Detecting and evaluating land cover change in the eastern half of East Timor (1972-2011)

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Avaliação de técnicas de classificação digital de imagens landsat em diferentes padrões de cobertura da terra em Rondônia

Remotely sensed imagery has been widely used for land use/cover classification thanks to the periodic data acquisition and the widespread use of digital image processing systems offering a wide range of classification algorithms. The aim of this work was to evaluate some of the most commonly used supervised and unsupervised classification algorithms under different landscape patterns found in Rondônia, including (1) areas of mid-size farms, (2) fish-bone settlements and (3) a gradient of forest and Cerrado (Brazilian savannah). Comparison with a reference map based on the kappa statistics resulted in good to superior indicators (best results - K-means: k=0.68; k=0.77; k=0.64 and MaxVer: k=0.71; k=0.89; k=0.70 respectively for three areas mentioned). Results show that choosing a specific algorithm requires to take into account both its capacity to discriminate among various spectral signatures under different landscape patterns as well as a cost/benefit analysis considering the different steps performed by the operator performing a land cover/use map. it is suggested that a more systematic assessment of several options of implementation of a specific project is needed prior to beginning a land use/cover mapping job.

A toolbox for hyperspectral image analysis

This paper introduces a new toolbox for hyperspectral imagery, developed under the MATLAB environment. This toolbox provides easy access to different supervised and unsupervised classification methods. This new application is also versatile and fully dynamic since the user can embody their own methods, that can be reused and shared. This toolbox, while extends the potentiality of MATLAB environment, it also provides a user-friendly platform to assess the results of different methodologies. In this paper it is also presented, under the new application, a study of several different supervised and unsupervised classification methods on real hyperspectral data.

Os Sistemas de Informação Geográfica e Detecção Remota na Determinação das Regiões de Risco por Malária na Guiné-Bissau

A malária é uma doença infecciosa complexa, que resulta do “vírus” plasmodium, e manifesta-se sob cinco tipos distintos de espécies protozoários (plasmodium vivax, plasmodium ovale, plasmodium falciparum, plasmodium malariae e plasmodium Knowlesi), atacando sobretudo os glóbulos vermelhos. Considerada a quinta maior causa de morte por doenças infecciosas em todo o mundo após doenças respiratórias, VIH/SIDA, doenças diarreicas e tuberculose, no continente africano, a malária é considerada a segunda causa do aumento da mortalidade, após VIH/SIDA. No caso particular da Guiné-Bissau, esta constitui a principal causa do incremento da morbilidade e da mortalidade naquele país, onde, em 2012 foram notificados 129.684 casos de paludismo, dos quais 370 resultaram em óbitos. Partindo da realidade acima constatada, em particular, da complexidade e o impacto global da doença associada a uma forte mortalidade e morbilidade, concluiu-se ser necessário abordar esta temática, utilizando os SIG e a DR no sentido de determinar as regiões de elevado risco. Entendeu-se serem necessárias novas abordagens e novas ferramentas de análise dos dados epidemiológicos e consequentemente novas metodologias que possibilitem a determinação de áreas de risco por malária. O presente estudo, pretende demonstrar o papel dos SIG e DR na determinação das regiões de risco por malária. A metodologia utilizada centrou-se numa abordagem quantitativa baseada na hierarquização das variáveis. Pretende-se, assim abordar os impactos da malária e simultaneamente demonstrar as potencialidades dos SIG e das ferramentas de Análise Espacial no estudo da disseminação da mesma na Guiné-Bissau.

Environmental Monitoring using Image Analysis

Tärkeä tehtävä ympäristön tarkkailussa on arvioida ympäristön nykyinen tila ja ihmisen siihen aiheuttamat muutokset sekä analysoida ja etsiä näiden yhtenäiset suhteet. Ympäristön muuttumista voidaan hallita keräämällä ja analysoimalla tietoa. Tässä diplomityössä on tutkittu vesikasvillisuudessa hai vainuja muutoksia käyttäen etäältä hankittua mittausdataa ja kuvan analysointimenetelmiä. Ympäristön tarkkailuun on käytetty Suomen suurimmasta järvestä Saimaasta vuosina 1996 ja 1999 otettuja ilmakuvia. Ensimmäinen kuva-analyysin vaihe on geometrinen korjaus, jonka tarkoituksena on kohdistaa ja suhteuttaa otetut kuvat samaan koordinaattijärjestelmään. Toinen vaihe on kohdistaa vastaavat paikalliset alueet ja tunnistaa kasvillisuuden muuttuminen. Kasvillisuuden tunnistamiseen on käytetty erilaisia lähestymistapoja sisältäen valvottuja ja valvomattomia tunnistustapoja. Tutkimuksessa käytettiin aitoa, kohinoista mittausdataa, minkä perusteella tehdyt kokeet antoivat hyviä tuloksia tutkimuksen onnistumisesta.

