unsupervised classification 中文意思是什麼
unsupervised classification
解釋
非監督分類- unsupervised : 無監督的
- classification : n 1 選別；分等，分級；分選。2 【動、植】分類（法）。 〈分類級別為： phylum 【動物；動物學】及 div...
-
In this paper, we made an investigation into texture feature extraction and classification based on statistic method and its application in multi - spectral image classification. the research works of this paper have been done as follows : firstly, in order to overcome the weakness of gray level co - occurrence matrix ( glcm ), a new unsupervised texture segment algorithm, based on multi - resolution model, is presented in this thesis
本文主要研究了基於紋理統計特性的特徵提取與分割方法，並將其用於實際的多光譜圖像分類，具體工作如下:第一，針對傳統灰度共現陣方法中特徵提取的尺度單一問題，本文提出了一種多分辨無監督紋理分割演算法。 -
The vegetation type was classified in the east transect by unsupervised classification using the data ( ikmx 1km ) from noaa meteorology satellite from 1995 - 1996, and the article analyzed the change of ndvi of all kinds of vegetation types, in the same time analyzed the forest dynamics in typical ecotone ( warm temperate zone to semitropical in qinling woods ) using the higher spatial resolution tm data
本論文利用1995 - 1996年noaa氣象衛星的ndvi （ 1km 1km ）數據，採用無監督分類方法對中國東部樣帶南方部分進行植被類型的劃分，分析各植被類型的ndvi變化情況；並利用較高精度的tm數據分析典型交錯區域（暖溫帶到亞熱帶的秦嶺林區）的森林動態變化情況。 -
A series methods of data combination analyzing are selected to form the operating method system for crop discrimination. combining gis, gps, and other data from field work with rs data can determine interpretation features and set off working regions, combining rs data can enhance spatial features in order to do unsupervised classification efficiently, union of gis data enable us to join maps and extract features, to analyze crop structure, crop calendar, cultivating system
本項研究篩選出了構成運行化作物遙感識別技術體系的一系列數據復合分析方法，包括gps 、 gis數據以及其它田間作業信息與rs數據之間的復合，確立解譯標志和劃分作業區； rs數據之間的復合，進行圖像增強，改善非監督分類效果； gis數據之間的復合，分析作業區作物結構、物候和耕作制度現狀，地圖拼接、特徵提取等。 -
3 ) semantic classification model based som network we use the classification model to combines attributes within a database. this is done using an unsupervised learning algorithm. the output is used as training data for the next stage
3 ）基於som網路的語義分類模型設計建立som網路模型，將元數據特徵向量進行分類，形成bp網路的目標向量，用於匹配規則的提取。 -
The details are as follows : firstly, in order to extract audio / visual middle level features, we adopt supervised audio classification and unsupervised scene clustering to satisfy the requirement of generality. the audio is robust and supervised audio classification is general in the same type sports video
具體來說本文的工作可以分為以下幾點:首先，在體育視頻中層特徵提取上，本文採用了有監督的音頻分類和無監督的場景聚類以適應通用性要求。 -
2 ) to increase the difference, the non - linear transform function is used. the each pixel is computed by the average of a window ' s energy, which is gabor wavelets energy and the input feature vector of unsupervised classification
2 ）對濾波后圖像進行非線性處理，以加大不同類之間特徵的差異，給出了計算圖像gabor小波能量特徵的計算方法，該能量特徵作為無監督分類器的輸入向量。 -
Decision theory, statistical classification, maximum likelihood and bayesian estimation, non - parametric methods, unsupervised learning and clustering
決策理論，統計分類，最大似然和貝葉斯估計，非參數方法，非監督的學習與聚類。 -
The main factors of probabilistic neural network including the hidden neuron size, hidden central vector and the smoothing parameter, to influence the pnn classification, are analyzed ; the xor problem is implemented by using pnn. a new supervised learning algorithm for the pnn is developed : the learning vector quantization is employed to group training samples and the genetic algorithms ( ga ’ s ) is used for training the network ’ s smoothing parameters and hidden central vector for determining hidden neurons. simulations results show that, the advantage of our method in the classification accuracy is over other unsupervised learning algorithms for pnn
