本文選題：多譜 + 協(xié)同解譯　； 參考：《哈爾濱工業(yè)大學(xué)》2014年碩士論文

【摘要】：隨著遙感對地觀測技術(shù)的發(fā)展，多譜遙感數(shù)據(jù)越來越多地應(yīng)用在軍事目標(biāo)檢測、城市綠化監(jiān)測、農(nóng)業(yè)遙感等領(lǐng)域，如擁有近乎連續(xù)的光譜信息的高光譜影像、可提供更精細的形狀紋理等空間信息的高空間分辨率遙感影像、可反映地物熱輻射信息的紅外數(shù)據(jù)、可獲得地物數(shù)字高程模型的激光雷達數(shù)據(jù)等。然而對這些多平臺數(shù)據(jù)的應(yīng)用都面臨著數(shù)據(jù)量更加巨大、信息更加復(fù)雜的問題，因此無論是針對高空間分辨率還是高光譜分辨率的遙感影像，都迫切地需要我們對其信息提取以及分類解譯方法進行不斷深入的研究。本論文主要針對多譜遙感影像數(shù)據(jù)中的高光譜數(shù)據(jù)及高空間分辨率遙感影像進行協(xié)同處理，利用光譜與空間的聯(lián)合信息實現(xiàn)多譜遙感影像在特征層的協(xié)同解譯。 首先，針對高光譜影像，分析了其數(shù)據(jù)特點，利用高光譜影像具有的圖譜合一、非線性和稀疏性的特點，研究了受限玻爾茲曼機算法原理，介紹了深層網(wǎng)絡(luò)模型框架下的半監(jiān)督的貪婪逐層學(xué)習(xí)方法，同時嘗試了經(jīng)典的主成分分析及非負矩陣分解的特征提取方法，對比了支持向量機與深度置信網(wǎng)絡(luò)模型對高光譜影像地物分類解譯的解譯精度及訓(xùn)練、測試時間，為后續(xù)協(xié)同解譯奠定理論研究基礎(chǔ)。實驗驗證了深度置信網(wǎng)絡(luò)對于具有復(fù)雜光譜信息的類別具有出色的分類效果。 其次，為了解決隨著高分辨率遙感影像分辨率越高，紋理信息越復(fù)雜，分類精度隨之下降的問題，計算不同地物的最優(yōu)分割尺度，采用分形網(wǎng)絡(luò)演化算法對影像進行多尺度分割，而后分別采用基于樣本對象和基于多層次體系結(jié)構(gòu)的面向?qū)ο蟮男畔⑻崛》椒▽Ω黝悇e地物進行信息提取分類，與基于像素的處理方法對比，驗證了面向?qū)ο筇幚淼挠行浴?最后，利用高分辨率遙感影像的信息提取結(jié)果，合并相鄰的同類地物對象，對合并后的對象提取空間特征，，與高光譜影像的光譜特征進行特征層融合，將空譜聯(lián)合特征輸入深度置信網(wǎng)絡(luò)模型，討論了不同學(xué)習(xí)率、隱層單元數(shù)、網(wǎng)絡(luò)層數(shù)、訓(xùn)練樣本比例等網(wǎng)絡(luò)參數(shù)對模型的影響，利用兩種數(shù)據(jù)源各自的優(yōu)勢，提高了單一數(shù)據(jù)源的解譯精度，實現(xiàn)多譜遙感影像特征層的高精度協(xié)同解譯。
[Abstract]:With the development of remote sensing Earth observation technology, multispectral remote sensing data are more and more used in military target detection, urban greening monitoring, agricultural remote sensing and other fields, such as hyperspectral images with almost continuous spectral information. High spatial resolution remote sensing images which can provide more precise spatial information such as shape and texture can reflect the infrared data of the thermal radiation information of the ground objects and obtain the lidar data of the digital elevation model of the ground objects and so on. However, the applications of these multi-platform data are faced with the problems of more huge amount of data and more complex information, so it is not only for the remote sensing image with high spatial resolution or hyperspectral resolution, but also for the remote sensing image with high spatial resolution or hyperspectral resolution. It is urgent for us to study the methods of information extraction and classification. In this paper, the hyperspectral data and the spatial resolution remote sensing image of multi-spectral remote sensing image are processed in cooperation, and the cooperative interpretation of multi-spectral remote sensing image in the feature layer is realized by using the joint information of spectrum and space. Firstly, the characteristics of hyperspectral images are analyzed, and the algorithm principle of constrained Boltzmann machine is studied, which is based on the characteristics of unifying, nonlinear and sparsity of hyperspectral images. In this paper, the semi-supervised greedy learning method based on deep network model is introduced, and the classical principal component analysis (PCA) and non-negative matrix decomposition feature extraction method are tried. The accuracy, training and test time of classification interpretation of hyperspectral images based on support vector machine and depth confidence network model are compared, which lays a theoretical foundation for subsequent cooperative interpretation. The experimental results show that the depth confidence network has a good classification effect for the categories with complex spectral information. Secondly, in order to solve the problem that the higher the resolution of high resolution remote sensing image is, the more complex the texture information is and the lower the classification accuracy is, the optimal segmentation scale of different ground objects is calculated. The fractal network evolution algorithm is used to segment the image at multi-scale, and then the object oriented information extraction method based on the sample object and the multi-level architecture is used to extract and classify the information of each kind of ground objects. Compared with the pixel-based processing method, the effectiveness of object-oriented processing is verified. Finally, using the information extraction result of high-resolution remote sensing image, merging the adjacent objects of similar ground objects, extracting the spatial features of the combined objects, and fusion with the spectral features of hyperspectral images. The spatial spectrum joint feature is input into the depth confidence network model. The influence of the network parameters such as different learning rate, hidden layer number, network layer number and training sample ratio on the model is discussed. The advantages of the two kinds of data sources are utilized. The interpretation accuracy of single data source is improved, and the cooperative interpretation of multi-spectral remote sensing image feature layer is realized.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP751

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