O monitoramento do meio ambiente e suas mudanças requer a análise de uma grade quantidade de imagens muitas vezes coletadas por satélites. No entanto, variações nos sinais devido a mudanças nas condições atmosféricas frequentemente resultam num deslocamento da distribuição dos dados para diferentes locais e datas. Isso torna difícil a distinção dentre as várias classes de uma base de dados construída a partir de várias imagens. Neste trabalho introduzimos uma nova abordagem de classificação supervisionada, chamada Classifica-Normaliza-Classifica (CNC), para amenizar o problema de deslocamento dos dados. A proposta é implementada usando dois classificadores. O primeiro é treinado em imagens não normalizadas de refletância de topo de atmosfera para distinguir dentre pixels de uma classe de interesse (CDI) e pixels de outras categorias (e.g. floresta versus não-floresta). Dada uma nova imagem de teste, o primeiro classificador gera uma segmentação das regiões da CDI e então um vetor mediano é calculado para os valores espectrais dessas áreas. Então, esse vetor é subtraído de cada pixel da imagem e portanto fixa a distribuição de dados de diferentes imagens num mesmo referencial. Finalmente, o segundo classificador, que é treinado para minimizar o erro de classificação em imagens já centralizadas pela mediana, é aplicado na imagem de teste normalizada no segundo passo para produzir a segmentação binária final. A metodologia proposta foi testada para detectar desflorestamento em pares de imagens co-registradas da Landsat 8 OLI sobre a floresta Amazônica. Experimentos usando imagens multiespectrais de refletância de topo de atmosfera mostraram que a CNC obteve maior acurácia na detecção de desflorestamento do que classificadores aplicados em imagens de refletância de superfície fornecidas pelo United States Geological Survey. As acurácias do método proposto também se mostraram superiores às obtidas pelas máscaras de desflorestamento do programa PRODES. / Monitoring natural environments and their changes over time requires the analysis of a large amount of image data, often collected by orbital remote sensing platforms. However, variations in the observed signals due to changing atmospheric conditions often result in a data distribution shift for different dates and locations making it difficult to discriminate between various classes in a dataset built from several images. This work introduces a novel supervised classification framework, called Classify-Normalize-Classify (CNC), to alleviate this data shift issue. The proposed scheme uses a two classifier approach. The first classifier is trained on non-normalized top-of-the-atmosphere reflectance samples to discriminate between pixels belonging to a class of interest (COI) and pixels from other categories (e.g. forest vs. non-forest). At test time, the estimated COI’s multivariate median signal, derived from the first classifier segmentation, is subtracted from the image and thus anchoring the data distribution from different images to the same reference. Then, a second classifier, pre-trained to minimize the classification error on COI median centered samples, is applied to the median-normalized test image to produce the final binary segmentation. The proposed methodology was tested to detect deforestation using bitemporal Landsat 8 OLI images over the Amazon rainforest. Experiments using top-of-the-atmosphere multispectral reflectance images showed that the deforestation was mapped by the CNC framework more accurately as compared to running a single classifier on surface reflectance images provided by the United States Geological Survey (USGS). Accuracies from the proposed framework also compared favorably with the benchmark masks of the PRODES program.

Background Subtraction is one of the fundamental pre-processing steps in video processing. It helps to distinguish between foreground and background for any given image and thus has numerous applications including security, privacy, surveillance and traffic monitoring to name a few. Unfortunately, no single algorithm exists that can handle various challenges associated with background subtraction such as illumination changes, dynamic background, camera jitter etc. In this work, we propose a Multiple Background Model based Background Subtraction (MB2S) system, which is universal in nature and is robust against real life challenges associated with background subtraction. It creates multiple background models of the scene followed by both pixel and frame based binary classification on both RGB and YCbCr color spaces. The masks generated after processing these input images are then combined in a framework to classify background and foreground pixels. Comprehensive evaluation of proposed approach on publicly available test sequences show superiority of our system over other state-of-the-art algorithms.

