× 【作者】 石婷婷

【导师】 童庆禧；张立福

【 学位年度 】 2015

【论文级别】 硕士

【关键词】 高光谱遥感，目标探测，空间分辨率，光谱分辨率，合适尺度

【Key words】 hyperspectral remote sensing, target detection, spatial resolution, spectral resolution, suitable scale

【中文摘要】

高光谱遥感可以细致识别地物特征，在目标探测领域有着不可替代的优势。目前，高空间、高光谱遥感的飞速发展为目标探测提供了更多的数据源。目标探测精度和目标的尺寸大小、空间分辨率、光谱分辨率、波段设置、目标背景光谱特性、探测算法都有着密切的关系。一般说来，对于特定目标和背景，使用同种目标探测算法，影像的的空间和光谱分辨率越高，探测效果越好。但由于传感器的限制，这两个指标不可能也没必要追求最高水平。对于特定目标和背景，选择合适的空间和光谱分辨率的数据，即合适的空间和光谱尺度，就可以达到较好的探测效果。
针对如何选择合适空间和光谱尺度数据进行目标探测的问题，本文研究高光谱目标探测经典算法，得到不同目标探测算法的适应性，选择合适的算法，利用地面高光谱成像仪FISS（Field Imaging Spectrometer System）数据和航空高光谱AVIRIS数据，研究目标和背景光谱相似情况下，单一均匀草地上人工伪装目标和较复杂建筑用地上飞机的探测问题，通过空间和光谱降采样的方法得到不同空间和光谱分辨率数据，进而得到探测精度与空间和光谱分辨率之间的关系，定量评价目标背景探测中合适的空间和光谱尺度。本文得到结论如下：
1. RXD算法适用于目标少且突出的情况，异常探测算法较其它有先验知识的算法来说虚警率较高；CEM算法适用于目标小的情况，探测精度较高；ACE算法适用于背景均匀的情况；OSP算法对背景的输入较为敏感，在复杂背景下探测结果可能会受影响；
2. 当图像空间分辨率下降时，待探测目标从纯像元变为亚像元，探测精度逐步呈缓慢下降、急速下降、缓慢下降的趋势，目标尺寸和空间分辨率之比与探测精度正相关。将探测精度急速下降之前的空间尺度作为探测的合适空间尺度，则在本文地面实验中，探测绿色布目标所需的最佳空间分辨率约为目标尺寸的2倍以内。在利用AVIRIS数据探测飞机中，所需的最佳空间分辨率同样约为目标尺寸的2倍以内；
3. 目标光谱尺度的选择与目标、背景之间的光谱差异（明显的反射峰距离）相关。在地面试验中，目标绿色布和背景草地反射峰相距20 nm，在光谱分辨率低于40 nm时，该光谱特征消失，探测精度下降；利用航空AVIRIS数据探测复杂建筑用地背景下飞机目标，飞机与建筑地类飞机光谱的反射峰相距200 nm，在光谱分辨率10-60 nm范围内，探测精度基本不变；
4. 在合适光谱尺度范围内，探测精度受空间分辨率的影响较大。在地面探测绿色布的实验中，当空间分辨率一定，光谱分辨率优于40 nm时，探测精度变化不大，但是当空间分辨率下降，目标从纯像元到亚像元状态时，精度出现明显下降。在利用AVIRIS数据进行飞机探测时得到了同样的结果；
5. 选取目标、背景光谱特征差异较大的波段可提高探测的有效性，在舍弃目标背景相似波段后，探测精度上升。在地面探测绿色布实验中，参照多光谱传感器的设置，舍去目标和背景相似的430 nm处的波段，可以提高探测精度，最佳探测的波段组合为450-510 nm、510-580 nm、585-625 nm、630-690 nm、705-745 nm。在利用AVIRIS数据探测飞机的实验中，同样参照多光谱传感器的设置，使用基于信息量准则选择最佳波段组合，得出6个波段450-510 nm、510-580 nm、630-690 nm、770-895 nm、2235-2285 nm、2305-2365 nm，在纯像元和亚像元探测中具有最佳的探测精度。另外，由于飞机和建筑地类飞机光谱的多变性，去掉1200-1700 nm光谱变化较大波段提高了探测精度。
本研究首创性地使用地面成像光谱仪和航空数据选择特定目标背景进行目标探测中空间及光谱尺度定量化研究，方法及结论对类似目标背景特征下数据源的选择和其它目标背景组合目标探测研究具有重要意义。


