6 research outputs found

    Spatial pattern recognition for crop-livestock systems using multispectral data

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    Within the field of pattern recognition (PR) a very active area is the clustering and classification of multispectral data, which basically aims to allocate the right class of ground category to a reflectance or radiance signal. Generally, the problem complexity is related to the incorporation of spatial characteristics that are complementary to the nonlinearities of land surface process heterogeneity, remote sensing effects and multispectral features. The present research describes the application of learning machine methods to accomplish the above task by inducting a relationship between the spectral response of farms’ land cover, and their farming system typology from a representative set of instances. Such methodologies are not traditionally used in crop-livestock studies. Nevertheless, this study shows that its application leads to simple and theoretically robust classification models. The study has covered the following phases: a)geovisualization of crop-livestock systems; b)feature extraction of both multispectral and attributive data and; c)supervised farm classification. The first is a complementary methodology to represent the spatial feature intensity of farming systems in the geographical space. The second belongs to the unsupervised learning field, which mainly involves the appropriate description of input data in a lower dimensional space. The last is a method based on statistical learning theory, which has been successfully applied to supervised classification problems and to generate models described by implicit functions. In this research the performance of various kernel methods applied to the representation and classification of crop-livestock systems described by multispectral response is studied and compared. The data from those systems include linear and nonlinearly separable groups that were labelled using multidimensional attributive data. Geovisualization findings show the existence of two well-defined farm populations within the whole study area; and three subgroups in relation to the Guarico section. The existence of these groups was confirmed by both hierarchical and kernel clustering methods, and crop-livestock systems instances were segmented and labeled into farm typologies based on: a)milk and meat production; b)reproductive management; c)stocking rate; and d)crop-forage-forest land use. The minimum set of labeled examples to properly train the kernel machine was 20 instances. Models inducted by training data sets using kernel machines were in general terms better than those from hierarchical clustering methodologies. However, the size of the training data set represents one of the main difficulties to be overcome in permitting the more general application of this technique in farming system studies. These results attain important implications for large scale monitoring of crop-livestock system; particularly to the establishment of balanced policy decision, intervention plans formulation, and a proper description of target typologies to enable investment efforts to be more focused at local issues

    Improvements to algorithms for hyperspectral linear unmixing based on statistical model

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    Spectral mixing is one of the main problems that arise when characterizing the spectral constituents residing at a sub-pixel level in a hyperspectral scene. In this work we propose a improvement of the algorithms based on statistical model, i.e. NCM, with a novel sampling approach inspired by Genetic Algorithms. Furthermore, linearization is introduced to reduce computational complexity

    Service robotics and machine learning for close-range remote sensing

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    Hyperspectral data analysis of typical surface covers in Hong Kong.