In this contributon we evaluate on-line and off-line techniques to train a single hidden layer neural network classifier with logistic hidden and softmax output transfer functions on a multispectral pixel-by-pixel classification problem. In contrast to current practice a multiple class cross-entropy error function has been chosen as the function to be minimized. The non-linear diffierential equations cannot be solved in closed form. To solve for a set of locally minimizing parameters we use the gradient descent technique for parameter updating based upon the backpropagation technique for evaluating the partial derivatives of the error function with respect to the parameter weights. Empirical evidence shows that on-line and epoch-based gradient descent backpropagation fail to converge within 100,000 iterations, due to the fixed step size. Batch gradient descent backpropagation training is superior in terms of learning speed and convergence behaviour. Stochastic epoch-based training tends to be slightly more effective than on-line and batch training in terms of generalization performance, especially when the number of training examples is larger. Moreover, it is less prone to fall into local minima than on-line and batch modes of operation. (authors' abstract) / Series: Discussion Papers of the Institute for Economic Geography and GIScience

This paper evaluates the classification accuracy of three neural network classifiers on a satellite image-based pattern classification problem. The neural network classifiers used include two types of the Multi-Layer-Perceptron (MLP) and the Radial Basis Function Network. A normal (conventional) classifier is used as a benchmark to evaluate the performance of neural network classifiers. The satellite image consists of 2,460 pixels selected from a section (270 x 360) of a Landsat-5 TM scene from the city of Vienna and its northern surroundings. In addition to evaluation of classification accuracy, the neural classifiers are analysed for generalization capability and stability of results. Best overall results (in terms of accuracy and convergence time) are provided by the MLP-1 classifier with weight elimination. It has a small number of parameters and requires no problem-specific system of initial weight values. Its in-sample classification error is 7.87% and its out-of-sample classification error is 10.24% for the problem at hand. Four classes of simulations serve to illustrate the properties of the classifier in general and the stability of the result with respect to control parameters, and on the training time, the gradient descent control term, initial parameter conditions, and different training and testing sets. (authors' abstract) / Series: Discussion Papers of the Institute for Economic Geography and GIScience

O monitoramento do meio ambiente e suas mudanças requer a análise de uma grade quantidade de imagens muitas vezes coletadas por satélites. No entanto, variações nos sinais devido a mudanças nas condições atmosféricas frequentemente resultam num deslocamento da distribuição dos dados para diferentes locais e datas. Isso torna difícil a distinção dentre as várias classes de uma base de dados construída a partir de várias imagens. Neste trabalho introduzimos uma nova abordagem de classificação supervisionada, chamada Classifica-Normaliza-Classifica (CNC), para amenizar o problema de deslocamento dos dados. A proposta é implementada usando dois classificadores. O primeiro é treinado em imagens não normalizadas de refletância de topo de atmosfera para distinguir dentre pixels de uma classe de interesse (CDI) e pixels de outras categorias (e.g. floresta versus não-floresta). Dada uma nova imagem de teste, o primeiro classificador gera uma segmentação das regiões da CDI e então um vetor mediano é calculado para os valores espectrais dessas áreas. Então, esse vetor é subtraído de cada pixel da imagem e portanto fixa a distribuição de dados de diferentes imagens num mesmo referencial. Finalmente, o segundo classificador, que é treinado para minimizar o erro de classificação em imagens já centralizadas pela mediana, é aplicado na imagem de teste normalizada no segundo passo para produzir a segmentação binária final. A metodologia proposta foi testada para detectar desflorestamento em pares de imagens co-registradas da Landsat 8 OLI sobre a floresta Amazônica. Experimentos usando imagens multiespectrais de refletância de topo de atmosfera mostraram que a CNC obteve maior acurácia na detecção de desflorestamento do que classificadores aplicados em imagens de refletância de superfície fornecidas pelo United States Geological Survey. As acurácias do método proposto também se mostraram superiores às obtidas pelas máscaras de desflorestamento do programa PRODES. / Monitoring natural environments and their changes over time requires the analysis of a large amount of image data, often collected by orbital remote sensing platforms. However, variations in the observed signals due to changing atmospheric conditions often result in a data distribution shift for different dates and locations making it difficult to discriminate between various classes in a dataset built from several images. This work introduces a novel supervised classification framework, called Classify-Normalize-Classify (CNC), to alleviate this data shift issue. The proposed scheme uses a two classifier approach. The first classifier is trained on non-normalized top-of-the-atmosphere reflectance samples to discriminate between pixels belonging to a class of interest (COI) and pixels from other categories (e.g. forest vs. non-forest). At test time, the estimated COI’s multivariate median signal, derived from the first classifier segmentation, is subtracted from the image and thus anchoring the data distribution from different images to the same reference. Then, a second classifier, pre-trained to minimize the classification error on COI median centered samples, is applied to the median-normalized test image to produce the final binary segmentation. The proposed methodology was tested to detect deforestation using bitemporal Landsat 8 OLI images over the Amazon rainforest. Experiments using top-of-the-atmosphere multispectral reflectance images showed that the deforestation was mapped by the CNC framework more accurately as compared to running a single classifier on surface reflectance images provided by the United States Geological Survey (USGS). Accuracies from the proposed framework also compared favorably with the benchmark masks of the PRODES program.