An environment for knowledge discovery in biology

This paper describes a data mining environment for knowledge discovery in bioinformatics applications. The system has a generic kernel that implements the mining functions to be applied to input primary databases, with a warehouse architecture, of biomedical information. Both supervised and unsupervised classification can be implemented within the kernel and applied to data extracted from the primary database, with the results being suitably stored in a complex object database for knowledge discovery. The kernel also includes a specific high-performance library that allows designing and applying the mining functions in parallel machines. The experimental results obtained by the application of the kernel functions are reported. © 2003 Elsevier Ltd. All rights reserved.

Object and Scene Classification: what does a Supervised Approach Provide us?

Given a set of images of scenes containing different object categories (e.g. grass, roads) our objective is to discover these objects in each image, and to use this object occurrences to perform a scene classification (e.g. beach scene, mountain scene). We achieve this by using a supervised learning algorithm able to learn with few images to facilitate the user task. We use a probabilistic model to recognise the objects and further we classify the scene based on their object occurrences. Experimental results are shown and evaluated to prove the validity of our proposal. Object recognition performance is compared to the approaches of He et al. (2004) and Marti et al. (2001) using their own datasets. Furthermore an unsupervised method is implemented in order to evaluate the advantages and disadvantages of our supervised classification approach versus an unsupervised one

Object and Scene Classification: what does a Supervised Approach Provide us?

Given a set of images of scenes containing different object categories (e.g. grass, roads) our objective is to discover these objects in each image, and to use this object occurrences to perform a scene classification (e.g. beach scene, mountain scene). We achieve this by using a supervised learning algorithm able to learn with few images to facilitate the user task. We use a probabilistic model to recognise the objects and further we classify the scene based on their object occurrences. Experimental results are shown and evaluated to prove the validity of our proposal. Object recognition performance is compared to the approaches of He et al. (2004) and Marti et al. (2001) using their own datasets. Furthermore an unsupervised method is implemented in order to evaluate the advantages and disadvantages of our supervised classification approach versus an unsupervised one

Unsupervised classification of remote sensing images combining Self Organizing Maps and segmentation techniques

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Weight loss strategies, stress, and cognitive function:supervised versus unsupervised dieting

The early stages of dieting to lose weight have been associated with neuro-psychological impairments. Previous work has not elucidated whether these impairments are a function solely of unsupported or supported dieting. Raised cortico-steroid levels have been implicated as a possible causal mechanism. Healthy, overweight, pre-menopausal women were randomised to one of three conditions in which they dieted either as part of a commercially available weight loss group, dieted without any group support or acted as non-dieting controls for 8 weeks. Testing occurred at baseline and at 1, 4 and 8 weeks post baseline. During each session, participants completed measures of simple reaction time, motor speed, vigilance, immediate verbal recall, visuo-spatial processing and (at Week 1 only) executive function. Cortisol levels were gathered at the beginning and 30 min into each test session, via saliva samples. Also, food intake was self-recorded prior to each session and fasting body weight and percentage body fat were measured at each session. Participants in the unsupported diet condition displayed poorer vigilance performance (p=0.001) and impaired executive planning function (p=0.013) (along with a marginally significant trend for poorer visual recall (p=0.089)) after 1 week of dieting. No such impairments were observed in the other two groups. In addition, the unsupported dieters experienced a significant rise in salivary cortisol levels after 1 week of dieting (p<0.001). Both dieting groups lost roughly the same amount of body mass (p=0.011) over the course of the 8 weeks of dieting, although only the unsupported dieters experienced a significant drop in percentage body fat over the course of dieting (p=0.016). The precise causal nature of the relationship between stress, cortisol, unsupported dieting and cognitive function is, however, uncertain and should be the focus of further research. © 2005 Elsevier Ltd. All rights reserved.

Machine Learning and Clinical Text. Supporting Health Information Flow

Fluent health information flow is critical for clinical decision-making. However, a considerable part of this information is free-form text and inabilities to utilize it create risks to patient safety and cost-­effective hospital administration. Methods for automated processing of clinical text are emerging. The aim in this doctoral dissertation is to study machine learning and clinical text in order to support health information flow.First, by analyzing the content of authentic patient records, the aim is to specify clinical needs in order to guide the development of machine learning applications.The contributions are a model of the ideal information flow,a model of the problems and challenges in reality, and a road map for the technology development. Second, by developing applications for practical cases,the aim is to concretize ways to support health information flow. Altogether five machine learning applications for three practical cases are described: The first two applications are binary classification and regression related to the practical case of topic labeling and relevance ranking.The third and fourth application are supervised and unsupervised multi-class classification for the practical case of topic segmentation and labeling.These four applications are tested with Finnish intensive care patient records.The fifth application is multi-label classification for the practical task of diagnosis coding. It is tested with English radiology reports.The performance of all these applications is promising. Third, the aim is to study how the quality of machine learning applications can be reliably evaluated.The associations between performance evaluation measures and methods are addressed,and a new hold-out method is introduced.This method contributes not only to processing time but also to the evaluation diversity and quality. The main conclusion is that developing machine learning applications for text requires interdisciplinary, international collaboration. Practical cases are very different, and hence the development must begin from genuine user needs and domain expertise. The technological expertise must cover linguistics,machine learning, and information systems. Finally, the methods must be evaluated both statistically and through authentic user-feedback.

