ISODATA Classification

Resources created at the University of Southampton for the module Remote Sensing for Earth Observation

Cartografia de ocupação do solo com imagens MERIS: uma abordagem exploratória com classificação não assistida

Dissertação apresentada como requisito parcial para a obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

Wetland Habitat Studies using various Classification Techniques on Multi-Spectral Landsat Imagery: Case study: Tram chim National Park, Dong Thap Vietnam

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

Abordagem metodológica baseada em imagens do SAR-R99B para identificar prováveis pistas de pouso não-homologadas na Amazônia

Este estudo apresenta uma abordagem metodológica baseada em imagens de radar e nos critérios de tonalidade, tamanho e forma geométrica para identificar prováveis pistas de pouso não-homologadas na Amazônia. Os seguintes procedimentos foram conduzidos: georreferenciamento da imagem do sensor SAR-R99B do município paraense de Itaituba, adquirida na banda L, polarização HH e resolução espacial de três metros; subtração do ruído speckle com filtro mediana; classificação com a técnica não-supervisionada ISODATA; vetorização da classe indicativa dos alvos de interesse; e cálculo e seleção automática dos alvos de interesse por critérios de índice de circularidade e de tortuosidade. Foram identificados dez alvos, dos quais dois foram considerados como prováveis pistas de pouso pelos referidos índices.

Identification of different heart tissues from MRI C-SENC images using an unsupervised multi-stage fuzzy clustering technique.

PURPOSE: To objectively characterize different heart tissues from functional and viability images provided by composite-strain-encoding (C-SENC) MRI. MATERIALS AND METHODS: C-SENC is a new MRI technique for simultaneously acquiring cardiac functional and viability images. In this work, an unsupervised multi-stage fuzzy clustering method is proposed to identify different heart tissues in the C-SENC images. The method is based on sequential application of the fuzzy c-means (FCM) and iterative self-organizing data (ISODATA) clustering algorithms. The proposed method is tested on simulated heart images and on images from nine patients with and without myocardial infarction (MI). The resulting clustered images are compared with MRI delayed-enhancement (DE) viability images for determining MI. Also, Bland-Altman analysis is conducted between the two methods. RESULTS: Normal myocardium, infarcted myocardium, and blood are correctly identified using the proposed method. The clustered images correctly identified 90 +/- 4% of the pixels defined as infarct in the DE images. In addition, 89 +/- 5% of the pixels defined as infarct in the clustered images were also defined as infarct in DE images. The Bland-Altman results show no bias between the two methods in identifying MI. CONCLUSION: The proposed technique allows for objectively identifying divergent heart tissues, which would be potentially important for clinical decision-making in patients with MI.

Analysis of vineyard differential management zones and relation to vine development, grape maturity and quality

The objective of research was to analyse the potential of Normalized Difference Vegetation Index (NDVI) maps from satellite images, yield maps and grapevine fertility and load variables to delineate zones with different wine grape properties for selective harvesting. Two vineyard blocks located in NE Spain (Cabernet Sauvignon and Syrah) were analysed. The NDVI was computed from a Quickbird-2 multi-spectral image at veraison (July 2005). Yield data was acquired by means of a yield monitor during September 2005. Other variables, such as the number of buds, number of shoots, number of wine grape clusters and weight of 100 berries were sampled in a 10 rows × 5 vines pattern and used as input variables, in combination with the NDVI, to define the clusters as alternative to yield maps. Two days prior to the harvesting, grape samples were taken. The analysed variables were probable alcoholic degree, pH of the juice, total acidity, total phenolics, colour, anthocyanins and tannins. The input variables, alone or in combination, were clustered (2 and 3 Clusters) by using the ISODATA algorithm, and an analysis of variance and a multiple rang test were performed. The results show that the zones derived from the NDVI maps are more effective to differentiate grape maturity and quality variables than the zones derived from the yield maps. The inclusion of other grapevine fertility and load variables did not improve the results.

