DOI: 10.1016/j.jag.2014.06.003
Scopus记录号: 2-s2.0-84904753263
论文题名: An ensemble pansharpening approach for finer-scale mapping of sugarcane with Landsat 8 imagery
作者: Johnson B ; A ; , Scheyvens H ; , Shivakoti B ; R
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2014
卷: 33, 期: 1 起始页码: 218
结束页码: 225
语种: 英语
英文关键词: IHS
; Image fusion
; Landsat 8
; Pansharpening
; Sugarcane mapping
; SVM
Scopus关键词: accuracy assessment
; ensemble forecasting
; image analysis
; image classification
; Landsat
; multispectral image
; pixel
; regression analysis
; satellite imagery
; wavelet
英文摘要: We tested the effects of three fast pansharpening methods - Intensity-Hue-Saturation (IHS), Brovey Transform (BT), and Additive Wavelet Transform (AWT) - on sugarcane classification in a Landsat 8 image (bands 1-7), and proposed two ensemble pansharpening approaches (band stacking and band averaging) which combine the pixel-level information of multiple pansharpened images for classification. To test the proposed ensemble pansharpening approaches, we classified "sugarcane" and "other" land cover in the unsharpened Landsat multispectral image, the individual pansharpened images, and the bandstacked and band-averaged ensemble images using Support Vector Machines (SVM), and assessed the classification accuracy of each image. Of the individual pansharpened images, the AWT image achieved higher classification accuracy than the unsharpened image, while the IHS and BT images did not. The band-stacked ensemble images achieved higher classification accuracies than the unsharpened and individual pansharpened images, with the IHS-BT-AWT band-stacked image producing the most accurate classification result, followed by the IHS-BT band-stacked image. The ensemble images containing averaged pixel values from multiple pansharpened images achieved lower classification accuracies than the band-stacked ensemble images, but most still had higher accuracies than the unsharpened and individual pansharpened results. Our results indicate that ensemble pansharpening approaches have the potential to increase classification accuracy, at least for relatively simple classification tasks. Based on the results of the study, we recommend further investigation of ensemble pansharpening for image analysis (e.g. classification and regression tasks) in agricultural and non-agricultural environments. © 2014 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79647
Appears in Collections: 气候变化事实与影响
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作者单位: Institute for Global Environmental Strategies, 2108-11 Kamiyamaguchi, Hayama, Kanagawa 240-0115, Japan
Recommended Citation:
Johnson B,A,, Scheyvens H,et al. An ensemble pansharpening approach for finer-scale mapping of sugarcane with Landsat 8 imagery[J]. International Journal of Applied Earth Observation and Geoinformation,2014-01-01,33(1)