Identificación no-supervisada de parcelas agrícolas en imágenes satelitales multiespectrales basado en la semejanza de pixeles homólogos en las distintas bandas

En este trabajo se presenta un algoritmo para identificar parcelas agrícolas en imágenes multiespectrales de Landsat 7. En un primer paso se analiza cada banda de la imagen con un proceso gaussiano. Luego, se ajustan los parámetros de un filtro no-lineal de acuerdo a los resultados del análisis. Al final, se aplica un algoritmo de segmentación que busca conjuntos de pixeles similares en todas las bandas. Este algoritmo tiene un parámetro que permite definir el nivel de semejanza de las parcelas agrícolas. Por lo tanto, es posible identificar parcelas poco homogéneas y después estudiar en detalle la composición de una. Los resultados del filtro propuesto son prometedores y facilitan la segmentación de la imagen satelital. El algoritmo de segmentación identifica las parcelas agrícolas en la imagen usada con un alto nivel de precisión y, además, detecta estructuras escondidas como pivotes de riego.In this work an algorithm for the identification of agricultural parcels in multispectral Landsat 7 images is presented. In a first step each band is analyzed with a Gaussian process. Afterwards the parameters of a non-lineal filter are adjusted according to the results of the analysis. Finally a segmentation algorithm which searches for pixels with similar feature vectors is executed. This algorithm has a free parameter which allows defining a similarity threshold for each field. Therefore it is possible to identify inhomogeneous fields first and then have a closer look at their compounding. The results of the filter are promising and simplify the segmentation of the satellite image. The segmentation algorithm identifies the agricultural fields in the given image with a high degree of reliability, besides that it discovers hidden structures like irrigation pivots.Sociedad Argentina de Informática e Investigación Operativ

Clasificación de áreas sembradas y determinación del momento de cosecha en caña de azúcar y pastizales mediante imágenes ópticas y SAR

Tesis (Magister en Aplicaciones de Información Espacial)--Universidad Nacional de Córdoba, Facultad de Matemática, Astronomía, Física y Computación, 2018.Maestría conjunta con el Instituto de Altos Estudios Espaciales "Mario Gulich"- CONAE.La interpretación fenológica y la clasificación de cultivos a partir de sensores remotos surgen como las aplicaciones más utilizadas y necesarias de cara al asesoramiento agronómico a partir de técnicas espaciales. El desarrollo de esta tesis tiene como finalidad explorar el comportamiento de las imágenes SAR y ópticas en diferentes cultivos, tales como el cultivo de la caña de azúcar y los pastizales naturales, obtener el área sembrada con caña de azúcar y realizar mapas de momentos fenológicos de interés. Esta situación se contempla en diferentes zonas de estudio: la región central de la Provincia de Tucumán para el cultivo de la caña de azúcar y la región norte de Italia para el análisis de los pastizales naturales. Los sensores satelitales utilizados son: Landsat 8 como exponente de imágenes ópticas, Cosmo SkyMed en polarización HH y Sentinel 1 polarización HV como exponentes de imágenes SAR. Se realizó de manera exploratoria una comparación de datos ópticos y datos SAR. Se utilizó un conjunto de imágenes SAR, para evaluar clasificadores supervisados y no supervisados. Finalmente se realizaron mapas a partir del algoritmo de árbol de decisión para determinar momentos fenológicos particulares como lo es la cosecha en caña de azúcar y el inicio de crecimiento en pastizales naturales. Es necesario abordar con mayor profundidad la comparación de datos ópticos y SAR y seleccionar polarizaciones contrastantes para su evaluación. La clasificación SAR fue adecuada con los clasificadores no supervisados y baja con el clasificador supervisado evaluado. Los mapas que se obtuvieron mediante el uso del algoritmo de árbol de decisión, permitieron analizar eventos puntuales de manera óptima en grandes superficies.The phenological interpretation and the classification crops from remote sensors they arise as the most used and necessary applications with a view to the agronomic advice from spatial technologies. The development of this thesis has as purpose explore the behavior of the images SAR and optical in different crops, such as the crop of the sugar cane and the meadows, to obtain the area sowed with sugar cane and to realize maps of moments phenological of interest. This situation contemplates different zones of study the central region of Tucumán Province for the crop of the sugar cane and the north region of Italy for the analysis of the meadows. The satellite sensors used are Landsat 8 as exponent of optical images, Cosmo SkyMed in polarization HH and Sentinel 1 polarization HV as exponents of images SAR. There was realized in an exploratory way a comparison of optical information and information SAR. A set of images SAR was in use for evaluating supervised and not supervised classifiers. Finally maps were realized from the algorithm of tree of decision to determine particular phenological moment, such as harvest in the crop in sugar cane and the beginning of growth in natural pastures. It is necessary to approach with major depth the comparison of optical information and SAR and to select polarizations contrasting for his evaluation. The SAR classification was adequate with the unsupervised classifiers and low with the supervised classifier evaluated. The maps that were obtained by using the decision tree algorithm, allowed to analyze specific events optimally in large areas.Fil: Pascual, Ignacio Gastón. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.Fil: Pascual, Ignacio Gastón. Universidad Nacional de Córdoba. Instituto de Altos Estudios Espaciales Mario Gulich; Argentina

