THE USE OF SIMILARITY IMAGES ON MULTI-SENSOR AUTOMATIC IMAGE REGISTRATION

Automatic image registration (AIR) is still a present challenge regarding remote sensing applications. Although several methods have been proposed in the last few years, geometric correction is often a time and effort consuming manual task. The only AIR method which is commonly used is the correlation-based template matching method. It usually consists on considering a window from one image and passing it throughout the other, looking for a maximum of correlation, which may be associated to the displacement between the two images. This approach leads sometimes (for example with multi-sensor image registration) to low correlation coefficient values, which do not give sufficient confidence to associate the peak of correlation to the correct displacement between the images. Furthermore, the peak of correlation is several times too flat or ambiguous, since more than one local peak may occur. Recently, we have tested a new approach, which shortly consists on the identification of a brighter diagonal on a "similarity image". The displacement of this brighter diagonal to the main diagonal corresponds to the displacement in each axis. In this work, we explored the potential of using the "similarity images" instead of the classical "similarity surface", considering both correlation coefficient and mutual information measures. Our experiments were performed on some multi-sensor pairs of images with medium (Landsat and ASTER) and high (IKONOS, ALOS-PRISM and orthophotos) spatial resolution, where a subpixel accuracy was mostly obtained. It was also shown that the application of a low-pass filtering prior to the similarity measures computation, allows for a significant increase of the similarity measures, reinforcing the strength of this methodology in multi-spectral, multi-sensor and multi-temporal situations

Multimodal Remote Sensing Image Registration with Accuracy Estimation at Local and Global Scales

This paper focuses on potential accuracy of remote sensing images registration. We investigate how this accuracy can be estimated without ground truth available and used to improve registration quality of mono- and multi-modal pair of images. At the local scale of image fragments, the Cramer-Rao lower bound (CRLB) on registration error is estimated for each local correspondence between coarsely registered pair of images. This CRLB is defined by local image texture and noise properties. Opposite to the standard approach, where registration accuracy is only evaluated at the output of the registration process, such valuable information is used by us as an additional input knowledge. It greatly helps detecting and discarding outliers and refining the estimation of geometrical transformation model parameters. Based on these ideas, a new area-based registration method called RAE (Registration with Accuracy Estimation) is proposed. In addition to its ability to automatically register very complex multimodal image pairs with high accuracy, the RAE method provides registration accuracy at the global scale as covariance matrix of estimation error of geometrical transformation model parameters or as point-wise registration Standard Deviation. This accuracy does not depend on any ground truth availability and characterizes each pair of registered images individually. Thus, the RAE method can identify image areas for which a predefined registration accuracy is guaranteed. The RAE method is proved successful with reaching subpixel accuracy while registering eight complex mono/multimodal and multitemporal image pairs including optical to optical, optical to radar, optical to Digital Elevation Model (DEM) images and DEM to radar cases. Other methods employed in comparisons fail to provide in a stable manner accurate results on the same test cases.Comment: 48 pages, 8 figures, 5 tables, 51 references Revised arguments in sections 2 and 3. Additional test cases added in Section 4; comparison with the state-of-the-art improved. References added. Conclusions unchanged. Proofrea

Espacialização e quantificação de sesquióxidos de ferro (Goethita e Hematita) em solos tropicais por meio de sensoriamento remoto hiperespectral

