本文選題：寬波段高光譜數(shù)據(jù) + 空間信息　； 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著遙感技術(shù)的不斷發(fā)展,人們獲取的高光譜遙感數(shù)據(jù)不但在空間分辨率以及光譜分辨率上取得了長(zhǎng)足的進(jìn)步,其波段范圍更是得到了極大的擴(kuò)展。寬波段遙感圖像覆蓋了可見(jiàn)/近紅外以及熱紅外波段,為科研工作者提供了更為完備與充分的遙感信息。由于可見(jiàn)/近紅外傳感器與熱紅外傳感器固有成像機(jī)理的不同,其所獲得的遙感圖像有著各自的特點(diǎn),反映出了地面目標(biāo)不同的物化特性:前者有著較高的空間分辨率,包含了目標(biāo)豐富的空間、結(jié)構(gòu)以及光譜信息;后者雖然空間分辨率較低,但是由于其熱輻射成像的特點(diǎn),紅外數(shù)據(jù)中包含了地物目標(biāo)溫度以及輻射信息。本文從寬波段遙感數(shù)據(jù)信息充分利用的角度出發(fā),提出了寬波段遙感圖像亞像元豐度以及溫度聯(lián)合制圖技術(shù)。具體研究?jī)?nèi)容如下:首先,對(duì)于可見(jiàn)光波段遙感圖像而言,由于其較高的空間分辨率,可以提供豐富的空間與結(jié)構(gòu)信息。傳統(tǒng)的端元提取算法只注重光譜信息來(lái)提取端元,而忽視了遙感圖像的空間特性。因此,未考慮空間信息的端元提取算法對(duì)噪聲以及異常信號(hào)較為敏感,導(dǎo)致端元提取精度的降低,因而其誤差會(huì)傳遞到亞像元制圖的過(guò)程中去。針對(duì)這個(gè)問(wèn)題,本文提出一種基于正交子空間及局部空間相關(guān)性的端元提取算法。該算法利用空間信息對(duì)提取的端元進(jìn)行判定、更新以及擴(kuò)展,從而保證了端元提取算法的有效性,為亞像元制圖提供準(zhǔn)確的豐度支撐。其次,熱紅外波段遙感圖像蘊(yùn)含地物豐富的溫度以及輻射率信息。本文通過(guò)對(duì)傳統(tǒng)溫度反演算法的學(xué)習(xí)與分析,針對(duì)混合像元各組分溫度反演難以實(shí)現(xiàn)的問(wèn)題,提出了一種亞像元級(jí)溫度反演算法。該算法利用豐度信息對(duì)純像元以及混合像元加以區(qū)分,并分別進(jìn)行處理。對(duì)于純像元使用傳統(tǒng)溫度輻射率分離算法,實(shí)現(xiàn)溫度與輻射率的估計(jì)。對(duì)于混合像元,該算法以大氣底層輻射線(xiàn)性混合模型為基礎(chǔ),對(duì)混合像元中不同地物組分的溫度分別進(jìn)行求解。該部分實(shí)現(xiàn)了熱紅外圖像亞像元級(jí)溫度反演,為亞像元制圖提供了地物溫度信息。最終,針對(duì)寬波段遙感圖像,充分結(jié)合可見(jiàn)光/近紅外遙感圖像與熱紅外遙感圖像各自的優(yōu)勢(shì),形成完整的豐度以及溫度聯(lián)合制圖算法。利用可見(jiàn)光波段遙感圖像豐度的空間信息對(duì)像元各個(gè)地物組分的豐度進(jìn)行估計(jì),并實(shí)現(xiàn)純像元與混合像元的定位。利用熱紅外波段所提供的溫度信息,結(jié)合所得到的豐度,對(duì)純像元以及混合像元的溫度、輻射率信息進(jìn)行反演。在亞像元制圖的過(guò)程中,利用像元吸引模型對(duì)亞像元空間位置進(jìn)行分配與調(diào)整,使制圖結(jié)果更加符合真實(shí)地物分布狀態(tài)。
[Abstract]:With the development of remote sensing technology, the hyperspectral remote sensing data not only has made great progress in spatial resolution and spectral resolution, but also has greatly expanded its band range.The wide band remote sensing images cover the visible / near infrared and thermal infrared bands, which provide more complete and sufficient remote sensing information for researchers.Because of the difference of the inherent imaging mechanism between the visible / near infrared sensor and the thermal infrared sensor, the remote sensing images obtained by them have their own characteristics, which reflect the different physical and chemical characteristics of the ground target. The former has higher spatial resolution.The latter contains rich spatial, structural and spectral information. Although the spatial resolution of the latter is low, the infrared data contain object temperature and radiation information due to its thermal radiation imaging characteristics.In this paper, from the point of view of making full use of remote sensing data information in wide band, the technique of sub-pixel abundance and temperature joint mapping of wide band remote sensing image is put forward.The specific research contents are as follows: first, for the visible light band remote sensing image, because of its high spatial resolution, it can provide rich spatial and structural information.The traditional End-element extraction algorithm only pays attention to spectral information, but neglects the spatial characteristics of remote sensing image.Therefore, the End-element extraction algorithm without spatial information is sensitive to noise and abnormal signals, which leads to the reduction of the precision of End-element extraction, so the error will be transferred to the process of sub-pixel mapping.In order to solve this problem, this paper presents an end-component extraction algorithm based on orthogonal subspace and local spatial correlation.The algorithm uses spatial information to judge, update and extend the extracted end elements, thus ensuring the effectiveness of the end element extraction algorithm and providing accurate abundance support for sub-pixel mapping.Secondly, thermal infrared remote sensing images contain rich information of temperature and emissivity.Based on the study and analysis of the traditional temperature inversion algorithm, a subpixel temperature inversion algorithm is proposed to solve the problem that the temperature inversion of each component of mixed pixel is difficult to realize.The algorithm uses abundance information to distinguish pure and mixed pixels, and processes them separately.Traditional temperature emissivity separation algorithm is used to estimate temperature and emissivity for pure pixels.For mixed pixels, the algorithm is based on the linear mixing model of atmospheric bottom radiation, and the temperature of different ground components in the mixed pixel is solved separately.In this part, subpixel temperature inversion of thermal infrared image is realized, which provides ground object temperature information for subpixel mapping.Finally, based on the advantages of visible / near infrared remote sensing images and thermal infrared remote sensing images, a complete joint mapping algorithm of abundance and temperature is formed for the wide band remote sensing images.The spatial information of the abundance of remote sensing image in the visible light band is used to estimate the abundance of each component of the pixel and to realize the localization of pure pixel and mixed pixel.The temperature and emissivity information of pure and mixed pixels are retrieved by using the temperature information provided by the thermal infrared band and the abundance obtained.In the process of sub-pixel mapping, the pixel attraction model is used to allocate and adjust the location of sub-pixel space, so that the mapping results are more consistent with the real distribution state of ground objects.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP751