Autonomous Vehicles: MMW Radar Backscattering Modeling of Traffic Environment, Vehicular Communication Modeling, and Antenna Designs

77 GHz Millimeter-wave (mmWave) radar serves as an essential component among many sensors required for autonomous navigation. High-fidelity simulation is indispensable for nowadays’ development of advanced automotive radar systems because radar simulation can accelerate the design and testing process and help people to better understand and process the radar data. The main challenge in automotive radar simulation is to simulate the complex scattering behavior of various targets in real time, which is required for sensor fusion with other sensory simulation, e.g. optical image simulation. In this thesis, an asymptotic method based on a fast-wideband physical optics (PO) calculation is developed and applied to get high fidelity radar response of traffic scenes and generate the corresponding radar images from traffic targets. The targets include pedestrians, vehicles, and other stationary targets. To further accelerate the simulation into real time, a physics-based statistical approach is developed. The RCS of targets are fit into statistical distributions, and then the statistical parameters are summarized as functions of range and aspect angles, and other attributes of the targets. For advanced radar with multiple transmitters and receivers, pixelated-scatterer statistical RCS models are developed to represent objects as extend targets and relax the requirement for far-field condition. A real-time radar scene simulation software, which will be referred to as Michigan Automotive Radar Scene Simulator (MARSS), based on the statistical models are developed and integrated with a physical 3D scene generation software (Unreal Engine 4). One of the major challenges in radar signal processing is to detect the angle of arrival (AOA) of multiple targets. A new analytic multiple-sources AOA estimation algorithm that outperforms many well-known AOA estimation algorithms is developed and verified by experiments. Moreover, the statistical parameters of RCS from targets and radar images are used in target classification approaches based on machine learning methods. In realistic road traffic environment, foliage is commonly encountered that can potentially block the line-of-sight link. In the second part of the thesis, a non-line-of-sight (NLoS) vehicular propagation channel model for tree trunks at two vehicular communication bands (5.9 GHz and 60 GHz) is proposed. Both near-field and far-field scattering models from tree trunk are developed based on modal expansion and surface current integral method. To make the results fast accessible and retractable, a macro model based on artificial neural network (ANN) is proposed to fit the path loss calculated from the complex electromagnetic (EM) based methods. In the third part of the thesis, two broadband (bandwidth > 50%) omnidirectional antenna designs are discussed to enable polarization diversity for next-generation communication systems. The first design is a compact horizontally polarized (HP) antenna, which contains four folded dipole radiators and utilizing their mutual coupling to enhance the bandwidth. The second one is a circularly polarized (CP) antenna. It is composed of one ultra-wide-band (UWB) monopole, the compact HP antenna, and a dedicatedly designed asymmetric power divider based feeding network. It has about 53% overlapping bandwidth for both impedance and axial ratio with peak RHCP gain of 0.9 dBi.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163001/1/caixz_1.pd