【Abstract】

Hyperspectral target detection has irreplaceable advantages which can identify meticulous spectral characteristic difference between the target and background. At present, the rapid development of high spaial and hyperspectral remote sensing provides more data for target detection. Target detection accuracy is closely related to target size、spatial resolution、spectral resolution、band settings、spectral characteristics of the target and background, detection algorithms. Generally, with regard to specifically target and background, the finer the resolution is, the higher the accuracy will be. Yet the spatial and spectral resolution can barely meet the need simultaneously, for technical obstacles. A tradeoff between the two factors is needed for effective target detection and the choice of appropriate remotely sensed data for target detection has always been concentrated nowadays.
To address this concern, this paper researched on target detection mechanism, evaluated the suitability of different detection algorithms. On this basis, this paper using ground hyperspectral sensor FISS (Field Imaging Spectrometer System) data and aerial hyperspectral AVIRIS data，studied on the situations where the target was small and had a similar spectrum to the homogenously distributed or complex backgrounds. Through the down sampling processing of the high spatial hyperspectral images, the paper analyzed the relationship between the spatial and spectral resolution and the detection accuracy. And then it proposed the optimal spatial and spectral scale for target detection. This study focused on the quantification of the scale impact of spectral and spatial resolution on target detection precision. Results revealed that：
(i) RXD algorithm is applicable to small and outstanding targets, anomaly detection algorithm has a higher false alarm rate comparing to other algorithms which have prior knowledges; CEM can achieve high detection accuracy in the case of small targets; ACE algorithm can be used to homogenously distributed background which can be represented by multivariate normal distribution；OSP algorithm is sensitive to the input spectrals of the background, the results may be affected in the complex background.
(ii) With the decline of spatial resolution, the pure target to be detected turned into sub-pixel, the detection accuracy experienced three stages of descending rates: gently-dramatically-gently. The radio of the target size and the spatial resolution has positive correlation to detection accuracy. The corresponding spatial resolution before the second stage is the effective scale for detection. In the ground experiment, the required spatial resolution for camouflaged detection was about within twice the size of the target. Using AVIRIS data, the required spatial resolution for aircraft detection was also about twice the size of the target.
(iii) Suitable spectral scale is related to the spectral difference between the target and the background (significant reflection peaks distance). In the ground experiment, the reflection peaks differences, associated with the target and the background, were 20nm apart. When the spectral resolution was coarser than 40 nm, the differences of reflection peaks disappeared and the detection accuracy decreased. Using AVIRIS data, since the reflection peaks differences were 200 nm apart, the detection accuracy was essentially the same when the spectral resolution ranging from 10-60 nm.
(iv) In the appropriate range of spectral scales, the spatial resolution plays an more important role in detection accuracy. In the ground experiment, the detection accuracy changed little when the spatial resolution remained the same and the spectral resolution varied from 10 to 40 nm. However, when the the spatial resolution decreased, the accuracy had a clearly decline. The aircraft detection had the same result.
(v) To select the quite different characteristic bands or abandon the similar bands from the target and background spectral can improve the detection accuracy. Refer to the multispectral sensors, the accuracy increased when we got rid of similar bands(430 nm) in the ground experiment, and the optimal combination bands were 450-510 nm, 510-580 nm, 585-625 nm, 630-690 nm, 705-745 nm. In the aircraft detection experiment, the optimal combination bands were 450-510 nm, 510-580 nm, 630-690 nm, 770-895 nm, 2235-2285 nm, 2305-2365 nm, according to the OIF(Optimum Index Factor). In addition, removing 1200-1700 nm bands can improve the detection accuracy due to the variability of the target.
It was concluded that the quantitative analysis method and results of spatial and spectral scales for target detection would be of great significance for both data source selection and studing on other target-background combinations under similar conditions.

× 【论文级别】 博士

【关键词】 理化参量，定量反演模型，不确定性问题，光谱尺度，混合光谱

【Key words】 Physicochemical parameters, Models for quantitative inversion, The issue of uncertainty, Spectral scale, Mixture spectrum

