發(fā)布時間：2018-03-29 14:43

  本文選題：分類算法　切入點：農(nóng)作物　出處：《電子科技大學(xué)》2015年碩士論文

【摘要】：及時掌握農(nóng)作物種植分布情況,對于宏觀指導(dǎo)農(nóng)業(yè)生產(chǎn)、合理采取農(nóng)作物管理措施有重要的現(xiàn)實意義。如何提高作物種植區(qū)信息提取精度是近年來遙感科學(xué)關(guān)注的重要課題之一。本文以華北平原為研究區(qū),結(jié)合相應(yīng)作物的物候信息,首先利用國產(chǎn)風(fēng)云衛(wèi)星中分辨率數(shù)據(jù),基于分層構(gòu)建決策樹的方法來提取裸地和冬小麥種植區(qū)分布圖,在此基礎(chǔ)上利用MERSI NDVI旬產(chǎn)品,建立多作物提取模型,獲取不同年份的農(nóng)作物種植分布情況。主要研究工作如下:(1)基于分層構(gòu)建決策樹的冬小麥種植區(qū)提取。首先選取冬小麥生長季中多幅數(shù)據(jù)質(zhì)量好的MERSI數(shù)據(jù),采用分層提取的方法,對于不同的層次選用與待提取類別最為敏感的特征波段來構(gòu)建相應(yīng)的決策樹,從中將每一幅影像中冬小麥和裸地提取出來,然后將多幅數(shù)據(jù)融合為一幅生長季內(nèi)的冬小麥種植區(qū)和裸地分布圖,最后采用野外實地調(diào)查的數(shù)據(jù)和LANDSAT 8影像解譯數(shù)據(jù)對提取結(jié)果進(jìn)行精度驗證,并對比和分析分層構(gòu)建決策樹與其他分類方法的優(yōu)劣。提取方法確定后,倒推回2010年,做出不同年份的冬小麥種植分布圖。研究表明,采用分層構(gòu)建決策樹獲取的精度比其他的分類方法相對較好,其中得到2013-2014年度的裸地、冬小麥和總體提取精度最好,分別達(dá)到91.80%,90.19%,90.90%,同時從縣域尺度上,MERSI數(shù)據(jù)提取的冬小麥和裸地與LANDSAT 8影像在空間分布上,大體一致。(2)基于NDVI時序的多作物種植區(qū)提取。首先生成不同年份的250米分辨率的MERSI NDVI旬產(chǎn)品,再利用提取出不同年份的冬小麥種植區(qū)和裸地對選取的NDVI影像進(jìn)行掩膜處理,以求取華北平原春玉米、夏玉米和棉花關(guān)鍵生育期內(nèi)NDVI變化曲線,并結(jié)合相應(yīng)作物的物候信息,建立提取模型,最后利用野外采集的數(shù)據(jù)和LANDSAT 8影像解譯數(shù)據(jù)對結(jié)果進(jìn)行精度驗證,結(jié)果表明,不同年份總體提取都達(dá)到84%以上,從250米遙感制圖及作物遙感監(jiān)測方面來說,精度能滿足需求,同時從縣域尺度上,獲取的作物種植分布與LANDSAT 8影像大體一致。(3)分類系統(tǒng)設(shè)計與實現(xiàn)。在了解本文所涉及的分類算法和研究成果基礎(chǔ)上,結(jié)合數(shù)據(jù)的處理框架,從系統(tǒng)應(yīng)用需求和設(shè)計思路上合理開發(fā)相應(yīng)的分類系統(tǒng),該系統(tǒng)由3個模塊組成,包括訓(xùn)練樣本數(shù)據(jù)格式轉(zhuǎn)換模塊、分類方法選擇模塊和數(shù)據(jù)融合處理模塊。
[Abstract]:To grasp the distribution of crop cultivation in a timely manner, and to guide agricultural production at a macro level, It is of great practical significance to rationally take crop management measures. How to improve the precision of information extraction in crop growing areas is one of the important topics of scientific concern in remote sensing in recent years. In this paper, the North China Plain is taken as the research area and the phenological information of the corresponding crops is combined. Based on the resolution data of domestic wind-cloud satellite and the method of constructing decision tree in layers, the distribution map of bare land and winter wheat planting area is extracted. On this basis, the multi-crop extraction model is established by using MERSI NDVI ten-day products. To obtain the distribution of crop planting in different years. The main research work is as follows: 1) extracting winter wheat planting area based on hierarchical decision tree. Firstly, MERSI data with good quality in winter wheat growing season are selected. By using the method of hierarchical extraction, the decision tree is constructed by selecting the most sensitive feature bands for different levels and extracting winter wheat and bare land from each image. Then, several pieces of data were fused into a map of winter wheat growing area and bare land in growing season. Finally, the accuracy of the extracted data was verified by field survey data and LANDSAT 8 image interpretation data. The advantages and disadvantages of hierarchical decision tree construction and other classification methods are compared and analyzed. After the determination of the extraction method, the distribution map of winter wheat planting in different years is drawn back to 2010. The precision obtained by using hierarchical decision tree is better than that of other classification methods. The bare land of 2013-2014 is obtained, and the precision of winter wheat and total extraction is the best. They reached 91.80 and 90.19, respectively. At the same time, winter wheat and bare land and LANDSAT 8 images extracted from the county scale were distributed in space. The extraction of multi-crop planting area is based on NDVI sequence. Firstly, 250m resolution MERSI NDVI products are generated in different years, and then the selected NDVI images are masked by extracting winter wheat planting areas in different years and bare land. In order to obtain the NDVI variation curves of spring corn, summer maize and cotton in North China Plain, and combined with the phenological information of the corresponding crops, the extraction model was established. Finally, the accuracy of the result is verified by the data collected in the field and interpreted by LANDSAT 8 image. The results show that the total extraction in different years is more than 84%, and the precision can meet the demand in terms of 250m remote sensing mapping and crop remote sensing monitoring. At the same time, on the county scale, the obtained crop planting distribution is roughly consistent with the LANDSAT 8 image. The classification system is designed and implemented. On the basis of understanding the classification algorithms and research results involved in this paper, combined with the data processing framework, The system consists of three modules, including training sample data format conversion module, classification method selection module and data fusion processing module.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：S512.11;S127
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本文編號：1681591