本文選題：衛(wèi)星云圖 + 云分類��； 參考：《南昌航空大學(xué)》2014年碩士論文

【摘要】：氣象衛(wèi)星能夠?qū)Φ乇砑霸茖舆B續(xù)地進(jìn)行大范圍觀測(cè)，由此得到的衛(wèi)星云圖蘊(yùn)含著豐富的氣象信息。這些信息為天氣預(yù)報(bào)尤其是降雨分析提供了可靠依據(jù)。可是，隨著氣象衛(wèi)星云圖數(shù)據(jù)源數(shù)量上的爆炸式增長(zhǎng)和內(nèi)容上的極大豐富，相應(yīng)的處理、分析工具的研發(fā)和應(yīng)用卻嚴(yán)重滯后。傳統(tǒng)分類算法用于遙感圖像云分類時(shí)，容易造成處理規(guī)模過大、分析過程復(fù)雜以及陷入局部極小值等問題，而且在分類速度和分類精度遠(yuǎn)遠(yuǎn)無法滿足需求。因此，對(duì)衛(wèi)星云圖進(jìn)行準(zhǔn)確、快速的自動(dòng)分類一直是遙感領(lǐng)域眾多學(xué)者和科研人員的研究熱點(diǎn)。 著眼于此，本文將一種新型的單隱層前饋神經(jīng)網(wǎng)絡(luò)算法——極限學(xué)習(xí)機(jī)（Extreme Learning Machine，ELM）應(yīng)用于遙感衛(wèi)星云圖分類中的分類器構(gòu)建。另外，本文還采用了支持向量機(jī)算法進(jìn)行云分類，與極限學(xué)習(xí)機(jī)分類效果進(jìn)行對(duì)比分析。本文主要內(nèi)容和研究成果概述如下： （1）首先介紹了論文的選題背景和意義，然后詳細(xì)介紹了云分類的研究歷程和現(xiàn)狀，并對(duì)云的分類方法進(jìn)行了深入的分析。 （2）介紹了氣象衛(wèi)星及衛(wèi)星云圖的概念，云的種類及其在衛(wèi)星云圖上的表現(xiàn)特性，詳細(xì)講述了本文所使用的樣本文件格式及讀取方法，分析了遙感云圖的特性和分類理論。 （3）詳細(xì)研究了極限學(xué)習(xí)機(jī)的學(xué)習(xí)過程，說明了該算法在學(xué)習(xí)性能上的優(yōu)勢(shì)和特性，并創(chuàng)新性地將極限學(xué)習(xí)機(jī)算法應(yīng)用于遙感衛(wèi)星云圖分類�；谏鲜鰧�(shí)驗(yàn)的結(jié)果，詳細(xì)分析了ELM算法中隱藏層節(jié)點(diǎn)數(shù)對(duì)分類結(jié)果，包括分類精度和分類時(shí)間的影響，研究了其變化的規(guī)律。 （4）為了進(jìn)行對(duì)比，本文利用支持向量機(jī)算法設(shè)計(jì)分類器，并對(duì)相同的分類樣本進(jìn)行測(cè)試，分析兩種算法的優(yōu)劣勢(shì)。 最后通過兩組結(jié)果的對(duì)比可以得出，，用ELM方法進(jìn)行云分類是有效且分類速度上有明顯優(yōu)勢(shì)，但是分類精度低于SVM。
[Abstract]:The meteorological satellite can continuously observe the surface and clouds in a wide range, and the resulting satellite cloud images contain abundant meteorological information.This information provides a reliable basis for weather forecast, especially for rainfall analysis.However, with the explosive growth in the number of data sources and the abundance of the contents, the research and development and application of analytical tools are lagging behind.When the traditional classification algorithm is used in cloud classification of remote sensing images, it is easy to cause problems such as too large processing scale, complex analysis process and falling into local minima. Moreover, the speed and accuracy of classification can not meet the requirements.Therefore, accurate and fast automatic classification of satellite cloud images has been a hot spot of many researchers and researchers in remote sensing field.In this paper, a new single-hidden layer feedforward neural network algorithm, extreme Learning Machine (ELM), is used to construct a classifier for remote sensing satellite cloud image classification.In addition, the support vector machine (SVM) algorithm is used for cloud classification.The main contents and research results of this paper are summarized as follows:Firstly, the background and significance of this paper are introduced, then the research history and present situation of cloud classification are introduced in detail, and the method of cloud classification is deeply analyzed.This paper introduces the concept of meteorological satellite and satellite cloud image, the category of cloud and its characteristics on satellite cloud image, describes in detail the format of sample file and the reading method used in this paper, and analyzes the characteristics and classification theory of remote sensing cloud image.(3) the learning process of LLM is studied in detail, and the advantages and characteristics of the algorithm in learning performance are explained, and the LLM algorithm is innovatively applied to the classification of remote sensing satellite cloud images.Based on the above experimental results, the effects of the number of hidden layer nodes in ELM algorithm on classification results, including classification accuracy and classification time, are analyzed in detail.In order to compare, the support vector machine (SVM) algorithm is used to design the classifier, and the same classification samples are tested to analyze the advantages and disadvantages of the two algorithms.Finally, through the comparison of the two groups of results, it can be concluded that the cloud classification using ELM method is effective and has obvious advantages in classification speed, but the classification accuracy is lower than that of SVM.
【學(xué)位授予單位】：南昌航空大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP751

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