【中文摘要】

植被生理生化参量是碳循环、养分循环和降雨截留等生态过程模型中重要的输入参数。高光谱遥感技术对植被生理生化参数反演具有独特的优势，已经引起广泛关注。但目前由于卫星高光谱传感器的局限，很难同时获取高空间、高时间和高光谱分辨率的遥感影像，从而导致植被理化参量的高光谱定量反演在时间、空间和光谱尺度上存在一定的不确定性。
针对这一问题，本文以冬小麦不同生育期叶绿素含量和叶面积指数为例，基于模型模拟和实测光谱数据，采用物理模型和经验/半经验统计分析模型，分析不同光谱尺度及光谱混合等对理化参量反演所产生的不确定问题，并尝试提出一种削弱不同传感器之间光谱尺度不确定性的算法，最后在时间尺度（文中为小麦的不同生育期）上对光谱尺度不确定性做进一步研究。基于以上思路，本文主要研究成果和结论如下：
（1）理化参量反演模型及不确定性机理。
在总结分析植被理化参量遥感反演的不确定性机理和理化参量高光谱反演模型的基础上，采用经验/半经验的统计模型和物理模型反演冬小麦叶绿素含量和叶面积指数。统计模型分别采用了基于光谱导数、光谱指数、光谱位置变量和包络线吸收特征的分析方法；物理模型采用了PROSPECT叶片模型、SAIL冠层模型和PROSAIL耦合模型，这些模型是植被理化参量高光谱反演的理论支撑。
（2）基于辐射传输模型的植被理化参量反演的光谱尺度不确定性研究。
利用模型模拟的光谱数据，研究在不同光谱尺度上植被理化参量反演的不确定性。选取传统的光谱指数和新型光谱指数VIUPD，对比几种指数与叶绿素含量和叶面积指数变化的敏感性及光谱尺度的不确定性。研究发现新型指数VIUPD的反演精度最高、稳定性最好且光谱尺度不确定性最小，是叶绿素和叶面积指数反演的有效模型。同时发现基于红边位置信息建立的光谱指数反演能力也较好，仅次于VIUPD，为改进并发展新的光谱指数进行植被理化参量的反演研究提供一定的理论基础。
（3）基于单时相实测光谱的冬小麦理化参量反演光谱尺度不确定性研究。
利用单时相实测光谱数据，研究光谱指数方法在不同卫星传感器下反演冬小麦叶绿素含量和叶面积指数的光谱尺度不确定性。研究发现，由于传感器的光谱响应特性存在差异，导致同一光谱指数针对不同传感器的反演能力存在很多不确定性。通过对比分析发现，在不同传感器之间，基于通用光谱分解模式算法建立的指数VIUPD的反演精度最高且稳定性最好。
（4）基于卫星遥感数据的削弱理化参量反演不确定性的算法研究。
引入一种多/高光谱卫星数据处理方法—通用光谱分解UPDM算法，利用其“独立于传感器”的特性，将HJ1A-CCD2数据模拟为MODIS传感器数据，研究光谱指数方法在原始HJ、原始MODIS和模拟MODIS数据之间的差异以及叶绿素含量在不同光谱尺度上反演精度的不确定性。研究表明，UPDM算法具有较好的尺度适应性，有效地减小了传感器之间由于光谱通道设置而引起的叶绿素含量反演精度的不确定性。
（5）基于多时相地面实测光谱的冬小麦理化参量反演光谱尺度不确定性研究。
基于多时相实测光谱数据，采用光谱位置变量、光谱指数和去包络线分析方法，在冬小麦整个生长期和不同生育期内，研究光谱参数与理化参量的敏感性、光谱参数自身以及理化参量反演的光谱尺度不确定性。结果表明，叶绿素指数CIgreen，绿峰高度GH、红谷吸收深度RD和红边面积REA受光谱尺度的影响较小，是叶绿素含量和叶面积指数反演的有效指数。将几个生育期分开进行讨论，发现光谱参数本身及理化参量反演的光谱尺度不确定性在不同生育期之间存在差异，说明植被理化参量反演的不确定性除受光谱尺度影响外，还受时间尺度的影响。
（6）基于冬小麦理化参量反演的光谱尺度不确定性研究发现，并不是波段范围越窄，理化参量反演精度越高，因为窄波段的光谱反射率受测量环境、背景及仪器等多种因素的影响，存在一定的噪声。同时，也并不是波段范围越宽，理化参量反演的误差越大精度越低。虽然宽波段下植被光谱的部分细节信息会产生一定程度的缺失，但宽波段范围可以避免或部分消除窄波段间存在的冗余信息及噪声。因此，在植被理化参量的估算中，需考虑选取恰当的反演方法和光谱尺度。
（7）基于模拟与地面实测数据的混合光谱的冬小麦理化参量反演的不确定研究。
分别基于模型模拟光谱和地面实测光谱，选取光谱尺度不确定性研究中最好的理化参量反演模型，以光谱指数VIUPD和绿峰高度GH为例，研究冬小麦叶面积指数反演中混合光谱引起的不确定性。文中以植被和土壤两种地物类型为例，探讨不同线性混合方式下叶面积指数反演的不确定性。研究发现，土壤背景反射率的影响使植被叶面积指数的反演产生一定的不确定性。随着土壤背景所占比例的逐渐增大，叶面积指数的反演精度总体呈下降趋势，但没有统一的变化规律。同时，实测光谱数据产生的不确定性大于模拟光谱；不同时间尺度上LAI反演精度受混合光谱的影响情况存在差异；不同传感器之间，混合光谱带来的LAI反演的不确定性也存在差异。
本研究为理化参量高光谱遥感反演和高光谱传感器的研制提供一定的参考依据。在取得上述成果的同时，本论文研究也存在以下不足：
（1）HJ和MODIS遥感影像的空间分辨率相差较大，MODIS的空间分辨率过低。在研究叶绿素含量反演的光谱尺度不确定性问题时，地表异质性对像元的光谱信息产生比较明显的影响。
（2）论文只选取了冬小麦拔节、灌浆和乳熟3个生育期，时间节点偏少，下一步可利用覆盖整个生长季内更多生育期同步的光谱和理化参量数据，对时间尺度上理化参量反演的光谱尺度不确定性进行深入研究。
（3）文中不同光谱尺度的数据是利用高斯光谱响应函数模拟得来，没有与实际的遥感传感器一一对应，研究结果并不能完全代表实际情况，下一步可考虑依据多种典型传感器研究理化参量反演的光谱尺度不确定性问题。
（4）研究中将植被与土壤背景进行不同比例的线性混合来研究叶面积指数反演的不确定性，而实际上像元内部地物类型不仅仅是植被和土壤，且地物之间的组合方式往往是非线性的。因此，下一步需考虑多种地物类型以线性和非线性多种方式组合对植被理化参量反演的影响。
针对上述问题，下一步将继续进行改进和研究。



