关键词 LULC Remote sensing Object-based image analysis Pixel-based image analysis maximum likelihood classifier(mlc) Support vector machine(SVM)
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基于基本竞争型神经网络的TM影像分类研究 被引量:3
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作者 王璐 刘艳华 +2 位作者 刘振华 徐剑波 严会超 《西北农林科技大学学报(自然科学版)》 CSCD 北大核心 2009年第8期154-160,170,共8页
【目的】针对传统遥感图像分类方法精度低的缺点,运用基本竞争型神经网络模型对TM影像进行分类研究。【方法】在考虑TM影像光谱信息和地表结构变化信息的基础上,应用经过基本竞争型神经网络训练后的分类器对TM影像进行分类研究,并与利... 【目的】针对传统遥感图像分类方法精度低的缺点,运用基本竞争型神经网络模型对TM影像进行分类研究。【方法】在考虑TM影像光谱信息和地表结构变化信息的基础上,应用经过基本竞争型神经网络训练后的分类器对TM影像进行分类研究,并与利用最大似然法的分类结果进行比较。【结果】研究区TM影像采用基本竞争型神经网络进行分类的总体分类精度和Kappa系数分别为89.1%和0.873,而采用最大似然法分别为70.6%和0.646,前者的分类精度明显高于后者。【结论】基本竞争型神经网络的分类结果明显优于最大似然法的分类结果。 展开更多
关键词 TM影像分类 地表结构信息 基本竞争型神经网络 最大似然法
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Urbanization and Other Land Use Land Cover Change Assessment in the Greater Kumasi Area of Ghana
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作者 Addo Koranteng Isaac Adu-Poku +3 位作者 Bernard Fosu Frimpong Jack Nti Asamoah John Agyei Tomasz Zawiła-Niedźwiecki 《Journal of Geoscience and Environment Protection》 2023年第5期363-383,共21页
Urbanization posits the expression of urban expanse expansion due to population growth, rise in built-up areas, high population density and its correspondingly urban way of life. Unrestrained impetus of development an... Urbanization posits the expression of urban expanse expansion due to population growth, rise in built-up areas, high population density and its correspondingly urban way of life. Unrestrained impetus of development and land use land cover change (LULCC) portent several issues such as unlawful urban sprawl, loss of agricultural land, forest loss and other associated complications. This study analyzed the dynamics of urbanization and other LULCC in Ghana’s Greater Kumasi area via Landsat images (TM 1986, OLI 2013 and OLI 2023) using ERDAS Imagine, Idrisi and ArcGIS software. Implementing supervised classification technique, the Maximum Likelihood Classifier (MLC) procedure was employed to categories the study area into five LULC classes. Accuracy assessment undertaken on the resultant LULC maps was deemed very satisfactory. The results from 1986-2023 pointed to an upsurge in a built-up extent as of 8% to 41%, a decrease in Closed Forest from 9% to 4%, another decrease in Open Forests from 64% to 33%, a slight increase from 16% to 20% in farmlands and a stable level of water share. Further analysis indicated that the study area had undergone LULCC within the periods 1986-2013 and 2013-2023 at 60% and 37% respectively. The findings showed uncontrolled urban sprawling along major roads and forest loss as deforestation outside protected areas and degradation in protected forest. The monitoring of urbanization and other LULCC is important for local, and national governments and other bodies charged with the implementation of programs and policies that manage and utilize natural resources. Development adapts to mitigate the effect on the environment. 展开更多
关键词 URBANIZATION maximum likelihood classifier (mlc) Urban Sprawl Change Detection Forest Loss
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