Land Use/Land Cover Classification of Google Earth Imagery

被引：0

作者：

Sowmya, D. R. ^{[1
]}

Hegde, Vishwas S. ^{[1
]}

Suhas, J. ^{[1
]}

Hegdekatte, Raghavendra V. ^{[1
]}

Shenoy, P. Deepa ^{[1
]}

Venugopal, K. R. ^{[1
]}

机构：

[1] Bangalore Univ, Univ Visvesvaraya Coll Engn, Dept Comp Sci & Engn, Bengaluru, India

来源：

2017 IEEE INTERNATIONAL WIE CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (IEEE WIECON-ECE 2017) | 2017年

关键词：

Classification Accuracy; Google Earth Imagery; Land Use/Land Cover; K-Nearest Neighbour; User's accuracy;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Google Earth is a source of high spatial resolution images. The freely available Google Earth (GE) images are utilized to generate Land use/Land cover thematic map of the highly heterogeneous landscape of typical urban scene. In this paper, we have presented Euclidean Distance and Average Pixel Intensity based K-NN classification to classify five different land objects. The classification accuracy of the proposed method is compared against generic K-NN. The overall classification accuracy and the kappa value of generic K-NN are found to be 75.04% and 0.74 respectively. Whereas, proposed method results with 76.38% and 0.78. Both the methods exhibits classification error because of poor spectral reflectance properties of google earth imagery.

引用

页码：10 / 13

页数：4

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