【Abstract】

The physiological and biochemical parameters of vegetation has been considered as significant input parameters in the models of ecological process, including carbon cycling, nutrient cycling and rainfall interception. Due to its unique advantages, the technology of Hyperspectral remote sensing has been applied widely in the area of predicting vegetation physiological and biochemical parameters. However, it is extremely hard to obtain the image data simultaneous with high spatial, high time and high spectral resolution because the limitation of hyperspectral satellite sensors. Therefore, the issue about uncertainty in the different scale of space, time and spectrum was proposed when retrieving vegetation physiological and biochemical parameters quantitatively.
To address this issue, based on simulated and measured spectrum data, this paper was focused on the uncertainty issue caused by spectral scale and spectral mixture when estimating chlorophyll content (Ca+b) and leaf area index (LAI) of winter wheat in different growth stages. The main methods we used contains physical models and statistical analysis models of empirical/semi-empirical. Meantime, a new spectral reconstruction algorithm derived from UPDM principle was proposed in order to weaken the uncertainty, which resulted from spectral scale among different remote sensors. And then the research about uncertainty of spectral scale was further discussed in time scale. Main conclusions and results can be deduced as follows.
(1) The models for inversing physiological and biochemical parameters and the generated mechanism of uncertainty.
The models and the mechanism of uncertainty in the estimation of vegetation physiological and biochemical parameters were thoroughly reviewed and analyzed. Statistical analysis models including derivative spectrum, spectral indices, variables of spectral positions and continuum removed method, and physical models containing PROSPECT leaf model, SAIL canopy model and PROSAIL coupling model, which were used to predict chlorophyll content and leaf area index of winter wheat. These models contribute greatly to the retrieve of vegetation physiological and biochemical parameters by hyperspectral technique and also provide the basis theory supports for its remote estimates.
(2) The research on uncertainty issue caused by spectral scale in the retrieve of vegetation physiological and biochemical parameters based on radiative transfer theories.
On the basis of simulated spectrum data from vegetation radiative transfer models, traditional spectral indices and new pattern index VIUPD were selected and further compared with their sensitivities to Ca+b and LAI and uncertainty of spectral scale. It is demonstrated that index VIUPD stand first because of its high precision, stability and slight effect by spectral scale, can be viewed as the effective model in the estimation of Ca+b and LAI. In addition, followed by VIUPD, the spectral indices built by vegetation red-edge information has a comparatively good performance. This conclusion can provide the theoretical foundation to the study of improving and developing new kinds of spectral indices in the applications of inversing vegetation physiological and biochemical parameters.
(3) The research on uncertainty issue caused by spectral scale in the retrieve of physiological and biochemical parameters of winter wheat based on single-temporal measured spectrum.
The spectral indices method was introduced to analyze the uncertainty of spectral scale produced in the estimation of Ca+b and LAI of winter wheat based on various satellite sensors. Results indicate that there are lots of uncertainties of every spectral index for their retrieve performance, due to the difference of spectral response characteristics of different remote sensors. The results we also found that spectra index VIUPD established by universal pattern decomposition method showed its high precision and best stability when predicting Ca+b and LAI.
(4) The research on algorithms about weakening uncertainty of spectral scale in the retrieve of physiological and biochemical parameters of winter wheat based on satellite remote images.
The algorithm of universal pattern decomposition method which was used to process multiple and hyperspectral data with its advantage of sensor-independent, was introduced to this paper. The simulated MODIS image generated from original HJ1A-CCD2 data based on spectral reconstruction principle using UPDM algorithms. And then the difference of spectral indices and the uncertainty of retrieve Ca+b exist in original HJ data, original MODIS and simulated MODIS were analyzed and compared. It is obvious that UPDM not only possess an excellent spectral scale adaptability, but also reduces the uncertainty caused by different setting of spectral channels among various remote sensors.
(5) The research on uncertainty issue caused by spectral scale in the retrieve of physiological and biochemical parameters of winter wheat based on multi-phase measured spectrum.
