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supervised classification in Chinese

Pronunciation:
How to pronounce "supervised classification""supervised classification" in a sentence

Translationmobile phoneMobile

  • 監(jiān)督分類法
  • 臨督分類

Examples

  • Studies on the purification of training samples in supervised classification by mode filtering
    利用眾數(shù)濾波對監(jiān)督分類訓(xùn)練樣本純化的研究
  • The image of nanchuan in 2000 was interpreted using a multi - extracting method combining supervised classification with non - supervised classification
    利用監(jiān)督分類和非監(jiān)督分類相結(jié)合的分層提取分類方法對南川市2000年遙感影像進(jìn)行解譯。
  • The image of nanchuan in 2000 was interpreted using a multi - extracting method combining supervised classification with non - supervised classification
    摘要利用監(jiān)督分類和非監(jiān)督分類相結(jié)合的分層提取分類方法對南川市2000年遙感影像進(jìn)行解譯。
  • Supported by the analysis and advance process to the geographical data using gis software , the paper discusses the question that whether the accuracy of bayes supervised classification will be improved considering the influence of the prior probability
    本文嘗試?yán)胓is軟件對地理數(shù)據(jù)進(jìn)行分析和預(yù)處理,對考慮先驗概率是否提高bayes監(jiān)督分類精度這一問題作了探討。
  • The proportion based on the assistant data is used as the prior probability to replace the prior value in the conventional supervised classification ; the farther iterative prior probability is applied into classifying progress on landsat tm image
    由輔助數(shù)據(jù)中計算各類別面積比率作為先驗概率,替換傳統(tǒng)監(jiān)督分類中的先驗值,并進(jìn)一步對先驗概率進(jìn)行迭代,最后利用改進(jìn)的先驗概率對landsattm影像進(jìn)行分類實驗。
  • Taken the northern slopes of the tianshan mountains as the research area , the multi - source remote sensing data were applied to investigate and classify in rangeland resources types based on supervised classification method ( mlc ) combined with " 3s " technical platform
    摘要利用多源遙感數(shù)據(jù)和" 3s "技術(shù)平臺，采用監(jiān)督分類法對天山北坡山體垂直帶內(nèi)草地資源進(jìn)行分類并獲取資源分布格局圖，探討多源遙感數(shù)據(jù)在草地資源類型分類中的應(yīng)用程度和發(fā)展前景。
  • The paper establishes a ecological transect , with a length of 90 km and width of 16 km , utilizing the part of tm data on 16th august 1998 , from harbin to the natural secondary forests in maoershan , heilongjiang province . with supervised classification , the tm data which had been corrected and enhanced via erdas , combining the data of gps in the field , was classified by seven types including : residential area , glebe , paddy field
    本論文利用1998年8月16日的tm衛(wèi)星數(shù)據(jù)的一部分，設(shè)置從黑龍江省哈爾濱市到帽兒山天然次生林區(qū)，長90km ，寬16km的生態(tài)研究樣帶。通過用erdas對衛(wèi)星數(shù)據(jù)的校正、圖像增強(qiáng)等處理，結(jié)合gps野外實地調(diào)查數(shù)據(jù)，進(jìn)行有監(jiān)督分類。共分為7種類型：居民點、旱田、水田、河流、灌叢、闊葉林、針葉林，并建立了衛(wèi)星影像分類解譯標(biāo)志。
  • The paper has made detailed observation by the existing apparatus and the field observation stations . based on the image and the vector database of the yutian oasis , the study has obtained the precise coordinates of investigation points by gps technique , and overlapped them with the images . after classifying the images with the maximum likelihood supervised classification method , we import the result into cis software , and analyze the yutian oasis changes about land cover " s quantity , quality and spatial position from 1976 to 2001
    本文利用現(xiàn)有儀器設(shè)備和野外觀測網(wǎng)站進(jìn)行詳細(xì)觀測，在建立于田綠洲圖像庫和矢量數(shù)據(jù)庫的基礎(chǔ)上，運用gps技術(shù)取得野外考察樣點的精確坐標(biāo)并與研究區(qū)各時期影像疊加，對影像進(jìn)行了最大似然法監(jiān)督分類，并將分類結(jié)果轉(zhuǎn)入gis軟件處理，全面分析了1976年至2001年該綠洲的土地覆蓋類型數(shù)量、質(zhì)量與空間上的變化。
  • The result indicates that the precision of msavi method is 79 . 6 % , higher than that of supervised classification and unsupervised classification . such precision is able to meet the requirement of monitoring on grassland desertification at large scale based remote sensing data . all the study is based on tm images on 11th august 1987 and 9th august 2001 . the grassland desertification in the middle part of naiman of inner mongolia was taken as a detailed case study to discuss the discipline and driving forces of the dynamic of the grassland desertification during two periods
    采用上述研究方法，利用1987年8月11日和2001年8月9日兩時相的tm數(shù)據(jù)，對奈曼旗中部十個鄉(xiāng)鎮(zhèn)的草地沙化動態(tài)進(jìn)行了分析，結(jié)果表明研究區(qū)草地嚴(yán)重沙化的總體趨勢得到控制，并且開始逆轉(zhuǎn)，生態(tài)環(huán)境建設(shè)已經(jīng)產(chǎn)生了成效，但還存在著局部的惡化趨勢，草地沙化發(fā)展與逆轉(zhuǎn)并存，草地沙化防治的形勢依然嚴(yán)峻。
  • An attribute means clustering binary tree is presented in this paper . the binary tree is extends naturally and turns to be a supervised classification method . the orl database is used to evaluate the proposed method . the performance of the attribute means clustering binary tree used in face recognition is compared with the standard eigenface approach and improves their performance much in the experiment
    在無監(jiān)督的屬性聚類網(wǎng)絡(luò)的基礎(chǔ)上，提出了一種二叉樹分類方法。此二叉樹自然地在無監(jiān)督聚類的基礎(chǔ)上擴(kuò)展開來，成為一有監(jiān)督的分類方法。用orl人臉數(shù)據(jù)庫做了測試，同標(biāo)準(zhǔn)的特征臉eigenface方法相比，識別率得到了較大的提高。
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