發(fā)布時(shí)間：2018-01-17 23:35

  本文關(guān)鍵詞：多種光譜指標(biāo)構(gòu)建決策樹(shù)的水稻種植面積提取　出處：《江蘇農(nóng)業(yè)學(xué)報(bào)》2016年05期 　論文類(lèi)型：期刊論文

  更多相關(guān)文章： 水稻 多光譜遙感 決策樹(shù)分類(lèi) 種植面積提取

【摘要】：合理選取不同光譜指標(biāo)制定決策樹(shù)規(guī)則,能有效提高決策樹(shù)分類(lèi)法提取水稻面積的精度。本研究以江蘇省淮安市為例,選取30 m空間分辨率HJ1A和16 m空間分辨率GF1多光譜影像,在對(duì)不同地物樣點(diǎn)像元光譜特征分析的基礎(chǔ)上,選擇地物光譜特征明顯的GF影像計(jì)算NDVI、EVI、DVI和RVI,并提取影像近紅外波段反射率,利用上述5種光譜指標(biāo)確定不同地物分類(lèi)閾值來(lái)對(duì)兩景影像進(jìn)行決策樹(shù)分類(lèi),進(jìn)而獲取淮安市水稻面積和分布情況。結(jié)果表明,GF影像地物光譜特征較明顯,有利于識(shí)別不同地物,可用來(lái)確定基于多種光譜指標(biāo)分類(lèi)的閾值范圍。其中,水稻判別條件為NDVI0.70,0.25DVI≤0.45,0.53EVI≤0.80,RVI5.5且0.30ρNIR≤0.46。HJ影像和GF影像提取水稻面積的樣本精度分別為87.29%和93.70%,GF影像比HJ影像的水稻面積提取精度提高了6.41個(gè)百分點(diǎn),說(shuō)明利用多種光譜指標(biāo)構(gòu)建決策樹(shù)分類(lèi)模型是一種有效提取水稻種植面積的方法。
[Abstract]:Reasonable selection of different spectral indicators to make decision tree rules can effectively improve the precision of rice area extraction by decision tree classification. This study takes Huaian City Jiangsu Province as an example. The 30 m spatial resolution HJ1A and 16 m spatial resolution GF1 multispectral images are selected, and the spectral characteristics of the pixels of different ground objects are analyzed. Select GF image with obvious spectral characteristics to calculate DVI and RVI, and extract the near infrared reflectance of the image. The above five spectral indexes were used to determine the threshold of different ground objects classification to classify the two scene images, and then to obtain the rice area and distribution in Huai'an City. The results showed that the spectral characteristics of ground objects in GF images were obvious. It can be used to determine the threshold range of classification based on various spectral indexes, and the rice discriminant condition is NDVI 0.70 / 0.25DVI 鈮，

本文編號(hào)：1438507