The methods including variables of derivative spectrum, spectral indices, variables of spectral positions and continuum removed method were selected and help to establish six kinds of spectral parameters (SP). In the entire and single growth periods of winter wheat, we devoted to analyze the sensitivities of SP to Ca+b and LAI, and discuss the spectral scale uncertainty which existed in spectral indices itself and the inversion of Ca+b and LAI. The results showed that chlorophyll index CIgreen, height of green peak (GH), absorption depth of red band (RD) and area of red edge (REA) were found to be slightly affected by spectral scale and can be considered as the effective index for inversing Ca+b and LAI of winter wheat. In addition, the situation of uncertainty also presented certain difference when taking the diverse growth period into consideration, which demonstrate that the generation of uncertainty issue were also affected by time scale.
(6) Based on the research about spectral scale uncertainty of inversing physiological and biochemical parameters of winter wheat, we can get another conclusion that the retrieve precision was not proportional to the spectral resolution. Certain noise could be found in the reflectance results due to the effects of environment, background, instruments and other possible factors when measuring hyperspectral data. Although the vegetation spectrum got from wide-band range would lost some details about its characteristics, it can avoid or partly eliminate the noise and redundant information among the narrow bands. Therefore, the selection of appropriate spectral scales and analyze methods must be taken into consideration in the estimation of vegetation physiological and biochemical parameters.
(7) The research on uncertainty issue caused by spectral scale in the retrieve of physiological and biochemical parameters of winter wheat based on simulated and measured spectrum.
In this part, VIUPD and GH which presented the best performance in the research about spectral scale uncertainty, were selected to explore their ability in the estimation of LAI of winter wheat with the influence of mixture spectral. Under the assumption that one pixel only contains vegetation and soil, we investigate the uncertainty issue of estimating LAI under the different linear mixture patterns. Results show that the addition of soil background into pure leaf vegetation resulted in the uncertainty when predicting LAI. It can be further found that the accuracy of LAI retrieve describes a generally declining trend with increase of the proportion of soil background, but not coming into a uniform rules. Meanwhile, the uncertainty generated from measured spectrum were greater than the simulated spectrum. And there are still some difference of uncertainty under various growth periods of winter wheat. Based on different satellite sensors, the uncertainty caused by spectrum mixing of vegetation and soil were found to be diverse in the estimation of LAI.
This study provides the basic theory for the inversion of vegetation physiological and biochemical parameters by hyperspectral remote sensing techniques and also contribute greatly to the development of hyperspectral sensors. While making the above achievements, there are some disadvantages as following:
(1) The spatial resolution of MODIS is quite different from HJ. Compared to HJ, MODIS data has too low spatial resolution. As a result, surface heterogeneity will generate significant effect on the spectral reflectance of pixel when examining the uncertainty issue of spectrum scale in the inversion of chlorophyll content.
(2) Only three growth periods including jointing, filling and milky stage were selected in this paper, the time nodes are less than normal. The further study can be conducted throughout the whole growing season and covered more growth stages. And a systematic study about the uncertainty of spectral scale would be executed under the more complete time sequence.
(3) In this work, the function of Gaussian spectral response was used to simulate the spectrum data of different spectral scale based on different Full Width Half Maximum (FWHM). However, the simulated data was not able to be fully accordant with the actual remote sensors. So there are still some limitation in the results we got. The further research we should pay close attention to more typical actual sensors in order to practically investigate the spectral scale uncertainties in the estimation of vegetation physiological and biochemical parameters.
(4) Linear mixing of vegetation and soil in different proportions was to analyze the uncertainty resulted from the inversion of LAI. In fact, the combinations of surface features are often in the non-linear way and the pixel is consist of more than just vegetation or soil. Therefore, the effects of variety of feature types with diverse combinations including linear and nonlinear should be considered in the application of estimating vegetation physiological and biochemical parameters.
Aiming at the problems mentioned above, the further investigation and improvement would be done in future.

