本文選題：LIDAR + 地物分類��； 參考：《中北大學(xué)》2015年碩士論文

【摘要】：激光掃描與測距技術(shù)（Light Detection and Ranging,LIDAR)可以快速、主動、自動獲取大范圍地物密集采樣點的三維信息，彌補了傳統(tǒng)攝影測量技術(shù)獲取地物信息單一的缺陷，自上世紀九十年代投入商業(yè)使用以來，已被廣泛地應(yīng)用于構(gòu)造數(shù)字地形模型（DigitalTerrain Model,DTM）與數(shù)字城市模型、突發(fā)自然災(zāi)害評估、道路與電力線勘探、生物量估計等領(lǐng)域。如何利用LIDAR系統(tǒng)提供的光譜、紋理、高程、強度等多源信息精確快速地獲取地物分布信息已成為當(dāng)前亟需解決的問題。 本文針對機載LIDAR數(shù)據(jù)地物分類領(lǐng)域內(nèi)的特征提取、特征重要性分析、分類器構(gòu)建、分類結(jié)果類別混淆出現(xiàn)原因及分類精度優(yōu)化方法進行了深入的研究和探討。論文主要研究內(nèi)容包括： 1.針對以往在對LIDAR數(shù)據(jù)進行地物分類時，特征選取缺乏依據(jù)，主要依賴個人經(jīng)驗與偏好，并由此導(dǎo)致分類精度未能達到最優(yōu)的問題，首先，，在進行特征提取時，對LIDAR點云與影像數(shù)據(jù)所提供的特征進行較為完備的提��；其次，在隨機森林分類算法框架下，利用袋外樣本的特征置換重要性測度評估特征對分類精度的影響程度；最后，選擇對分類精度影響較大特征代替原有的高維特征進行分類。 2.從算法原理上，對支持向量機、隨機森林、馬爾科夫隨機場、D-S證據(jù)理論等LIDAR數(shù)據(jù)地物分類領(lǐng)域內(nèi)常用的分類算法的優(yōu)缺點進行分析，通過實驗驗證構(gòu)建最適合LIDAR數(shù)據(jù)的分類方案。 3.針對分類器分類結(jié)果存在分類精度低、不符合真實地物特性、不符合人們觀測習(xí)慣等缺陷，研究分析分類結(jié)果中易出現(xiàn)混淆的類型、位置；利用目標(biāo)邊緣測度分析混淆出現(xiàn)的原因；根據(jù)各類混淆出現(xiàn)的原因，利用地物類間空間限制構(gòu)建具有針對性的混淆目標(biāo)類別修正算法，改善分類結(jié)果。
[Abstract]:The laser scanning and ranging technology, Light Detection and ranging list (LIDARL), can acquire 3D information of dense sampling points in a large area quickly, actively and automatically, which makes up for the single defect of traditional photogrammetry technology in obtaining ground object information. Since it was put into commercial use in 1990s, it has been widely used in the construction of digital terrain models (DTM) and digital city models, sudden natural disaster assessment, road and power line exploration, biomass estimation and so on. How to use the spectrum, texture, elevation, intensity and other multi-source information provided by the LIDAR system to accurately and quickly obtain the distribution information of ground objects has become a problem that needs to be solved. This paper aims at feature extraction in the field of airborne LIDAR data in the field of ground object classification. The analysis of feature importance, the construction of classifier, the cause of classification confusion and the optimization method of classification accuracy are discussed. The main contents of this paper are as follows: 1. In order to solve the problem that the feature selection of LIDAR data is lack of basis, it mainly depends on personal experience and preference, and thus leads to the classification accuracy can not reach the optimal. Firstly, in feature extraction, The features provided by LIDAR point cloud and image data are extracted completely. Secondly, under the framework of stochastic forest classification algorithm, the importance measure of feature replacement of out-of-bag samples is used to evaluate the degree of influence of features on classification accuracy. Select the feature which has a great influence on the classification accuracy instead of the original high dimensional feature to classify. 2. Based on the principle of the algorithm, the advantages and disadvantages of the common classification algorithms in the field of LIDAR data object classification, such as support vector machine, random forest, Markov random field D-S evidence theory, are analyzed. Through the experimental verification to construct the most suitable classification scheme for LIDAR data. 3. The classification results of the classifier have some defects, such as low classification accuracy, not accord with the real features of ground objects, and do not accord with the observation habits of people, so the types and positions that are easily confused in the classification results are studied and analyzed. The reason of confusion is analyzed by using target edge measure, and according to the cause of all kinds of confusion, a modified algorithm is constructed to improve the classification result by using space restriction between ground objects.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN249

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