Methods for sugarcane harvest detection using polarimetric SAR

Thesis (MA)--Stellenbosch University, 2017.ENGLISH ABSTRACT: Remote sensing has long been used as a method for crop harvest monitoring and harvest classification. Harvest monitoring is necessary for the planning of and prompting of effective agricultural practices. Traditionally sugarcane harvest monitoring and classification within the realm of remote sensing is performed with the use of optical data. However, when monitoring sugarcane, the growth period of the crop requires a complete set of multi-temporal image acquisitions throughout the year. Due to the limitations associated with optical sensors, the use of all weather, daylight independent Synthetic Aperture Radar (SAR) sensors is required. The added polarimetric information associated with fully polarimetric SAR sensors result in complex datasets which are expensive to acquire. It is therefore important to assess the benefits of using a fully polarimetric dataset for sugarcane harvest monitoring as opposed to a dual polarimetric dataset. The dual polarimetric dataset which is less complex in nature and can be acquired at a fee much less than that of the fully polarimetric dataset. This thesis undertakes the task of identifying the value of fully polarimetric data for sugarcane harvest identification and classification. Two main experiments were designed in order to complete the task. The experiments make use of fully polarimetric RADARSAT-2 C-band imagery covering the southern part of Rèunion Island. Experiment 1 made use of a multi temporal single feature differencing technique for sugarcane harvest identification. Polarimetric decompositions were extracted from the fully polarimetric data and used along with the inherent SAR features. The accuracy with which each SAR feature was able to predict the sugarcane harvest date for each field was assessed. The polarimetric decompositions were superior in classification accuracy to the inherent SAR features. The Van Zyl volume decomposition component achieved an accuracy of 88.33% whereas the inherent SAR backscatter feature (HV) achieved an accuracy of 80%. Hereby displaying the value of the added information associated with fully polarimetric SAR data. The SAR backscatter channels did not achieve accuracies as high as the polarimetric features but did display promise for single feature sugarcane harvest identification when using only a dual polarimetric dataset. Experiment 2 assessed six different machine learning classifiers, applied to single-date, dual- and fully polarized imagery, to determine appropriate combinations of machine learning classifier and SAR features. Polarimetric decompositions were extracted from the fully polarimetric data and mean texture measures were then calculated for all SAR features for both the dual- and full polatrimetric data. A multi-tiered feature reduction method was undertaken in order to reduce dataset dimensionality for the dual- and fully polarised datasets. In general, the reduction in features resulted in improved accuracies. The best sugarcane harvest accuracy was achieved using the Maximum likelihood classifier using on the HV and VV backscatter channels (96.18%). The results from Experiments 1 and 2 indicate that SAR C-band data is suitable for sugarcane harvest monitoring and mapping in a tropical region where optical data have limitations associated with cloud cover and large amounts of moisture in the atmosphere. With the availability of dual polarised Sentinel-1 SAR data, future research should be focussed on the use of a dual polarimetric sugarcane harvest monitoring tool and should be extended to focus not only on sugarcane but other crops which contribute largely to the agriculture and economic sectors.AFRIKAANS OPSOMMING: Afstandswaarneming word lankal reeds gebruik as ‘n