× 【作者】 赵恒谦

【导师】 童庆禧；张立福

【 学位年度 】 2015

【论文级别】 博士

【关键词】 高光谱遥感，矿物定量反演模型，不确定性，光谱解混，吸收特征提取

【Key words】 hyperspectral remote sensing, quantitative retrieval of minerals, uncertainties, spectral unmixing, absorption feature extraction

【中文摘要】

矿物定量信息的获取，具有重要的经济和科学价值，对人类文明的延续和长远发展意义重大。高光谱遥感在矿物定量反演中具有无可取代的优势和巨大潜力，但在实际生产中还没有发挥其应有的作用，其根源在于高光谱矿物定量反演中的不确定因素，具体包括矿物光谱成因的不确定性、矿物光谱解混精度的不确定性以及矿物吸收特征提取的不确定性等等。本文紧扣这一国际难点问题，从矿物类型等五个方面系统分析了矿物光谱成因，进而从光谱解混和吸收特征提取两个角度出发，对高光谱矿物定量反演精度中的三个影响因素，即光谱解混模型、光谱位置、吸收特征提取方法开展探索研究，发展了新的高光谱矿物定量反演模型算法，提高了反演精度，为将来基于星空地及深空探测等多平台高光谱数据准确获取矿物成分含量，以及基于矿物含量开展成矿预测、行星地质演化、矿山环境修复等重要课题的研究奠定了基础。本文得到以下主要结论和成果：
（1）从矿物类型因素、矿物化学因素、矿物物理因素、矿物光谱观测因素以及矿物光谱混合因素五个方面系统分析了矿物光谱的成因，明确了各方面因素之间的相关性，其中矿物光谱混合因素是目前高光谱矿物定量反演的主要理论基础和思路来源。
（2）针对无地面采样点验证情况下的解混模型精度评估需求，总结了混合反射率光谱重建的概念，并提出从光谱维、空间维和综合维对混合反射率重建结果进行全方位分析，建立较为完善的光谱解混模型精度评估体系。基于精细定量配比的矿物粉末混合物光谱，研究了混合光谱重建误差和解混精度之间的关系，实验结果表明两者具有较高的关联性，使用混合反射率重建精度来评估解混模型的精度具有较高可行性。
（3）基于对矿物光谱解混算法和物理模型分析，提出了新的自然对数-包络线去除模型（LCR）。与线性模型、自然对数模型、包络线去除模型以及简化Hapke模型等现存模型相比，该模型在矿物粉末混合光谱和航空高光谱数据处理中表现同样出色，在光谱维、空间维以及综合维等不同维度的光谱重建精度分析中都获得了最高的精度，其结果受大气校正因素影响也最小，因此在高光谱矿物定量反演中有非常大的潜力。
（4）基于化学透过率分析中的比值导数法和遥感反射率光谱线性混合模型，首次提出了比值导数光谱解混模型，并基于该模型进行了光谱位置对于高光谱定量反演精度的研究。对矿物粉末混合光谱处理结果表明，端元矿物吸收特征对不同光谱位置解混精度有较大影响，并且精度高的波段分布在端元矿物特征吸收谷附近的陡坡或反射率光谱交点处。
（5）针对目前混合光谱中矿物吸收特征无法有效提取的现状，基于对以包络线去除为代表的背景去除算法机理进行了深入剖析，利用坐标系转换的创新思路提出一种参考背景光谱去除技术。该算法基于参考光谱波形在原始光谱节点间拟合出特定的背景光谱，并通过背景光谱去除处理消除背景成分当中重叠特征的干扰，提取出纯净的目标物吸收特征。
（6）采用参考背景光谱去除算法对矿物粉末混合物光谱和航空高光谱数据进行处理，得出该算法在吸收特征参量提取中有如下优点：1）能够从混合光谱中提取出准确的目标物吸收中心波长和吸收宽度，且不受成分丰度含量高低的影响；2）提取的吸收深度与目标物成分丰度含量呈强线性相关，对于定量反演有非常大的潜力；3）能够有效提取特征吸收波形，结合光谱角距离等光谱匹配方法能够有效识别特定矿物成分。