Identification of croplands of winter cereals in Rio Grande do Sul state, Brazil, through unsupervised classification of normalized difference vegetation index images

This study aimed to propose methods to identify croplands cultivated with winter cereals in the northern region of Rio Grande do Sul State, Brazil. Thus, temporal profiles of Normalized Difference Vegetation Index (NDVI) from MODIS sensor, from April to December of the 2000 to 2008, were analyzed. Firstly, crop masks were elaborated by subtracting the minimum NDVI image (April to May) from the maximum NDVI image (June to October). Then, an unsupervised classification of NDVI images was carried out (Isodata), considering the crop mask areas. According to the results, crop masks allowed the identification of pixels with greatest green biomass variation. This variation might be associated or not with winter cereals areas established to grain production. The unsupervised classification generated classes in which NDVI temporal profiles were associated with water bodies, pastures, winter cereals for grain production and for soil cover. Temporal NDVI profiles of the class winter cereals for grain production were in agree with crop patterns in the region (developmental stage, management standard and sowing dates). Therefore, unsupervised classification based on crop masks allows distinguishing and monitoring winter cereal crops, which were similar in terms of morphology and phenology.

Toward Satellite-Based Land Cover Classification Through Optimum-Path Forest

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Estimation of croplands using indicator kriging and fuzzy classification

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Efficient Image and Video Representations for Retrieval

Image (Video) retrieval is an interesting problem of retrieving images (videos) similar to the query. Images (Videos) are represented in an input (feature) space and similar images (videos) are obtained by finding nearest neighbors in the input representation space. Numerous input representations both in real valued and binary space have been proposed for conducting faster retrieval. In this thesis, we present techniques that obtain improved input representations for retrieval in both supervised and unsupervised settings for images and videos. Supervised retrieval is a well known problem of retrieving same class images of the query. We address the practical aspects of achieving faster retrieval with binary codes as input representations for the supervised setting in the first part, where binary codes are used as addresses into hash tables. In practice, using binary codes as addresses does not guarantee fast retrieval, as similar images are not mapped to the same binary code (address). We address this problem by presenting an efficient supervised hashing (binary encoding) method that aims to explicitly map all the images of the same class ideally to a unique binary code. We refer to the binary codes of the images as `Semantic Binary Codes' and the unique code for all same class images as `Class Binary Code'. We also propose a new class­ based Hamming metric that dramatically reduces the retrieval times for larger databases, where only hamming distance is computed to the class binary codes. We also propose a Deep semantic binary code model, by replacing the output layer of a popular convolutional Neural Network (AlexNet) with the class binary codes and show that the hashing functions learned in this way outperforms the state­ of ­the art, and at the same time provide fast retrieval times. In the second part, we also address the problem of supervised retrieval by taking into account the relationship between classes. For a given query image, we want to retrieve images that preserve the relative order i.e. we want to retrieve all same class images first and then, the related classes images before different class images. We learn such relationship aware binary codes by minimizing the similarity between inner product of the binary codes and the similarity between the classes. We calculate the similarity between classes using output embedding vectors, which are vector representations of classes. Our method deviates from the other supervised binary encoding schemes as it is the first to use output embeddings for learning hashing functions. We also introduce new performance metrics that take into account the related class retrieval results and show significant gains over the state­ of­ the art. High Dimensional descriptors like Fisher Vectors or Vector of Locally Aggregated Descriptors have shown to improve the performance of many computer vision applications including retrieval. In the third part, we will discuss an unsupervised technique for compressing high dimensional vectors into high dimensional binary codes, to reduce storage complexity. In this approach, we deviate from adopting traditional hyperplane hashing functions and instead learn hyperspherical hashing functions. The proposed method overcomes the computational challenges of directly applying the spherical hashing algorithm that is intractable for compressing high dimensional vectors. A practical hierarchical model that utilizes divide and conquer techniques using the Random Select and Adjust (RSA) procedure to compress such high dimensional vectors is presented. We show that our proposed high dimensional binary codes outperform the binary codes obtained using traditional hyperplane methods for higher compression ratios. In the last part of the thesis, we propose a retrieval based solution to the Zero shot event classification problem - a setting where no training videos are available for the event. To do this, we learn a generic set of concept detectors and represent both videos and query events in the concept space. We then compute similarity between the query event and the video in the concept space and videos similar to the query event are classified as the videos belonging to the event. We show that we significantly boost the performance using concept features from other modalities.