These problems of nonlinearity, fuzziness and few labeled data were rarely considered in traditional remote sensing image classification. A semi-supervised kernel fuzzy C-means （SSKFCM） algorithm is proposed to over...These problems of nonlinearity, fuzziness and few labeled data were rarely considered in traditional remote sensing image classification. A semi-supervised kernel fuzzy C-means （SSKFCM） algorithm is proposed to overcome these disadvantages of remote sensing image classification in this paper. The SSKFCM algorithm is achieved by introducing a kernel method and semi-supervised learning technique into the standard fuzzy C-means （FCM） algorithm. A set of Beijing-1 micro-satellite＇s multispectral images are adopted to be classified by several algorithms, such as FCM, kernel FCM （KFCM）, semi-supervised FCM （SSFCM） and SSKFCM. The classification results are estimated by corresponding indexes. The results indicate that the SSKFCM algorithm significantly improves the classification accuracy of remote sensing images compared with the others.展开更多
Soil water management plays an important role in the response of kiwi plants （Actinidia deliciosa A. Chev.）. In GuimarSes district soil moisture content is monitored in kiwifi＇uit orchard as a routine parameter. Dr...Soil water management plays an important role in the response of kiwi plants （Actinidia deliciosa A. Chev.）. In GuimarSes district soil moisture content is monitored in kiwifi＇uit orchard as a routine parameter. Drip irrigation system is the method used. This crop tends to have high water requirements and extends over a wide area in Portugal, requiring innovative solutions to achieve better benefits. A method that correlates soil and crop conditions with the parameters of remote sensing was established in this study. To assess the level of accuracy of soil moisture measurements from satellites, it is important to compare satellite image with ground real data （namely the frequency domain reflectometry （FDR）, Diviner 2000）. The combination of multispectral satellite images produces an image representative of vegetation vigour, density and health. In this study, Landsat satellite images （2011 and 2013） are used and vegetation indexes are calculated for different periods of time, using the software Idrisi Taiga. The information of vegetation indexes is crossed with data of soil moisture, in situ, to establish a correlation between both of them. Thus, it allows to improve the soil water content monitoring, in particular for the soil water balance optimization and its effect on kiwi biornass production.展开更多
基金Supported by the National High Technology Research and Development Programme （No.2007AA12Z227） and the National Natural Science Foundation of China （No.40701146）.
文摘These problems of nonlinearity, fuzziness and few labeled data were rarely considered in traditional remote sensing image classification. A semi-supervised kernel fuzzy C-means （SSKFCM） algorithm is proposed to overcome these disadvantages of remote sensing image classification in this paper. The SSKFCM algorithm is achieved by introducing a kernel method and semi-supervised learning technique into the standard fuzzy C-means （FCM） algorithm. A set of Beijing-1 micro-satellite＇s multispectral images are adopted to be classified by several algorithms, such as FCM, kernel FCM （KFCM）, semi-supervised FCM （SSFCM） and SSKFCM. The classification results are estimated by corresponding indexes. The results indicate that the SSKFCM algorithm significantly improves the classification accuracy of remote sensing images compared with the others.
文摘Soil water management plays an important role in the response of kiwi plants （Actinidia deliciosa A. Chev.）. In GuimarSes district soil moisture content is monitored in kiwifi＇uit orchard as a routine parameter. Drip irrigation system is the method used. This crop tends to have high water requirements and extends over a wide area in Portugal, requiring innovative solutions to achieve better benefits. A method that correlates soil and crop conditions with the parameters of remote sensing was established in this study. To assess the level of accuracy of soil moisture measurements from satellites, it is important to compare satellite image with ground real data （namely the frequency domain reflectometry （FDR）, Diviner 2000）. The combination of multispectral satellite images produces an image representative of vegetation vigour, density and health. In this study, Landsat satellite images （2011 and 2013） are used and vegetation indexes are calculated for different periods of time, using the software Idrisi Taiga. The information of vegetation indexes is crossed with data of soil moisture, in situ, to establish a correlation between both of them. Thus, it allows to improve the soil water content monitoring, in particular for the soil water balance optimization and its effect on kiwi biornass production.