O monitoramento do meio ambiente e suas mudanças requer a análise de uma grade quantidade de imagens muitas vezes coletadas por satélites. No entanto, variações nos sinais devido a mudanças nas condições atmosféricas frequentemente resultam num deslocamento da distribuição dos dados para diferentes locais e datas. Isso torna difícil a distinção dentre as várias classes de uma base de dados construída a partir de várias imagens. Neste trabalho introduzimos uma nova abordagem de classificação supervisionada, chamada Classifica-Normaliza-Classifica (CNC), para amenizar o problema de deslocamento dos dados. A proposta é implementada usando dois classificadores. O primeiro é treinado em imagens não normalizadas de refletância de topo de atmosfera para distinguir dentre pixels de uma classe de interesse (CDI) e pixels de outras categorias (e.g. floresta versus não-floresta). Dada uma nova imagem de teste, o primeiro classificador gera uma segmentação das regiões da CDI e então um vetor mediano é calculado para os valores espectrais dessas áreas. Então, esse vetor é subtraído de cada pixel da imagem e portanto fixa a distribuição de dados de diferentes imagens num mesmo referencial. Finalmente, o segundo classificador, que é treinado para minimizar o erro de classificação em imagens já centralizadas pela mediana, é aplicado na imagem de teste normalizada no segundo passo para produzir a segmentação binária final. A metodologia proposta foi testada para detectar desflorestamento em pares de imagens co-registradas da Landsat 8 OLI sobre a floresta Amazônica. Experimentos usando imagens multiespectrais de refletância de topo de atmosfera mostraram que a CNC obteve maior acurácia na detecção de desflorestamento do que classificadores aplicados em imagens de refletância de superfície fornecidas pelo United States Geological Survey. As acurácias do método proposto também se mostraram superiores às obtidas pelas máscaras de desflorestamento do programa PRODES. / Monitoring natural environments and their changes over time requires the analysis of a large amount of image data, often collected by orbital remote sensing platforms. However, variations in the observed signals due to changing atmospheric conditions often result in a data distribution shift for different dates and locations making it difficult to discriminate between various classes in a dataset built from several images. This work introduces a novel supervised classification framework, called Classify-Normalize-Classify (CNC), to alleviate this data shift issue. The proposed scheme uses a two classifier approach. The first classifier is trained on non-normalized top-of-the-atmosphere reflectance samples to discriminate between pixels belonging to a class of interest (COI) and pixels from other categories (e.g. forest vs. non-forest). At test time, the estimated COI’s multivariate median signal, derived from the first classifier segmentation, is subtracted from the image and thus anchoring the data distribution from different images to the same reference. Then, a second classifier, pre-trained to minimize the classification error on COI median centered samples, is applied to the median-normalized test image to produce the final binary segmentation. The proposed methodology was tested to detect deforestation using bitemporal Landsat 8 OLI images over the Amazon rainforest. Experiments using top-of-the-atmosphere multispectral reflectance images showed that the deforestation was mapped by the CNC framework more accurately as compared to running a single classifier on surface reflectance images provided by the United States Geological Survey (USGS). Accuracies from the proposed framework also compared favorably with the benchmark masks of the PRODES program.

