本文選題：GF-遙感影像　切入點(diǎn)：BP神經(jīng)網(wǎng)絡(luò)　出處：《中國(guó)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(guó)產(chǎn)GF-1衛(wèi)星影像在干旱區(qū)土壤鹽漬化監(jiān)測(cè)中的適用性,以渭-庫(kù)綠洲外圍荒漠交錯(cuò)帶為研究對(duì)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(gè)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(gè)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(cè)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(cè)精度;利用BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型反演研究區(qū)的土壤含鹽量,發(fā)現(xiàn)預(yù)測(cè)情況與研究區(qū)實(shí)際情況相符,說(shuō)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(cè)研究區(qū)土壤鹽漬化問(wèn)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(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(U1303381,4126090,41161063) 教育部長(zhǎng)江學(xué)者計(jì)劃創(chuàng)新團(tuán)隊(duì)計(jì)劃(IRT1180) 自治區(qū)科技支疆項(xiàng)目(201504051064) 自治區(qū)重點(diǎn)實(shí)驗(yàn)室專項(xiàng)基金(2014KL005) 高分辨率對(duì)地觀測(cè)重大專項(xiàng)(民用部分)(95-Y40B02-9001-13/15-03-01)
【分類號(hào)】：S156.41;S127

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