本文主要分析了pnn隱層神經元個數，隱中心矢量，平滑參數等要素對網路分類效果的影響，並用pnn實現了異或邏輯問題;提出了一種新的pnn有監督學習演算法:用學習矢量量化對各類訓練樣本進行聚類，對平滑參數和距離各類模式中心最近的聚類點構造區域，並採用遺傳演算法在構造的區域內訓練網路，實驗表明:該演算法在分類效果上優于其它pnn學習演算法 -
Besides, we study an unsupervised classification method based on polar decomposition
我們接著研究了基於極化分解的非監督分類方法。 -
In this dissertation, we study an unsupervised classification method based on fuzzy set theory
我們首先研究了基於模糊集理論的非監督分類方法。 -
This method is a combination of the usage of polarimetric information of polsar data and the unsupervised classification method based on fuzzy set theory
該方法是原始sar數據極化信息的利用和基於模糊集理論的非監督分類方法的結合。 -
In this dissertation, we study some unsupervised classification methods through three aspects, such as preprocessing, the selection of classifier and feature extracting
為了得到更快速、更準確的分類結果，本文圍繞預處理、分類器的選擇和特徵提取等問題研究了一些非監督分類方法。 -
The experimental results show that these two classification methods of multi - sources information fusion can result in better accuracy than that of conventional unsupervised classification method
實驗結果表明基於bdset和fdset融合的分類方法比傳統的非監督分類方法具有更好的分類效果，有效地提高了分類的精度。 -
In the end, multispectral image fusion algorithms based on principal component analysis and clustering algorithms are introduced. multispectral image fusion based on unsupervised classification is realized
最後，還介紹了基於主成分分析（ pca ）的多光譜圖象融合方法以及非監督分類的方法，並實現了基於非監督分類的多光譜圖象融合演算法。 -
Based on unsupervised classification, improve the original kononen neural network ( knn ) by modifying learning rate and kernel function. and developed two fusion rules based on the magnitude of providing recognition information of each information sources
1 ）改進基本的kohonen神經網路（ knn ）學習率和核函數，並針對每個信息源對最終識別貢獻的大小，提出加權與不加權兩種融合規則。 -
In this paper, theories about wavelet analysis and image fusion are reviewed and remote - sensing image fusion is realized using wavelet analysis and unsupervised classification. first, wavelet transform theories are systematically reviewed and summarized from the point of view of signal and information processing
本文首先從小波理論在信號與信息處理學科領域中的應用角度對小波理論進行了系統的總結和介紹，分別利用mallat演算法和trous演算法實現了圖象的正交小波變換和冗餘小波變換。 -
However, as data mining has penetrated to more application domains, feature selection for unsupervised learning becomes concerned increasingly. therefore, without any information of classification from samples, method of feature selection cannot result in satisfying effect
由於無指導學習沒有關于樣本類別的重要信息，在面對大量屬性的數據集所進行屬性選擇的效果不如有指導學習環境下的結果。 -
It is discovered that the correlation between msavi ( modified soil adjusted vegetation index ) and vegetation coverage is higher than that between ndvi ( normalized differential vegetation index ) and grassland coverage when the land cover is low. so msavi can be used as the main index of grassland desertification degree. in order to evaluate the precision of the result, different methods such as supervised classification, unsupervised classification and visual interpretation on image are tried respectively
本文將其作為草地沙化的分級指標，直接通過計算分級，建立msavi等級與草地沙化程度的對應關系，確定草地沙化的程度，然後將msavl分類結果分別與目視解譯、監督、非監督分類的結果進行了精度比較，其精度高於監督與非監督分類，與典型區現場繪制和目視解譯結果的誤差為5 . 9 ，能滿足草地沙化監測實際應用要求。 -
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
採用上述研究方法，利用1987年8月11日和2001年8月9日兩時相的tm數據，對奈曼旗中部十個鄉鎮的草地沙化動態進行了分析，結果表明研究區草地嚴重沙化的總體趨勢得到控制，並且開始逆轉，生態環境建設已經產生了成效，但還存在著局部的惡化趨勢，草地沙化發展與逆轉並存，草地沙化防治的形勢依然嚴峻。 -
And then the paper describes em - mrf iterative algorithm and its realization for the parameter estimation in unsupervised image classiifcation process. the em - mrf - based image classification strategy is introduced into multisensor feature - level image fusion, distributed and centric based fusion methods are proposed. finally, simulated results through sythetic and real remotely sensed image illustrate the effectiveness and advantage of the proposed methods
針對遙感圖像非監督分類中的參數估計問題，重點討論了em - mrf迭代演算法的原理和實現，並將em - mrf迭代演算法引入到多源遙感圖像融合的過程中，提出了兩種分別基於集中式融合模型和分散式融合模型的圖像融合方法。
分享友人