【Abstract】

The retrieval of quantitative information of minerals has strong economic and scientific meanings, and is essential for the long term development of human beings. Hyperspectral remote sensing has unique advantage and great potential in quantitative analysis of minerals, but has not been fully exploited in practical applications. However, the primary cause is due to the uncertain factors in quantitative analysis of minerals using hyperspectral remote sensing, which includes the complex mechanism of mineral spectra, the accuracy of spectral unmixing, and the accuract extraction of mineral absorption features. To address this issue, this paper firstly systematically analyzed the mechanism of mineral spectra, and then investigated the uncertain factors in quantitative retrieval algorithms, which can be generally grouped into two classes: spectral unmixing and absorption feature extraction. As for spectral unmixing, the effects of spectral unmixing models and band position on mineral retrival accuracy were fully investigated. In the field of absorption feature extraction, a new absorption feature extraction method was proposed to retrieve more accurate absorption parameters. This dissertation will further serve as a guide for improved quantitative retrievals of minerals from spaceborne, airborne, or other hyperspectral remote sensing platforms, and pave the way to explore some important subjects using remotely sensed mineral contents, including the metallogenic prognosis, planetary geology evolution, mine environmental restoration, etc. The main conlusions and results are as follows.
(1) The mechanism of mineral spectra were systematically analyzed from five different aspects, including the mineral types, the chemical change, the physical propoties, spectral measurement conditions, and spectral mixing effect, and discussed about the correlations between these factors. Among them, spectral mixing effect is the main source and theory basis of quantitative analysis of minerals using hyperspectral remote sensing data.
(2) This dissertation summarized the concept of mixing reflectance reconstruction (MRR), and proposed a thorough method to determine the accuracies of spectral unmixing models based on MRR. The MRR error can be analyzed from 3 different dimensions, including the spectral dimension, the spatial dimension, and the total dimension. By measuring the spectra of proportionally mixed mineral powders, we were able to investigate and verify the relationship between MRR accuracy and spectral unmixing accuracy. This finding validates that when the actual fractions are not available, it is possible to estimate the spectral unmixing accuracy based on an MRR accuracy analysis.
(3) A newly developed LCR model was developed based on investigating the existing spectral unmixing algorithms and physical mechanism analysis. Existing models having typical applications related to mineral analysis, including the Linear model, the NL model, the CR model and the SH model, were also summarized. Experiments on the well-known AVIRIS data for Cuprite allowed us to evaluate the effects of the five unmixing models based on the MRR accuracy analysis. The results revealed that the LCR model yielded good results in nearly all aspects, and had considerable potential for practical application. By comparing the total MRR error of ATREM dataset and Flat Field dataset, the effects of atmospheric correction on spectral unmixing accuracy were verified, but the level of influence was different for different unmixing models. LCR model achieved the most outstanding results, which verified its great robustness.
(4) This dissertation proposed a new spectral unmixing model based on derivative of ratio spectroscopy (DRS), which can directly interpret the correspondence between the target content and variations of mixing spectra, eliminate the influence of other substances in the mixture, and extract the bands which are more sensitive to the target information. Based on this model, the effects of spectral position on spectral unmixing accuracy were investigated using mineral powder mixtures. The results indicated that the absorption features have strong influence on the spectral unmixing accuracy, and the bands near the slopes of absorption feature valleys tend to have higher accuracy.
(5) Based on the study of the mechanism of background removal methods, represented by continuum removal, a new spectral fitting method was presented to obtain the background curve, and a novel background removal method named reference spectral background removal (RSBR) was given. RSBR retains the advantages of continuum removal, and when given the reference spectral background, RSBR can eliminate the influence of unwanted contribution factor, and extract the absorption feature of target contribution factor.
(6) Based experiments on both mineral powder mixtures and airborne hyperspectral data, RSBR was demonstrated to have the following advantages in extraction of absorption feature parameters: 1) RSBR can extract accurate absorption centers and absorption widths from mixing spectrum, independent of the variation in abundance; 2) absorption depths calculated from the RSBR spectra are strongly linearly correlated to the fractions of the component of interest; 3) the spectral waveform of the specific absorption factor can be well extracted by RSBR, and by using spectral matching methods, such as SAM, the mineral composition can be identified.

× 【作者】 王桂珍

【导师】 张立福；吴太夏

【 学位年度 】 2015

【论文级别】 硕士

【关键词】 成像光谱 钻孔岩心编录 界面设计 功能设计

【Key words】 Imaging Spectrometer; drill core record compiling; interface design; function design