Protocolo para la zonificación intraparcelaria de la viña para vendimia selectiva a partir de imágenes multiespectratrales

En este trabajo se presenta un protocolo para la zonificación intraparcelaria de la viña con la finalidad de vendimia selectiva. Se basa en la adquisición de una imagen multiespectral detallada en el momento del envero, a partir de la cual se obtiene el índice de vegetación de la diferencia normalizada (NDVI). Este índice se clasifica en áreas de vigor alto y bajo mediante un proceso de clasificación no supervisada (algoritmo ISODATA). Las zonas resultantes se generalizan y se transfieren al monitor de cosecha de una máquina vendimiadora para realizar la recolección selectiva. La uva recolectada según este protocolo en parcelas control ha mostrado diferenciación en cuanto a parámetros de calidad como el pH, la acidez total, el contenido de polifenoles y el color. La imagen multiespectral utilizada fue adquirida por el satélite Quickbird-2. Los datos de calidad de la uva fueron muestreados según una malla regular de 5 filas por 10 cepas, procediendo a un test estadístico de rangos múltiples para analizar la separación de medias de las variables analizadas en cada zona de NDVI.

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.

Identificação de hidroformas e hidromorfologias em zonas Costeiras arenosas utilizando técnicas de classificação de Imagem

A aplicação de metodologias inovadoras no estudo da zona costeira, como as Técnicas de Informação Geográfica (TIG), utilizando fotografia aérea e imagens de satélite de alta resolução espacial, é um assunto proeminente da investigação das áreas das Ciências Geo-Espaciais e da Engenharia Costeira. Um conjunto de fotografias aéreas, entre 1958 e 2002, foi analisado visualmente num ambiente de Sistemas de Informação Geográfica (SIG), com o objectivo de identificar hidroformas e hidromorfologias costeiras, no sector entre Esmoriz e Mira. Este trabalho tem como objectivo principal identificar e analisar formas/padrões morfológicas e hidrodinâmicos (hidroformas e hidromorfologias) recorrendo a algoritmos da classificação de imagem. Para alcançar esse objectivo foram aplicados diferentes métodos de classificação de imagem, nomeadamente técnicas de classificação supervisionada e não supervisionada, utilizando o software PCI Geomatica®. Foram testados diferentes algoritmos na classificação supervisionada, (paralelepípedo, distância mínima e máxima probabilidade) e na classificação não supervisionada, o K-médias e o ISODATA. Os algoritmos de classificação supervisionada apresentaram bons resultados, demonstrados pela precisão global e coeficiente Kappa, de 95.65% - 0.95661 e de 95.85% - 0.95840, para o método do paralelepípedo e para o método da máxima probabilidade respectivamente. Os algoritmos de classificação não supervisionada (K-médias e ISODATA) permitiram identificar várias classes, como por exemplo, praia, face da praia e zona de rebentação. Os resultados obtidos foram comparados (sobrepostos) com os da análise visual em ambiente SIG, mostrando uma boa concordância nas hidroformas e hidromorfologias identificadas.

Aplicação do Sensoriamento Remoto e do Sistema de Informações Geográficas na Detecção de Manchas de Óleo na Região do Pólo de Exploração de Guamaré, R.N.