metode in die monitering van die oes van gewasse asook vir oes-klassifikasie. Oes-monitering is nodig vir die beplanning en stimulering van effektiewe landboupraktyke. Tradisioneel word suikerriet oes-monitering en klassifisering, binne die raamwerk van afstandswaarneming, uitgevoer met die gebruik van optiese data. Tog, met die monitering van suikerriet, vereis die groeiperiode van die gewas ‘n volledige stel multi-temporale beeldverwerwings dwarsdeur die jaar. As gevolg van die beperkings geassosieer met optiese sensors, word die gebruik van daglig onafhanklike sintetiese gaatjie radar sensors, eerder bekend as Sintetiese Apertuur Radar (SAR) sensors, vir gebruik in alle weersomstandighede, vereis. Die bykomende polarimetriese informasie geassosieer met ten volle gepolarimetriese SAR sensors lei tot komplekse datastelle wat duur is om aan te skaf. Dit is daarom belangrik om die voordele van die gebruik van ‘n ten volle gepolarimetriese datastel vir suikerriet oes-monitering in teenstelling met ‘n tweeledige polarimetriese datastel wat minder kompleks van aard is en teen ‘n fooi veel minder as dié van die ten volle gepolarimetriese datastel verkry kan word, te evalueer. Hierdie tesis onderneem die taak van die identifisering van die waarde van ten volle gepolarimetriese data vir suikerriet oes-identifikasie en -klassifikasie. Twee hoof-eksperimente is ontwerp om die taak te voltooi. Die eksperimente gebruik ten volle gepolarimetriese RADARSAT-2 C-band beelde wat die suidelike deel van Reunion-eiland dek. Met eksperiment 1 is gebruik gemaak van 'n multi-temporale enkelkenmerk differensie- tegniek vir suikerriet oes-identifisering. Polarimetriese ontledings is uit die ten volle gepolarimetriese data geneem en saam met die inherente SAR kenmerke gebruik. Die akkuraatheid waarmee elke SAR kenmerk in staat was om die suikerriet oes-datum vir elke veld te voorspel, is geëvalueer. Die polarimetriese ontledings was beter in klassifikasie- akkuraatheid as die inherente SAR kenmerke. Hiermee word die waarde van die bykomende inligting geassosieer met ten volle gepolarimetriese SAR data, geopenbaar. Die SAR teruguitsaaiingskanale het nie akkuraathede so hoog soos die polarimetriese kenmerke bereik nie, maar het belofte getoon vir enkelkenmerk suikerriet oes-identifikasie wanneer slegs van 'n tweeledige polarimetriese datastel gebruik gemaak word. Met eksperiment 2 is ses verskillende masjien-leer klassifiseerders, toegepas op enkeldatum, tweeledige en ten volle gepolariseerde beelde, geëvalueer om toepaslike kombinasies van masjien-leer klassifiseerder en SAR kenmerke te bepaal. Polarimetriese ontledings is geneem uit die ten volle gepolarimetriese data en beteken dat tekstuur afmetings toe bereken is vir alle SAR kenmerke vir beide die tweeledige- en ten volle gepolarimetriese data. 'n Multi-reeks kenmerkreduksie-metode is onderneem om datasteldimensionaliteit te verminder vir die tweeledige- en ten volle gepolariseerde datastelle. Oor die algemeen het die redusering van kenmerke verbeterde akkuraatheid tot gevolg gehad. Die beste suikerriet oes-akkuraatheid is behaal deur die Maksimum waarskynlikheid klassifiseerder met behulp van die HV en VV teruguitsaaiingskanale (96,18%) te gebruik. Die resultate van eksperimente 1 en 2 dui daarop dat SAR C-band data geskik is vir suikerriet oes- monitering en kartering in 'n tropiese streek waar optiese data beperkings toon wat geassosieer word met wolkbedekking en groot hoeveelhede vog in die atmosfeer. Met die beskikbaarheid van tweeledige gepolariseerde Sentinel-1 SAR data, behoort toekomstige navorsing gefokus te wees op die gebruik van 'n tweeledige polarimetriese suikerriet oes- moniteringshulpmiddel en behoort dit uitgebrei te word om te fokus nie net op suikerriet nie, maar ook ander gewasse wat grootliks bydra tot die landbou- en ekonomiese sektore