Dust storms are one of the natural hazards, which have increased in frequency in the recent years over Sahara desert, Australia, the Arabian Desert, Turkmenistan and northern China, which have worsened during the last decade. Dust storms increase air pollution, impact on urban areas and farms as well as affecting ground and air traffic. They cause damage to human health, reduce the temperature, cause damage to communication facilities, reduce visibility which delays both road and air traffic and impact on both urban and rural areas. Thus, it is important to know the causation, movement and radiation effects of dust storms. The monitoring and forecasting of dust storms is increasing in order to help governments reduce the negative impact of these storms. Satellite remote sensing is the most common method but its use over sandy ground is still limited as the two share similar characteristics. However, satellite remote sensing using true-colour images or estimates of aerosol optical thickness (AOT) and algorithms such as the deep blue algorithm have limitations for identifying dust storms. Many researchers have studied the detection of dust storms during daytime in a number of different regions of the world including China, Australia, America, and North Africa using a variety of satellite data but fewer studies have focused on detecting dust storms at night. The key elements of this present study are to use data from the Moderate Resolution Imaging Spectroradiometers on the Terra and Aqua satellites to develop more effective automated method for detecting dust storms during both day and night and generate a MODIS dust storm database.

Various techniques of optimizing the multiple class cross-entropy error function to train single hidden layer neural network classifiers with softmax output transfer functions are investigated on a real-world multispectral pixel-by-pixel classification problem that is of fundamental importance in remote sensing. These techniques include epoch-based and batch versions of backpropagation of gradient descent, PR-conjugate gradient and BFGS quasi-Newton errors. The method of choice depends upon the nature of the learning task and whether one wants to optimize learning for speed or generalization performance. It was found that, comparatively considered, gradient descent error backpropagation provided the best and most stable out-of-sample performance results across batch and epoch-based modes of operation. If the goal is to maximize learning speed and a sacrifice in generalisation is acceptable, then PR-conjugate gradient error backpropagation tends to be superior. If the training set is very large, stochastic epoch-based versions of local optimizers should be chosen utilizing a larger rather than a smaller epoch size to avoid inacceptable instabilities in the generalization results. (authors' abstract) / Series: Discussion Papers of the Institute for Economic Geography and GIScience

Dust storms are one of the natural hazards, which have increased in frequency in the recent years over Sahara desert, Australia, the Arabian Desert, Turkmenistan and northern China, which have worsened during the last decade. Dust storms increase air pollution, impact on urban areas and farms as well as affecting ground and air traffic. They cause damage to human health, reduce the temperature, cause damage to communication facilities, reduce visibility which delays both road and air traffic and impact on both urban and rural areas. Thus, it is important to know the causation, movement and radiation effects of dust storms. The monitoring and forecasting of dust storms is increasing in order to help governments reduce the negative impact of these storms. Satellite remote sensing is the most common method but its use over sandy ground is still limited as the two share similar characteristics. However, satellite remote sensing using true-colour images or estimates of aerosol optical thickness (AOT) and algorithms such as the deep blue algorithm have limitations for identifying dust storms. Many researchers have studied the detection of dust storms during daytime in a number of different regions of the world including China, Australia, America, and North Africa using a variety of satellite data but fewer studies have focused on detecting dust storms at night. The key elements of this present study are to use data from the Moderate Resolution Imaging Spectroradiometers on the Terra and Aqua satellites to develop more effective automated method for detecting dust storms during both day and night and generate a MODIS dust storm database. / Libyan Centre for Remote Sensing and Space Science / Appendix A was submitted with extra data files which are not available online.