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    Ma Fung-yan.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 137-141).Abstracts in English and Chinese.Abstract --- p.iAcknowledgements --- p.ivTable of Contents --- p.vList of Tables --- p.ixList of Figures --- p.xChapter CHAPTER 1 --- INTRODUCTIONChapter 1.1 --- Introduction and background --- p.1Chapter 1.2 --- Objectives --- p.4Chapter 1.3 --- Significance --- p.5Chapter 1.4 --- Organization of the thesis --- p.5Chapter CHAPTER 2 --- LITERATURE REVIEWChapter 2.1 --- Introduction --- p.7Chapter 2.2 --- Hyperspectral remote sensing --- p.7Chapter 2.2.1 --- Current imaging spectrometers available --- p.8Chapter 2.2.2 --- Applications of hyperspectral remote sensing --- p.9Chapter 2.2.2.1 --- Biochemistry of vegetation --- p.10Chapter 2.2.2.2 --- Spatial and temporal patterns of vegetation --- p.12Chapter 2.3 --- Tree species recognition --- p.12Chapter 2.3.1 --- Factors affecting spectral reflectance of vegetation --- p.14Chapter 2.3.1.1 --- Optical properties of leaf --- p.14Chapter 2.3.1.2 --- Canopy structure --- p.15Chapter 2.3.1.3 --- Canopy cover --- p.16Chapter 2.3.1.4 --- Illumination and viewing geometry --- p.16Chapter 2.3.1.5 --- Spatial and temporal dynamics of plants --- p.17Chapter 2.3.2 --- Classification algorithms for hyperspectral analysis --- p.17Chapter 2.3.2.1 --- Use of derivative spectra for tree species recognition --- p.17Chapter 2.3.2.2 --- Linear discriminant analysis --- p.18Chapter 2.3.2.3 --- Artificial neural network --- p.19Chapter 2.3.3 --- Tree species recognition using hyperspectral data --- p.21Chapter 2.4 --- Data compression and feature extraction --- p.22Chapter 2.4.1 --- Analytical techniques of data compression --- p.23Chapter 2.4.2 --- Analytical techniques of feature extraction --- p.25Chapter 2.4.2.1 --- Feature selection by correlation with biochemical and biophysical data --- p.25Chapter 2.4.2.2 --- Spatial autocorrelation-based feature selection --- p.27Chapter 2.4.2.3 --- Spectral autocorrelation-based feature selection --- p.29Chapter 2.4.2.3.1 --- Optimization with distance metrics --- p.29Chapter 2.4.2.3.2 --- Stepwise linear discriminant analysis --- p.30Chapter 2.5 --- Summary --- p.31Chapter CHAPTER 3 --- METHODOLOGYChapter 3.1 --- Introduction --- p.33Chapter 3.2 --- Study site --- p.33Chapter 3.3 --- Instrumentation --- p.34Chapter 3.4 --- Data collection --- p.35Chapter 3.4.1 --- Laboratory measurement --- p.36Chapter 3.4.2 --- In situ measurement --- p.39Chapter 3.5 --- Methods of data analysis --- p.40Chapter 3.5.1 --- Preprocessing of data --- p.40Chapter 3.5.2 --- Compilation of hyperspectral database --- p.42Chapter 3.5.3 --- Tree species recognition --- p.42Chapter 3.5.3.1 --- Linear discriminant analysis --- p.44Chapter 3.5.3.2 --- Artificial neural network --- p.44Chapter 3.5.3.3 --- Accuracy assessment --- p.45Chapter 3.5.3.4 --- Comparison of different data processing strategies and classifiers --- p.45Chapter 3.5.3.5 --- Comparison of data among different seasons --- p.46Chapter 3.5.3.6 --- Comparison of laboratory and in situ data --- p.46Chapter 3.5.4 --- Data compression --- p.47Chapter 3.5.5 --- Band selection --- p.47Chapter 3.6 --- Summary --- p.48Chapter CHAPTER 4 --- RESULTS AND DISCUSSIONS OF TREE SPECIES RECOGNITIONChapter 4.1 --- Introduction --- p.50Chapter 4.2 --- Characteristics of hyperspectral data --- p.50Chapter 4.3 --- Tree species recognition --- p.79Chapter 4.3.1 --- Comparison of different classifiers --- p.82Chapter 4.3.1.1 --- Efficiency of the classifiers --- p.83Chapter 4.3.1.2 --- Discussions --- p.83Chapter 4.3.2 --- Comparison of different data processing strategies --- p.84Chapter 4.3.3 --- Comparison of data among different seasons --- p.86Chapter 4.3.4 --- Comparison of laboratory and in situ data --- p.88Chapter 4.4 --- Summary --- p.92Chapter CHAPTER 5 --- RESULTS AND DISCUSSIONS OF DATA COMPRESSION AND BAND SELECTIONChapter 5.1 --- Introduction --- p.93Chapter 5.2 --- Data compression --- p.93Chapter 5.2.1 --- PCA using in situ spectral data --- p.93Chapter 5.2.1.1 --- Characteristics of PC loadings --- p.95Chapter 5.2.1.2 --- Scatter plots of PC scores --- p.96Chapter 5.2.2 --- PCA using laboratory spectral data --- p.99Chapter 5.2.2.1 --- Characteristics of PC loadings --- p.102Chapter 5.2.2.2 --- Scatter plots of PC scores --- p.103Chapter 5.2.2.3 --- Results