Caractérisation des occupations du sol en milieu urbain par imagerie radar

Cette étude vise à tester la pertinence des images RSO - de moyenne et de haute résolution - à la caractérisation des types d’occupation du sol en milieu urbain. Elle s’est basée sur des approches texturales à partir des statistiques de deuxième ordre. Plus spécifiquement, on recherche les paramètres de texture les plus pertinents pour discriminer les objets urbains. Il a été utilisé à cet égard des images Radarsat-1 en mode fin en polarisation HH et Radarsat-2 en mode fin en double et quadruple polarisation et en mode ultrafin en polarisation HH. Les occupations du sol recherchées étaient le bâti dense, le bâti de densité moyenne, le bâti de densité faible, le bâti industriel et institutionnel, la végétation de faible densité, la végétation dense et l’eau. Les neuf paramètres de textures analysés ont été regroupés, en familles selon leur définition mathématique. Les paramètres de ressemblance/dissemblance regroupent l’Homogénéité, le Contraste, la Similarité et la Dissimilarité. Les paramètres de désordre sont l’Entropie et le Deuxième Moment Angulaire. L’Écart-Type et la Corrélation sont des paramètres de dispersion et la Moyenne est une famille à part. Il ressort des expériences que certaines combinaisons de paramètres de texture provenant de familles différentes utilisés dans les classifications donnent de très bons résultants alors que d’autres associations de paramètres de texture de définition mathématiques proches génèrent de moins bons résultats. Par ailleurs on constate que si l’utilisation de plusieurs paramètres de texture améliore les classifications, la performance de celle-ci plafonne à partir de trois paramètres. Malgré la bonne performance de cette approche basée sur la complémentarité des paramètres de texture, des erreurs systématiques dues aux effets cardinaux subsistent sur les classifications. Pour pallier à ce problème, il a été développé un modèle de compensation radiométrique basé sur la section efficace radar (SER). Une simulation radar à partir du modèle numérique de surface du milieu a permis d'extraire les zones de rétrodiffusion des bâtis et d'analyser les rétrodiffusions correspondantes. Une règle de compensation des effets cardinaux fondée uniquement sur les réponses des objets en fonction de leur orientation par rapport au plan d'illumination par le faisceau du radar a été mise au point. Des applications de cet algorithme sur des images RADARSAT-1 et RADARSAT-2 en polarisations HH, HV, VH, et VV ont permis de réaliser de considérables gains et d’éliminer l’essentiel des erreurs de classification dues aux effets cardinaux.This study aims to test the relevance of medium and high-resolution SAR images on the characterization of the types of land use in urban areas. To this end, we have relied on textural approaches based on second-order statistics. Specifically, we look for texture parameters most relevant for discriminating urban objects. We have used in this regard Radarsat-1 in fine polarization mode and Radarsat-2 HH fine mode in dual and quad polarization and ultrafine mode HH polarization. The land uses sought were dense building, medium density building, low density building, industrial and institutional buildings, low density vegetation, dense vegetation and water. We have identified nine texture parameters for analysis, grouped into families according to their mathematical definitions in a first step. The parameters of similarity / dissimilarity include Homogeneity, Contrast, the Differential Inverse Moment and Dissimilarity. The parameters of disorder are Entropy and the Second Angular Momentum. The Standard Deviation and Correlation are the dispersion parameters and the Average is a separate family. It is clear from experience that certain combinations of texture parameters from different family used in classifications yield good results while others produce kappa of very little interest. Furthermore, we realize that if the use of several texture parameters improves classifications, its performance ceils from three parameters. The calculation of correlations between the textures and their principal axes confirm the results. Despite the good performance of this approach based on the complementarity of texture parameters, systematic errors due to the cardinal effects remain on classifications. To overcome this problem, a radiometric compensation model was developed based on the radar cross section (SER). A radar simulation from the digital surface model of the environment allowed us to extract the building backscatter zones and to analyze the related backscatter. Thus, we were able to devise a strategy of compensation of cardinal effects solely based on the responses of the objects according to their orientation from the plane of illumination through the radar's beam. It appeared that a compensation algorithm based on the radar cross section was appropriate. Some examples of the application of this algorithm on HH polarized RADARSAT-2 images are presented as well. Application of this algorithm will allow considerable gains with regard to certain forms of automation (classification and segmentation) at the level of radar imagery thus generating a higher level of quality in regard to visual interpretation. Application of this algorithm on RADARSAT-1 and RADARSAT-2 images with HH, HV, VH, and VV polarisations helped make considerable gains and eliminate most of the classification errors due to the cardinal effects