En segmentation d’images, les informations de couleur et de texture sont très utilisées. Le premier apport de cette thèse se situe au niveau de l’utilisation conjointe de ces deux sources d’informations. Nous proposons alors une méthode de combinaison couleur/texture, adaptative et non paramétrique, qui consiste à combiner un (ou plus) gradient couleur et un (ou plus) gradient texture pour ensuite générer un gradient structurel utilisé comme image de potentiel dans l’algorithme de croissance de régions par LPE. L’originalité de notre méthode réside dans l’étude de la dispersion d’un nuage de point 3D dans l’espace, en utilisant une étude comparative des valeurs propres obtenues par une analyse des composantes principales de la matrice de covariance de ce nuage de points. L’approche de combinaison couleur/texture proposée est d’abord testée sur deux bases d’images, à savoir la base générique d’images couleur de BERKELEY et la base d’images de texture VISTEX. Cette thèse s’inscrivant dans le cadre des projets ViLoc (RFC) et CAPLOC (PREDIT), le deuxième apport de celle-ci se situe au niveau de la caractérisation de l’environnement de réception des signaux GNSS pour améliorer le calcul de la position d’un mobile en milieu urbain. Dans ce cadre, nous proposons d’exclure certains satellites (NLOS dont les signaux sont reçus par réflexion voir totalement bloqués par les obstacles environnants) dans le calcul de la position d’un mobile. Deux approches de caractérisation, basées sur le traitement d’images, sont alors proposées. La première approche consiste à appliquer la méthode de combinaison couleur/texture proposée sur deux bases d’images réelles acquises en mobilité, à l’aide d’une caméra fisheye installée sur le toit du véhicule de laboratoire, suivie d’une classification binaire permettant d’obtenir les deux classes d’intérêt « ciel » (signaux LOS) et « non ciel » (signaux NLOS). Afin de satisfaire la contrainte temps réel exigée par le projet CAPLOC, nous avons proposé une deuxième approche basée sur une simplification de l’image couplée à une classification pixellaire adaptée. Le principe d’exclusion des satellites NLOS permet d’améliorer la précision de la position estimée, mais uniquement lorsque les satellites LOS (dont les signaux sont reçus de manière direct) sont géométriquement bien distribués dans l’espace. Dans le but de prendre en compte cette connaissance relative à la distribution des satellites, et par conséquent, améliorer la précision de localisation, nous avons proposé une nouvelle stratégie pour l’estimation de position, basée sur l’exclusion des satellites NLOS (identifiés par le traitement d’images), conditionnée par l’information DOP, contenue dans les trames GPS. / Color and texture are two main information used in image segmentation. The first contribution of this thesis focuses on the joint use of color and texture information by developing a robust and non parametric method combining color and texture gradients. The proposed color/texture combination allows defining a structural gradient that is used as potential image in watershed algorithm. The originality of the proposed method consists in studying a 3D points cloud generated by color and texture descriptors, followed by an eigenvalue analysis. The color/texture combination method is firstly tested and compared with well known methods in the literature, using two databases (generic BERKELEY database of color images and the VISTEX database of texture images). The applied part of the thesis is within ViLoc project (funded by RFC regional council) and CAPLOC project (funded by PREDIT). In this framework, the second contribution of the thesis concerns the characterization of the environment of GNSS signals reception. In this part, we aim to improve estimated position of a mobile in urban environment by excluding NLOS satellites (for which the signal is masked or received after reflections on obstacles surrounding the antenna environment). For that, we propose two approaches to characterize the environment of GNSS signals reception using image processing. The first one consists in applying the proposed color/texture combination on images acquired in mobility with a fisheye camera located on the roof of a vehicle and oriented toward the sky. The segmentation step is followed by a binary classification to extract two classes « sky » (LOS signals) and « not sky » (NLOS signals). The second approach is proposed in order to satisfy the real-time constraint required by the application. This approach is based on image simplification and adaptive pixel classification. The NLOS satellites exclusion principle is interesting, in terms of improving precision of position, when the LOS satellites (for which the signals are received directly) are well geometrically distributed in space. To take into account the knowledge of satellite distribution and then increase the precision of position, we propose a new strategy of position estimation, based on the exclusion of NLOS satellites (identified by the image processing step), conditioned by DOP information, which is provided by GPS data.