Objective to establish a methodology for the oil spill monitoring on the sea surface, located at the Submerged Exploration Area of the Polo Region of Guamaré, in the State of Rio Grande do Norte, using orbital images of Synthetic Aperture Radar (SAR integrated with meteoceanographycs products. This methodology was applied in the following stages: (1) the creation of a base map of the Exploration Area; (2) the processing of NOAA/AVHRR and ERS-2 images for generation of meteoceanographycs products; (3) the processing of RADARSAT-1 images for monitoring of oil spills; (4) the integration of RADARSAT-1 images with NOAA/AVHRR and ERS-2 image products; and (5) the structuring of a data base. The Integration of RADARSAT-1 image of the Potiguar Basin of day 21.05.99 with the base map of the Exploration Area of the Polo Region of Guamaré for the identification of the probable sources of the oil spots, was used successfully in the detention of the probable spot of oil detected next to the exit to the submarine emissary in the Exploration Area of the Polo Region of Guamaré. To support the integration of RADARSAT-1 images with NOAA/AVHRR and ERS-2 image products, a methodology was developed for the classification of oil spills identified by RADARSAT-1 images. For this, the following algorithms of classification not supervised were tested: K-means, Fuzzy k-means and Isodata. These algorithms are part of the PCI Geomatics software, which was used for the filtering of RADARSAT-1 images. For validation of the results, the oil spills submitted to the unsupervised classification were compared to the results of the Semivariogram Textural Classifier (STC). The mentioned classifier was developed especially for oil spill classification purposes and requires PCI software for the whole processing of RADARSAT-1 images. After all, the results of the classifications were analyzed through Visual Analysis; Calculation of Proportionality of Largeness and Analysis Statistics. Amongst the three algorithms of classifications tested, it was noted that there were no significant alterations in relation to the spills classified with the STC, in all of the analyses taken into consideration. Therefore, considering all the procedures, it has been shown that the described methodology can be successfully applied using the unsupervised classifiers tested, resulting in a decrease of time in the identification and classification processing of oil spills, if compared with the utilization of the STC classifier

Trajetórias tecnológicas e sistemas agrários: qualificação territorial utilizando geoprocessamento em Paragominas e Santarém/PA

A Amazônia tem sido observada principalmente por meio do fenômeno do desmatamento, utilizando recursos tradicionais do sensoriamento remoto como a quantificação de área desflorestada e seu posterior incremento anual, que parece se constituir em uma metodologia eficaz. Ratificando este raciocínio, constatei num levantamento de 16.591 multas aplicadas pelo IBAMA/PA no período de 2000 até 2008, que mais de 85,0% das autuações estavam relacionadas apenas à componente flora;e na jurisdição da Gerência do IBAMA em Santarém, oeste do Pará, em 2008, quase 60% das multas se deu por conta de desflorestamento, identificados via sensoriamento remoto. Ressalta-se que as análises de imagens de satélites por si só não definem elementos da superfície terrestre, contribuindo pouco para o entendimento e posterior intervenção da realidade. Nesse contexto, foram investigados e vetorizados 479 estabelecimentos rurais nas regiões de Paragominas e Santarém, estado do Pará, que possuem históricos de uso e ocupação distintos, qualificando-os segundo suas trajetórias tecnológicas prevalentes, na perspectiva apresentada por Costa, concretizando um importante passo para correção das distorções no desenvolvimento econômico, agregando informação ao dado de sensoriamento remoto. Aplicaram-se recursos geotecnológicos de métricas de paisagem, construíram-se banco de dados celular integrado com estatísticas e algoritmos de otimização probabilística, associando dados de classificação não supervisionada isodata (validadas com kappa= 0,87, classificação considerada“excelente”) com os tipos de produção coletados em campo, gerando uma classificação final "integrada"(kappa= 0,78, classificação “muito boa”). Na região de Paragominas, foram qualificadas 3 tipos de trajetórias tecnológicas, a camponesa T8 (domínio de culturas temporárias), a camponesa T3 e patronal T4 (especializadas em pecuária de corte). Em Santarém, revelaram-se 2 trajetórias, a camponesa T2 (forte presença de culturas permanentes, temporárias e sistemas agro florestais) e a patronal T7 (mutação da T4, aumentando a participação das culturas temporárias). A metodologia aplicada logrou êxito, espacializando as propriedades rurais segundo seus tipos de trajetórias tecnológicas e gerando classes de uso mais representativas, como cultura temporária e pastagem, mas que na classificação via sensoriamento remoto isodata é englobada pela classe “agropecuária”, possibilitando uma visão mais realista das atividades de produção desenvolvidas na área investigada, concretizando a geração de informações espaciais integrando dados de diferentes fontes e o aumento do poder de leitura do pixel.

