Detecting changes in land cover through time using remotely sensed imagery is a powerful application that has seen increased use, as imagery has become more widely available and inexpensive. Before a time series of remotely sensed imagery can be used for change detection, images must first be standardized for effects outside of real surface change.
This thesis introduced the concept, mathematical mode, and the work flow of relative radiometric normalization. This thesis established an improved pseudo-invariant features method to normalize temporally separate but spatially coincident imagery. Using the concept of pseudo-invariant features between master-slave image pairs, spatially coincident urban features with difference thresholds are identified from images; then these features were filtered using principal component analysis, their quality control is through correlation coefficients; finally a regression equation is calculated using robust orthogonal regression to normalize slave images to a master. This improved method uses objective statistical characteristics of sampling points, overcomes the defect of subjective sampling, and improves the normalization accuracy, so as to guarantee further effective extraction of land use/cover change information.
This thesis used two sets of imagery to test the performance of the standardization process, a temporally variable image pair of the same sensor, and a temporally variable image pair of different sensors. This thesis calculate RMSE, statistical characteristics, slope of the major axis in PCA, and performed simple NDVI image subtraction, to validate the reduction of master-slave differences using invariant locations. As a result of the standardization process, RMSE showed decreases in master-slave differences, statistical characteristics including mean and standard deviation is more similar to the master image, the slope of major axis in PCA is closer to unit, and NDVI image subtraction showed decreases in master-slave differences. Also, this thesis compared the result of this improved method with other methods including image regression, histogram match, Schott-PIF, and multivariate alteration detection. According to visual inspection and quantitative evaluation parameters, this improved method has accuracy and reliability; it can sample pseudo invariant points with better quality, and eliminate the multi-temporal differences and multi-sensor differences. After relative radiometric normalization, we perform change detection using three change detection algorithms and make land cover change detection analysis.
【Key words】 the vegetation index time series of remotely sensed data, Harmonic Analysis, HANTS, number of frequency, outlier detection
地面成像光谱系统有着极其广阔的应用前景，而相关硬件研制、系统开发和应用研究在我国尚处于起步阶段。基于这种系统研发与应用研究都严重不足的现状，本文立足于我国第一套地面成像光谱系统（Field Imaging Spectrometer System ,FISS），主要完成两项系统性的工作：其一，在系统研发与评价方面，完成了对地面成像光谱辐射仪的数据定标、数据预处理以及测量规范的初步探讨，系统地评价了其各项性能和特点，为促使其向系统化、集成化方向发展开展了基础性的工作；其二，在系统应用与推广方面，围绕植物信息提取这个主题进行了探索性研究工作，特别是对植物“精准”信息的提取，主要从植物精细识别（类别信息）、植物生化参量反演以及植物对环境因素的生理反应状况三个方面开展了详细的研究，为植物信息的快速获取提供了新技术和新方法，同时也为地面成像光谱系统的推广使用提供了示范研究。论文的主要研究内容和成果如下：
Field imaging spectrometer system spans a very broad range of applications. However, sensor design, system development and application researches are all at the very beginning stage in China. To promote both of development and application of this kind of system, based on Field Imaging Spectrometer System (FISS) which was firstly self-developed in China, this dissertation has a pioneer exlporation of this system. The study concentrates mainly on two aspects: for the development and evaluation of FISS, the research discusses data calibration, data pre-processing, operation specifications and instruction for use of FISS, evaluates the overall performance of this novel system, lays the first stone for its progressing towards systematization and integration. For the application and popularization of FISS, the research mainly focuses on plant information extraction especially at the respect of precise information, namely, plant precise identification and classification, biochemical parameters estimation and physiological reaction adapting to change of environmental factors, presents new technology and methods for quick acquisition of plant information, and thus provides a demonstration research for the wide-use of this system in future. Main contents and results are summarized as follows:
1. The first self-developed field imaging spectrometer system (FISS) is introduced in detail, including its imaging principle, structural design and technical parameters. Spectral calibration, radiometric calibration and data pre-process such as noise estimation, noise reduction, reflectance calculation and so on, are also discussed. Based on numerous field and indoor experiments, operation specifications and instruction for use of this novel system are summarized as well.
2. Under outdoor conditions, crop-weed discrimination is explored and studied using FISS, two discrimination models Linear Discrimination Analysis (LDA) and Support Vector Machine (SVM) with different features (spectral bands and wavelet coefficients) are compared. The results show that good discrimination accuracies could be obtained using several spectral bands and those bands located in ‘red edge’ range have prominent discrimination performance. For different models, SVM with V nonlinear trait is superior to LDA especially in weed-crop multi-classes discrimination. For different features, wavelet coefficients show better performance when the amount of variables used is small (e.g. less than 10), but the difference becomes negligible when amount of variables increases.
3. Biochemical parameters are estimated based on FISS, four models, namely, Linear Regression (LR), Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR), were compared. No prominent difference was observed between estimation accuracies using different models. Features impose much influence on estimation accuracy than models. To estimate chlorophyll contents of leaves, only several key spectral bands are needed and a multiple linear model could be competent.
4. Image information could be a good indicator of chlorophyll contents of leaves. Fusion of image information and spectral information could improve precision of chlorophyll contents estimation further. Due to its unique mode of data acquisition and trait of combination of image and spectral information, FISS could be employed to estimate chlorophyll contents more accurately. Estimation error using data from FISS is 30%-40% lower than error using counterpart data from non-imaging spectrometer.
5. This dissertation studies spectral changes of plant responding to natural illumination changes. This kind of changes could be used as indicators of plant physiological status. Photochemical Reflectance Index (PRI) shows a pattern of lower values around noon and higher values during morning and dusk. This corresponds to mechanism of plant heat dissipated (xanthophyll cycle), and is consistent with Light Use Efficiency (LUE) and Photosynthetically Active Radiation (PAR) changing patterns. This result indicates that plant heat dissipation information could be detected from spectral changes using FISS data.
6. Solar induced fluorescence information was successfully extracted from FISS data based on Fraunhofer Line Depth (FLD) method using 760nm O − A 2 absorption band. Fluorescence image spectrum with high spatial resolution was obtained and showed abundant information of spatial details. Fluorescence presents an ideal pattern of higher values around noon and lower values during morning and dusk. Both spatial distribution and temporal variation of Fluorescence are in consistent with physiological changes of plants. Compared to spectral indices, Fluorescence is much sensitive to LUE and thus could be a better indicator of LUE. This result may be directly used for researches of plant physiological status and its change responding to environmental stresses, may be widely used for crop yield estimation, Chinese medicinal materials production, environmental stress detection and monitoring, plant pests and diseases and many other application fields. This study is also beneficial to the development of passive remote sensing on airborne or spaceborne platforms to detect fluorescence of plants.
7. The phenomenon of blue shift of red edge of plant leaves during dehydration process is successfully detected using FISS data and visualized in the form of image series.
8. This dissertation evaluated and assessed the whole performance of FISS from researches of three respects on plant identification, biochemical parameter and physiological change. All shows that FISS has good spectral and radiometric properties and could be used in quantitative researches and precise information mapping.