【摘要】：隨著高光譜大氣紅外遙感探測技術(shù)的發(fā)展,對大氣的探測越來越精細,探測周期越來越短,從而探測信息的數(shù)據(jù)量也隨之越來越大,無論是在星上還是在星下,對于探測信息的存儲和傳輸是在數(shù)據(jù)應(yīng)用過程當(dāng)中必然面對的問題。因此,為達到快速傳輸高光譜大氣紅外遙感圖像數(shù)據(jù),并使其占用的存儲空間小,同時保證對數(shù)據(jù)同化和反演的準確度的目的,對其進行輻射抽稀(radiance thinning)是非常必要的,輻射抽稀分為兩種方面,即對數(shù)據(jù)的無損壓縮和光譜通道選擇。本文主要針對輻射抽稀的兩種情況展開研究。首先對大氣探測及其遙感數(shù)據(jù)傳輸和存儲進行分析,并以AIRS探測儀所探測的典型高光譜大氣紅外遙感圖像為典型實驗數(shù)據(jù),對其空間相關(guān)性和光譜相關(guān)性的特性進行分析,定性說明對其進行無損壓縮研究和光譜通道選擇的可行性和必要性。其次,考慮高光譜大氣紅外遙感圖像的光譜相關(guān)性極大,為實現(xiàn)有效的壓縮效果,本文采用ICA變換去除譜間冗余,使圖像在變換域的ICs成分實現(xiàn)相互獨立;之后對所得ICs成分及變換系數(shù)進行量化與反量化,保留量化殘差,對量化后數(shù)據(jù)以及量化殘差進行預(yù)測處理,以減小待編碼數(shù)據(jù)量;在編碼部分本文選取區(qū)間編碼并利用隨機學(xué)習(xí)弱估計方法(SLWE)改進其中的概率估計模型,以提高編碼效果。在編碼之前,對待編碼數(shù)據(jù)進行正值化處理,使其更適合區(qū)間編碼過程。最后對典型的AIRS實驗數(shù)據(jù)進行壓縮,壓縮比可達3.35以上,并與部分現(xiàn)有的經(jīng)典壓縮方法對比,本文所研究的壓縮方法在壓縮比上具有一定的優(yōu)勢。最后,根據(jù)AIRS探測資料中典型的亮溫資料以及溫、濕度反演廓線,通過輻射傳輸模式(RTTOV)得到其溫、濕度Jacobi矩陣,為了從大量的AIRS探測資料中抽取出與應(yīng)用相關(guān)的通道信息,實現(xiàn)減小數(shù)據(jù)量并適合應(yīng)用的目的,分別對溫、濕度Jacobi矩陣進行基于PC-AIC的通道選擇,選出對溫、濕度影響較大的波段通道。并根據(jù)所選通道對AIRS探測亮溫資料進行溫、濕度反演應(yīng)用,將其反演結(jié)果與衛(wèi)星資料數(shù)值天氣預(yù)報應(yīng)用研究組(NWPSAF)所給通道的反演結(jié)果對比,本文所研究方法對溫、濕度反演所得廓線的誤差更小。進一步給出基于信息容量迭代的經(jīng)典通道選擇方法,并與本文的PC-AIC算法對比,得出PC-AIC算法所選通道組合反演效果更好的結(jié)論,說明其在具體應(yīng)用上,保證數(shù)據(jù)量減小的同時,可進行有效的通道選擇。
[Abstract]:With the development of infrared remote sensing detection technology for hyperspectral atmosphere, the atmospheric detection becomes more and more precise, and the detection period is shorter and shorter, so the amount of data of the detecting information becomes more and more large, whether on or under the star. The storage and transmission of detection information is an inevitable problem in the process of data application. Therefore, in order to transmit hyperspectral infrared remote sensing image data quickly, and to make the storage space small, and to ensure the accuracy of data assimilation and inversion, it is very necessary to carry out radiation-pumped (radiance thinning). Radiation pumping can be divided into two aspects: lossless compression of data and spectral channel selection. In this paper, two kinds of radiation pumping conditions are studied. Firstly, the atmospheric detection and its remote sensing data transmission and storage are analyzed, and the spatial and spectral correlation characteristics of the typical hyperspectral infrared remote sensing images detected by the AIRS detector are analyzed. The feasibility and necessity of lossless compression study and spectral channel selection are explained qualitatively. Secondly, considering the spectral correlation of hyperspectral infrared remote sensing image, in order to achieve an effective compression effect, this paper uses ICA transform to remove the redundancy between spectra, so that the ICs components of the image in the transform domain can be independent of each other. Then the quantization and inverse quantization of the ICs components and transformation coefficients are carried out, the quantization residuals are retained, and the quantized data and quantized residuals are predicted and processed to reduce the amount of data to be encoded. In the coding part, interval coding is selected and the probabilistic estimation model is improved by using the random learning weak estimation method (SLWE) to improve the coding effect. Before coding, positive processing of coded data is carried out to make it more suitable for interval coding process. Finally, the compression ratio of the typical AIRS experimental data is over 3.35, and compared with some classical compression methods, the compression method studied in this paper has some advantages in compression ratio. Finally, according to the typical bright temperature data and the inversion profile of temperature and humidity in AIRS detection data, the temperature and humidity Jacobi matrix is obtained by radiative transfer mode (RTTOV). In order to extract the channel information related to application from a large amount of AIRS detection data, For the purpose of reducing the amount of data and being suitable for application, the Jacobi matrix of temperature and humidity is selected based on PC-AIC channel, and the band channel which has a great influence on temperature and humidity is selected. According to the selected channel, the inversion of temperature and humidity of AIRS detection bright temperature data is carried out. The inversion results are compared with the inversion results of the channel given by (NWPSAF), a research group of satellite data numerical weather forecast. The error of the profile obtained by humidity inversion is smaller. Furthermore, the classical channel selection method based on information capacity iteration is given, and compared with the PC-AIC algorithm in this paper, it is concluded that the combination of channels selected by the PC-AIC algorithm has better results. At the same time, effective channel selection can be carried out while the amount of data is reduced.
【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】：2017
【分類號】：TP751

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本文編號：2415826