【中文摘要】



随着矿产勘查技术的的不断发展，在地表开展矿产勘查的工作难度越来越大，勘查的主要方向已转向地表之下的大地岩层。岩心作为矿产勘查的最终环节，记录着地质体垂向变化信息，是珍贵的地质资料。在不破坏岩心表面及内部结构的前提下，遥感作为一种新的矿产勘查手段具有宏观、快速、信息量丰富等优势，是地质找矿中不可或缺的手段。高光谱遥感技术具有依据不同矿物对不同波长光的反射差异探测岩心矿物组成及其相对含量的能力，所获得的遥感地质信息有利于地质专家分析地质成矿条件，圈定找矿远景区和目标靶区，为开展详细地质工作提供可靠的依据。
本文以实现岩心成像光谱数据编录系统设计为目标，对岩心成像光谱编录系统的关键技术和系统功能和界面进行详细的设计。在充分考虑用户需求的前提下，通过对岩心编录所需要的稳定、高效、准确等方面研究总结，针对矿物的光谱特征，分析岩心成像光谱数据的光谱质量，探讨光谱匹配技术的精度，对岩心成像光谱编录系统功能进行详细的分析和设计；从用户使用习惯、方便快捷掌握软件和界面美观大方三个方面展开了岩心成像光谱数据编录系统的详细设计。论文的主要成果和结论如下：
1. 分析和总结了常见蚀变矿物的光谱特征，并在此基础上探讨了高光谱数据的各种矿物的光谱信息提取方法，如最小距离匹配、光谱角度匹配、光谱吸收指数等。用这些方法评估岩心成像光谱数据的光谱质量，探讨光谱匹配技术的精度，将光谱特征量化，以数学计算的方法实现未知目标的判别，解决岩心矿物识别的问题，实现岩心快速解译和分析。
2. 在深入分析岩心成像光谱数据编录系统的功能需求和目标、全面设计系统体系结构以及对编录系统功能构架和界面设计原则的研究基础上，根据岩心成像光谱数据的特点和形态应用的需求，提出了HyCore编录系统的整体设计方案。
3. 详细设计了岩心成像光谱编录系统界面和功能模块。将界面分为菜单栏、常用工具栏、钻孔显示窗口、岩心编录窗口、数理分析窗口和参数设置窗口6个部分，并对每个部分的详细功能进行设计，在满足岩心编录各个功能的基础上，力求空间的合理安排和界面的美观；功能模块设计中从用户角度设计为编录系统的自动功能，实现数据的导入、自动裁剪、自动解译和地质资料的编辑等。


【Abstract】

With the continuous development of mineral exploration technology, the surface mineral exploration work became more and more difficult. The main direction of the mineral investigation is toward the underground. Core as the final link in mineral exploration keep the records of vertical change geological information. Without undermining core surface and destroying the core integrity, remote sensing as a new technology have macroscopical, fast and informative advantages. It is an indispensable means of geological prospecting. Hyperspectral remote sensing technology has the ability to identify the different minerals and composition according to the different minerals have different spectrum feature. Geological experts can use remote sensing geological information to analysis the geological metallogenic conditions, find prospecting areas and delineate target goals which providing good information to carry out detailed geological work.
The goal of this paper is design the core imaging spectrometer data catalog system including the interface and system function design. Firstly, in the condition of full consideration of the needs of users, the core catalog need stable, efficient, accurate, etc. This paper make a detail design on the core imaging spectrometer catalog system. Also this paper explore the spectral characteristics of rocks and minerals and study the key technologies of mineral identification. Then this paper work on the detailed design of the core imaging spectrometer system catalog fully considering three aspects: user habits, convenient and elegant interface master the software. The main results and conclusions are as follows:
1. Analyzed and summarized the common spectral characteristics of altered minerals. On the knowledge of various mineral spectral information, this paper studied the hyperspectral data extraction methods, such as the minimum distance matching, spectral angle matching, spectral absorption index etc.; Evaluated the spectral quality of core imaging spectral data using these methods, and investigated the accuracy of spectral matching technique. Quantified the spectral characteristics of minerals and used the mathematical methods in calculations to achieve unknown targets. Solved the core mineral identification problem, rapidly realized the translation and analysis of core information.
2. Based on the analysis and summarize the spectral characteristics of common alteration minerals, this paper explore the spectral information enhancement and various mineral extraction methods, such as spectral derivative, envelope removal, the minimum distance matching, spectral angle matching and spectral absorption index. Using those methods to make comprehensive statistical analysis of imaging spectrometer data core, the spectral characteristics can be quantified with the method of mathematical calculations to achieve the goal of determining the unknown targets. It solve the identified problems of the mineral core and achieve quick deciphering and analysis.
3. This paper amply design the interface and function modules of the core imaging spectrometer catalog system. The interface is divided into six part menu bar, toolbar, drilling display window, core catalog window, mathematical analysis window and parameter setting window. And design detailed function of each part to meet the basic need of each catalog functions. Also the interface strive to reasonably arrange space and make beautiful interface. From the user point of view the function module was design to automatically catalog to achieve the function of import of data, automatic cutting, automatic interpretation and editing and other geological data.