of tree species recognition using PC scores --- p.107Chapter 5.2.3 --- Implications --- p.107Chapter 5.3 --- Band selection --- p.108Chapter 5.3.1 --- Preliminary band selection using stepwise discriminant analysis --- p.108Chapter 5.3.1.1 --- Selection of spectral bands --- p.109Chapter 5.3.1.2 --- Classification results of the selected bands --- p.109Chapter 5.3.1.3 --- Seasonal comparison using stepwise linear discriminant analysis --- p.114Chapter 5.3.1.4 --- Implications --- p.116Chapter 5.3.2 --- Band selection using hierarchical clustering technique --- p.116Chapter 5.3.2.1 --- Hierarchical clustering procedure --- p.116Chapter 5.3.2.2 --- Selection of spectral band sets --- p.119Chapter 5.3.2.3 --- Classification results of the selected band sets --- p.124Chapter 5.4 --- Summary --- p.127Chapter CHAPTER 6 --- SUMMARY AND CONCLUSIONChapter 6.1 --- Introduction --- p.129Chapter 6.2 --- Summary --- p.129Chapter 6.2.1 --- Tree species recognition --- p.129Chapter 6.2.2 --- Data compression --- p.130Chapter 6.2.3 --- Band selection --- p.131Chapter 6.3 --- Limitations of this study --- p.132Chapter 6.4 --- Recommendations for further studies --- p.133Chapter 6.5 --- Conclusion --- p.136BIBLIOGRAPHY --- p.137APPENDICESAppendix 1 Reflectance of the 25 tree species in four seasons with three levels of leaf density --- p.142-166"Appendix 2 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra with 138 bands classified by linear discriminant analysis for each season" --- p.167-178"Appendix 3 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra with 138 bands classified by neural networks for each season" --- p.179-190Appendix 4 Confusion matrices of tree species recognition using 21 tree species with original spectra classified by linear discriminant analysis for seasonal comparison --- p.191-193Appendix 5 Confusion matrices of tree species recognition using the first eight PC scores classified by linear discriminant analysis for each season --- p.194-197"Appendix 6 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra classified by stepwise linear discriminant analysis (Case 2) for each season" --- p.198-209"Appendix 7 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra classified by stepwise linear discriminant analysis (Case 3) for each season" --- p.210-220"Appendix 8 Confusion matrices of tree species recognition using 21 tree species with original spectra, first derivatives spectra and second derivatives spectra classified by stepwise linear discriminant analysis for seasonal comparison" --- p.221-229Appendix 9 Confusion matrices of tree species recognition using the spectral bands selected by hierarchical clustering procedures and classified by linear discriminant analysis for each season --- p.230-25

    Hyperspectral image representation and processing with binary partition trees

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    The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. Therefore, under the title Hyperspectral image representation and Processing with Binary Partition Trees, this PhD thesis proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation: the Binary Partition Tree (BPT). This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure. Hence, the Binary Partition Tree succeeds in presenting: (i) the decomposition of the image in terms of coherent regions and (ii) the inclusion relations of the regions in the scene. Based on region-merging techniques, the construction of BPT is investigated in this work by studying hyperspectral region models and the associated similarity metrics. As a matter of fact, the very high dimensionality and the complexity of the data require the definition of specific region models and similarity measures. Once the BPT is constructed, the fixed tree structure allows implementing efficient and advanced application-dependent techniques on it. The application-dependent processing of BPT is generally implemented through a specific pruning of the tree. Accordingly, some pruning techniques are proposed and discussed according to different applications. This Ph.D is focused in particular on segmentation, object detection and classification of hyperspectral imagery. Experimental results on various hyperspectral data sets demonstrate the interest and the good performances of the BPT representatio
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