發(fā)布時間：2018-03-04 13:37

  本文選題：GF-遙感影像　切入點：BP神經(jīng)網(wǎng)絡(luò)　出處：《中國農(nóng)村水利水電》2017年02期 　論文類型：期刊論文

【摘要】：為探討國產(chǎn)GF-1衛(wèi)星影像在干旱區(qū)土壤鹽漬化監(jiān)測中的適用性,以渭-庫綠洲外圍荒漠交錯帶為研究對象,利用BP神經(jīng)網(wǎng)絡(luò)和RBF神經(jīng)網(wǎng)絡(luò)2種建模算法,以GF-1影像的4個波段的反射率及影像提取的歸一化差異植被指數(shù)(NDVI)、差值植被指數(shù)(DVI)、土壤調(diào)節(jié)植被指數(shù)(SAVI)、鹽度指數(shù)(SI1、SI2、SI-T)共10個指標(biāo)構(gòu)建土壤含鹽量反演模型。結(jié)果表明:在2種算法中,BP神經(jīng)網(wǎng)絡(luò)模型預(yù)測精度最高,R2為0.818,RMSE為0.194;發(fā)現(xiàn)利用植被指數(shù)更能提高模型的預(yù)測精度;利用BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型反演研究區(qū)的土壤含鹽量,發(fā)現(xiàn)預(yù)測情況與研究區(qū)實際情況相符,說明利用GF-1數(shù)據(jù)結(jié)合BP神經(jīng)網(wǎng)絡(luò)構(gòu)建的反演模型適用于監(jiān)測研究區(qū)土壤鹽漬化問題。
[Abstract]:In order to study the applicability of domestic GF-1 satellite images in soil salinization monitoring in arid areas, two modeling algorithms, BP neural network and RBF neural network, were used to study the desert ecotone around Wei-ku oasis. Based on the reflectivity of four bands of GF-1 image and the normalized difference vegetation index (NDVI) extracted from the image, the difference vegetation index (DVI), the soil regulation vegetation index (Savi) and the salinity index (SI 1 / SI 2 / SI-T), a soil salt content inversion model was constructed. In the two algorithms, the prediction accuracy of BP neural network model is the highest (R ~ 2 = 0.818) and RMSE is 0.194. It is found that vegetation index can improve the prediction accuracy of the model. The prediction model of BP neural network is used to invert the soil salt content in the study area, and it is found that the prediction is consistent with the actual situation of the study area, which indicates that the inversion model based on GF-1 data combined with BP neural network is suitable for monitoring soil salinization in the study area.
【作者單位】： 新疆大學(xué)資源與環(huán)境科學(xué)學(xué)院;綠洲生態(tài)教育部重點實驗室;
【基金】：國家自然科學(xué)基金項目(U1303381,4126090,41161063) 教育部長江學(xué)者計劃創(chuàng)新團(tuán)隊計劃(IRT1180) 自治區(qū)科技支疆項目(201504051064) 自治區(qū)重點實驗室專項基金(2014KL005) 高分辨率對地觀測重大專項(民用部分)(95-Y40B02-9001-13/15-03-01)
【分類號】：S156.41;S127

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