本文選題：全極化　切入點(diǎn)：Radarsat-　出處：《中國(guó)農(nóng)業(yè)資源與區(qū)劃》2017年07期 　論文類型：期刊論文

【摘要】：[目的]為加快推動(dòng)貴州省"互聯(lián)網(wǎng)+"林業(yè)建設(shè),打破貴州喀斯特高原山區(qū)遙感監(jiān)測(cè)瓶頸,選取了空間分辨率8m的Radarsat-2全極化SAR數(shù)據(jù)與空間分辨率6m的SPOT 6多光譜影像作為數(shù)據(jù)源,探究微波與光學(xué)遙感結(jié)合在喀斯特山區(qū)農(nóng)村地區(qū)的林地提取技術(shù)。[方法]首先采用ENVI SARscape與NEST軟件對(duì)SAR圖像預(yù)處理。將Radarsat-2全極化數(shù)據(jù)與SPOT 6標(biāo)準(zhǔn)假彩色影像進(jìn)行HSV融合。計(jì)算融合圖像的平均梯度、信息熵、標(biāo)準(zhǔn)差與均值,評(píng)價(jià)出最優(yōu)融合效果的極化方式�；贙均值(K-means)與最大期望(EM聚類)聚類算法分割圖像,選擇合適的算法,基于聚類分割的閾值進(jìn)行面向?qū)ο蟮牧值胤诸�。最�?基于像素的混淆矩陣精度評(píng)價(jià),結(jié)合貴州省林業(yè)廳調(diào)查數(shù)據(jù)、野外樣方和航拍圖,建立參考樣本評(píng)價(jià)分類結(jié)果。[結(jié)果](1)融合之后,目視解譯出林地邊緣明顯但較粗糙;對(duì)于在林地中小面積建筑物、農(nóng)田中的較分散的林地小圖斑能夠識(shí)別,但邊緣粗糙;有林地和灌木林地在色調(diào)上區(qū)分明顯;在融合后的明度圖中的灌木林地有明度較大的像元,此類像元為石旮旯地。(2)通過(guò)定量分析,融合之后的影像較SAR和光學(xué)數(shù)據(jù)信息量大。同極化平均梯度大于交叉極化,HH極化方式下各指標(biāo)最大。圖像EM聚類分割比K-means聚類更加細(xì)化。EM聚類圖像的特征區(qū)分明顯;(3)研究分類出了有林地、灌木林地和其他林地。面向?qū)ο蟮牧值胤诸惪傮w分類精度達(dá)到85.71%。[結(jié)論]研究將微波與光學(xué)遙感結(jié)合,為喀斯特山區(qū)中農(nóng)村地區(qū)的林地提取提供新思路,與傳統(tǒng)的林地監(jiān)測(cè)相比,數(shù)據(jù)獲取快捷,提高工作效率,精度準(zhǔn)確。有助于通過(guò)遙感的手段解決地塊破碎區(qū)域的林地提取問(wèn)題,為提高多源遙感技術(shù)在喀斯特農(nóng)村地區(qū)中的林地智能監(jiān)測(cè)的能力提供借鑒。
[Abstract]:[objective] in order to speed up the construction of "Internet" forestry in Guizhou Province and break the bottleneck of remote sensing monitoring in Guizhou karst plateau mountain area, Radarsat-2 fully polarized SAR data with spatial resolution of 8 m and multispectral image of SPOT 6 with spatial resolution 6 m are selected as data sources. To explore the extraction technology of woodland in rural areas of karst mountain area by microwave and optical remote sensing. [methods] first, the SAR image was preprocessed by ENVI SARscape and NEST software. Radarsat-2 full polarization data and SPOT 6 standard pseudocolor image were used for HSV. Fusion. Calculate the average gradient of the fused image, Information entropy, standard deviation and mean value are used to evaluate the polarization mode of optimal fusion effect. Based on K-Means clustering algorithm and maximum expectation EM clustering algorithm, the image is segmented and the appropriate algorithm is selected. Finally, based on the accuracy evaluation of pixel confusion matrix, combining with the survey data of Guizhou Forestry Bureau, field sample and aerial map, the classification of forest land is carried out based on the threshold of clustering segmentation. Establishing reference samples to evaluate the classification results. [results] 1) after fusion, the forest land edge is obvious but rough, for the small and medium-sized buildings in the forest land, the scattered forest land small map spot in the farmland can be recognized, but the edge is rough; Forest land and shrub land are clearly differentiated in tone; in the fused brightness map, there are larger brightness pixels in the shrubbery area, which is called "Cooki.") quantitative analysis is made by means of quantitative analysis. Compared with SAR and optical data, the fused image has more information. The average gradient of the same polarization is larger than that of the cross polarization / HH polarization. The EM clustering segmentation of images is more detailed than K-means clustering. The feature areas of EM clustering images are clear. The study classifies the woodland. The overall classification accuracy of shrubbery land and other woodland is 85.71.Conclusion Microwave and optical remote sensing are combined to provide a new idea for woodland extraction in rural areas of karst mountain area, and compared with traditional forest monitoring, the classification accuracy of shrub land and other woodland classification is 85.71.Conclusion the study combines microwave with optical remote sensing. The data acquisition is quick, the work efficiency is improved, the precision is accurate. It is helpful to solve the woodland extraction problem in the broken area by remote sensing. It provides reference for improving the ability of intelligent monitoring of woodland in karst rural areas by multi-source remote sensing technology.
【作者單位】： 貴州師范大學(xué)喀斯特研究院;國(guó)家喀斯特石漠化防治工程技術(shù)研究中心;
【基金】：國(guó)家自然科學(xué)基金地區(qū)項(xiàng)目“喀斯特石漠化地區(qū)生態(tài)資產(chǎn)與區(qū)域貧困耦合機(jī)制研究”(41661088) 貴州省高層次創(chuàng)新型人才培養(yǎng)計(jì)劃——“百”層次人才(黔科合平臺(tái)人才[2016]5674) 貴州省科技計(jì)劃“基于北斗衛(wèi)星的山地高效農(nóng)業(yè)產(chǎn)業(yè)園區(qū)智能管理系統(tǒng)開發(fā)與應(yīng)用”(黔科合GY字[2015]3001) 國(guó)家遙感中心貴州分部平臺(tái)建設(shè)(黔科合計(jì)Z字[2012]4003)(黔科合計(jì)Z字[2013]003)
【分類號(hào)】：S757;TP751

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