A new information fusion method for land-use classification using high resolution satellite imagery

Satellite image classification involves designing and developing efficient image classifiers. With satellite image data and image analysis methods multiplying rapidly, selecting the right mix of data sources and data analysis approaches has become critical to the generation of quality land-use maps. In this study, a new postprocessing information fusion algorithm for the extraction and representation of land-use information based on high-resolution satellite imagery is presented. This approach can produce land-use maps with sharp interregional boundaries and homogeneous regions. The proposed approach is conducted in five steps. First, a GIS layer - ATKIS data - was used to generate two coarse homogeneous regions, i.e. urban and rural areas. Second, a thematic (class) map was generated by use of a hybrid spectral classifier combining Gaussian Maximum Likelihood algorithm (GML) and ISODATA classifier. Third, a probabilistic relaxation algorithm was performed on the thematic map, resulting in a smoothed thematic map. Fourth, edge detection and edge thinning techniques were used to generate a contour map with pixel-width interclass boundaries. Fifth, the contour map was superimposed on the thematic map by use of a region-growing algorithm with the contour map and the smoothed thematic map as two constraints. For the operation of the proposed method, a software package is developed using programming language C. This software package comprises the GML algorithm, a probabilistic relaxation algorithm, TBL edge detector, an edge thresholding algorithm, a fast parallel thinning algorithm, and a region-growing information fusion algorithm. The county of Landau of the State Rheinland-Pfalz, Germany was selected as a test site. The high-resolution IRS-1C imagery was used as the principal input data.

Land use/cover change using Remote Sensing and Geographic Information Systems : Pic Macaya National Park, Haiti

A post classification change detection technique based on a hybrid classification approach (unsupervised and supervised) was applied to Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Plus (ETM+), and ASTER images acquired in 1987, 2000 and 2004 respectively to map land use/cover changes in the Pic Macaya National Park in the southern region of Haiti. Each image was classified individually into six land use/cover classes: built-up, agriculture, herbaceous, open pine forest, mixed forest, and barren land using unsupervised ISODATA and maximum likelihood supervised classifiers with the aid of field collected ground truth data collected in the field. Ground truth information, collected in the field in December 2007, and including equalized stratified random points which were visual interpreted were used to assess the accuracy of the classification results. The overall accuracy of the land classification for each image was respectively: 1987 (82%), 2000 (82%), 2004 (87%). A post classification change detection technique was used to produce change images for 1987 to 2000, 1987 to 2004, and 2000 to 2004. It was found that significant changes in the land use/cover occurred over the 17- year period. The results showed increases in built up (from 10% to 17%) and herbaceous (from 5% to 14%) areas between 1987 and 2004. The increase of herbaceous was mostly caused by the abandonment of exhausted agriculture lands. At the same time, open pine forest and mixed forest areas lost (75%) and (83%) of their area to other land use/cover types. Open pine forest (from 20% to 14%) and mixed forest (from18 to 12%) were transformed into agriculture area or barren land. This study illustrated the continuing deforestation, land degradation and soil erosion in the region, which in turn is leading to decrease in vegetative cover. The study also showed the importance of Remote Sensing (RS) and Geographic Information System (GIS) technologies to estimate timely changes in the land use/cover, and to evaluate their causes in order to design an ecological based management plan for the park.

Analysis of Thematic Classified Aerial Images Trough Multispectral and LIDAR Data

The application of thematic maps obtained through the classification of remote images needs the obtained products with an optimal accuracy. The registered images from the airplanes display a very satisfactory spatial resolution, but the classical methods of thematic classification not always give better results than when the registered data from satellite are used. In order to improve these results of classification, in this work, the LIDAR sensor data from first return (Light Detection And Ranging) registered simultaneously with the spectral sensor data from airborne are jointly used. The final results of the thematic classification of the scene object of study have been obtained, quantified and discussed with and without LIDAR data, after applying different methods: Maximum Likehood Classification, Support Vector Machine with four different functions kernel and Isodata clustering algorithm (ML, SVM-L, SVM-P, SVM-RBF, SVM-S, Isodata). The best results are obtained for SVM with Sigmoide kernel. These allow the correlation with others different physical parameters with great interest like Manning hydraulic coefficient, for their incorporation in a GIS and their application in hydraulic modeling.