Télédétection radar appliquée au suivi des rizières. Méthodes utilisant le rapport des intensités de rétrodiffusion.

Because of the importance of rice in global food security and of the role of rice paddies in methane emissions, a large-scale near-real-time monitoring system of rice fields appears to be particularly useful. The objective of this work is to develop methods aiming at an effective use of remote sensing data from past and future satellites for rice fields monitoring. Radar imagery is preferred to optical imagery, due to its all-weather ability. Two methods are considered, both involving a C-band SAR intensity ratio as a classification feature: the HH/VV polarization ratio and the co-polarized temporal change HHdate2/HHdate1. First, a statistical study of intensity ratios is done, leading to the development of an error model that estimates the performance of the classification methods. The error model is also used to assess the impact of SAR system parameters (calibration, ambiguity ratio, revisit frequency) on the classification accuracy. Then, these classification methods are applied to two datasets acquired by the ASAR instrument onboard ENVISAT over the Mekong Delta, Vietnam, in order to map rice fields at two scales. The first approach relies on the use of the HH/VV polarization ratio calculated from the Alternating Polarization mode of ASAR, and is applied to produce a rice map covering one province in the delta. The second approach uses the HH temporal change of Wide-Swath mode images from ASAR, and allows mapping rice fields over the whole delta. Both methods are validated with success through the use of the cultivated areas reported in national statistics.En raison de l'importance du riz dans l'alimentation mondiale et du rôle des rizières dans les émissions de méthane, un suivi à grande échelle et en temps quasi-réel des surfaces cultivées en riz semble particulièrement utile. L'objectif de cette thèse est de développer des méthodes permettant une utilisation effective des données de télédétection des satellites présents et futurs pour le suivi des rizières. L'imagerie radar est privilégiée car elle permet des acquisitions sous toutes les conditions météorologiques, contrairement à l'imagerie optique. Deux méthodes sont retenues qui font intervenir un rapport d'intensité de deux images SAR en bande C : le rapport de polarisation HH/VV ou le changement temporel en co-polarisation HHdate2/HHdate1. Dans un premier temps, une étude statistique des rapports d'intensité de rétrodiffusion est effectuée, qui conduit au développement d'un modèle d'erreur permettant d'estimer la performance des méthodes de classification. Ce modèle d'erreur est également utilisé pour évaluer l'impact des paramètres des systèmes SAR (Synthetic Aperture Radar) sur la performance de la classification. Il s'agit des paramètres concernant l'étalonnage, l'ambiguïté, la fréquence de revisite. Dans un second temps, les méthodes de classification ainsi développées sont appliquées à deux jeux de données de l'instrument ASAR du satellite ENVISAT sur le delta du Mékong au Vietnam, pour faire la cartographie des rizières à deux échelles différentes. La première méthode repose sur l'utilisation du rapport HH/VV à partir de données du mode Alternating Polarization d'ASAR, qui permet de produire une carte de rizières couvrant une province du delta. La seconde méthode tire parti du changement temporel de HH sur des images du mode Wide-Swath d'ASAR, et est utilisée pour cartographier les rizières de l'ensemble du delta. Les deux méthodes sont validées avec succès en utilisant les surfaces cultivées données par les statistiques nationales