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 劉瑩奇;;空間目標(biāo)紅外多波段溫度分布反演[J];紅外與激光工程;2013年03期

2 普晗曄;王斌;張立明;;基于Cayley-Menger行列式的高光譜遙感圖像端元提取方法[J];紅外與毫米波學(xué)報(bào);2012年03期

3 楊貴軍;柳欽火;劉強(qiáng);肖青;黃文江;;植被冠層3D輻射傳輸模型及熱輻射方向性模擬[J];紅外與毫米波學(xué)報(bào);2010年01期

4 覃志豪,高懋芳,秦曉敏,李文娟,徐斌;農(nóng)業(yè)旱災(zāi)監(jiān)測(cè)中的地表溫度遙感反演方法——以MODIS數(shù)據(jù)為例[J];自然災(zāi)害學(xué)報(bào);2005年04期

5 毛克彪,覃志豪,徐斌;針對(duì)ASTER數(shù)據(jù)的單窗算法[J];測(cè)繪學(xué)院學(xué)報(bào);2005年01期

6 范聞捷,徐希孺;論熱紅外多角度遙感數(shù)據(jù)的相關(guān)性及視角優(yōu)選配置[J];中國(guó)科學(xué)(D輯:地球科學(xué));2003年08期

7 覃志豪,Zhang Minghua,Arnon Karnieli,Pedro Berliner;用陸地衛(wèi)星TM6數(shù)據(jù)演算地表溫度的單窗算法[J];地理學(xué)報(bào);2001年04期

8 王錦地,李小文,孫曉敏,劉強(qiáng);用熱輻射方向性模型反演非同溫像元組分溫度[J];中國(guó)科學(xué)E輯:技術(shù)科學(xué);2000年S1期

9 李召良,M.P.Stoll ,張仁華,賈立,蘇中波;利用ATSR數(shù)據(jù)分解土壤和植被溫度的研究[J];中國(guó)科學(xué)E輯:技術(shù)科學(xué);2000年S1期

10 李小文,王錦地,胡寶新,A.H.Strahler;先驗(yàn)知識(shí)在遙感反演中的作用[J];中國(guó)科學(xué)(D輯:地球科學(xué));1998年01期

相關(guān)博士學(xué)位論文 前4條

1 楊華東;高光譜遙感影像光譜解混算法研究[D];大連海事大學(xué);2015年

2 夏威;高光譜遙感圖像的解混和波段選擇方法研究[D];復(fù)旦大學(xué);2013年

3 耿修瑞;高光譜遙感圖像目標(biāo)探測(cè)與分類(lèi)技術(shù)研究[D];中國(guó)科學(xué)院研究生院（遙感應(yīng)用研究所）;2005年

4 劉強(qiáng);地表組分溫度反演方法及遙感像元的尺度結(jié)構(gòu)[D];中國(guó)科學(xué)院研究生院（遙感應(yīng)用研究所）;2002年

相關(guān)碩士學(xué)位論文 前4條

1 周昕;高光譜遙感圖像混合像元分解算法研究[D];浙江大學(xué);2015年

2 劉益世;高光譜遙感混合像元端元提取研究及應(yīng)用[D];中南大學(xué);2013年

3 曾凡霞;高光譜遙感圖像端元提取算法改進(jìn)及性能優(yōu)化[D];成都理工大學(xué);2013年

4 鐘曉姣;高光譜數(shù)據(jù)混合像元分解與光譜匹配驗(yàn)證算法[D];南京理工大學(xué);2013年

，

本文編號(hào)：1732674