Dissertação (mestrado)—Universidade de Brasília, Instituto de Geociências, 2012.Com o potencial agrícola do Brasil, conhecer as características dos seus solos paraobtenção de melhores resultados é fundamental. Os latossolos são solos com boas características físicas e suas propriedades químicas podem ser corrigidas para fins agrícolas. Métodos convencionais de caracterização de solos são caros e demorados, o que abre espaço para novas técnicas, dentre elas o sensoriamento remoto. Um dos sensores utilizados para estes fins é o sensor Hyperion, hiperespectral, possuindo 242 bandas de 10nm e com 30m de resolução espacial, recobrindo a faixa de 400 a 2500nm. Tais dados devem passar por uma série de processamentos para a obtenção das informações da superfície, dentre eles a correção atmosférica. O objeto do trabalho é testar o potencial do sensor Hyperion na detecção de óxidos e hidróxidos de ferro em ambiente tropical, assim como a interferência de diferentes corretores atmosféricos nos valores obtidos. As imagens Hyperion são obtidas junto ao USGS, para obtenção dos dados de trabalho as imagens receberam três diferentes pré-processamentos, dados brutos, remoção de bandas ruidosas e destriping. Posteriormente foi escolhido um local, com duas áreas, para a coleta de amostras georreferenciadas de solo em campo e sua analise de cor pela carta de Munsell. Para a espacialização dos dados da imagem foi usado o Spectral Feature Fitting (SFF) e com base em seus dados foi calculado o índice RHGt para a imagem e para os dados de campo com base na cor das amostras. Todas as imagens foram georreferenciadas e tiveram suas stripes muito atenuadas ou suprimidas. A imagem escolhida recebeu o tratamento com FLAASH e QUAC e ao analisar os espectros se notou que o espectro da hematita gerado pelo FLAASH está deslocado em direção à feição da Goethita. A análise dos dados do SFF mostrou a presença de stripes brancas no pré-processamento destriping, o que levou à exclusão do mesmo, e analisando os restantes optou-se pelo pré-processamento de remoção de bandas ruidosas para continuação do trabalho. Calculado o índice RHGt para a cena a cena com QUAC apresentou dados consistentes com o observado em campo, possibilitando uma separação de área com diferentes concentrações minerais, com FLAASH isso não foi possível. O índice RHGt calculado com base nos dados de campo não apresentou correlação com nenhum dos índices da imagem, e nem os índices da imagem entre si. Para os dados que apresentaram dados coerentes com o observado em campo, foram calculadas as imagens fit, que indicam a probabilidade de se ter o endmember em cena, que apresentaram boa espacialização dos minerais estudados. _______________________________________________________________________________________ ABSTRACTWith the agricultural potential of Brazil, to know the characteristics of their soils for best results is critical. The Oxisols are soils with good physical and chemical properties can be corrected for agricultural purposes. Conventional methods of soil characterization are expensive and time consuming, which makes room for new techniques, among them the remote sensing. One of the sensors used for this purpose is the Hyperion, hyperspectral having bands of 10 nm and 242 30m spatial resolution, covering the range of 400 to 2500 nm. Such data must pass through a series of processes for obtaining information from the surface, including the atmospheric correction. The object of the work is to test the potential of Hyperion for the detection of oxides and hydroxides of iron in a tropical environment, as well as the interference of different brokers in the atmospheric values. The Hyperion images are obtained from the USGS, to obtain data of the images were working three different pre-processing, raw data, removal of noisy bands and destriping. Later a site was chosen, with two areas for the collection of georeferenced soil samples and their analysis in the field of color by the Munsell charts. For the spatial distribution of the image data was used the Spectral Feature Fitting (SFF) and based on your data RHGt index was calculated for the image and field data based on the color of the samples. All images were georeferenced and had their stripes much reduced or resolved. The chosen image received treatment with FLAASH and QUAC and analyze the spectra noted that the spectrum of hematite generated by FLAASH is shifted towards the feature of Goethite. Data analysis showed the presence of the SFF white stripes in preprocessing destriping, which led to the exclusion of the same, and analyzing the remaining opted for the pre-processing removal of noisy bands for further work. Calculated index RHGt to the scene to scene with QUAC presented data consistent with that observed in the field, allowing a separation area with different concentrations of minerals, with FLAASH it was not possible. The index RHGt calculated based on field data showed no correlation with any of the contents of the image, or the contents of the image together. For data that were consistent with the data observed in the field, we calculated the fit images that indicate the likelihood of having endmember in the scene, which showed good spatial distribution of the minerals studied

Urban Growth and Its Impact on Urban Heat Sink and Island Formation in the Desert City of Dubai.

The rapid pace of urban growth in Dubai has attracted the attention of economists, environmentalists and urban planners. This thesis quantifies the extent of urbanisation within the Emirate since the discovery of oil and investigates the impacts of such growth on urban temperatures. The study used remotely-sensed imagery in the absence of publicly available data on city growth and microclimate. The study used a hybrid classification method and landscape metrics to capture historical urban forms, rates and engines of growth in the Emirate. Stepwise multiple regression analysis techniques were subsequently used to investigate the relationship between the rate and form of urbanisation and the intensity of the urban heat sink between 1990 and 2011. Local Climate Zones were then developed to specifically investigate the impacts of urban geometry variables and proximity to water on both urban heat sinks during the day-time and urban heat islands during the night. The study revealed a significant increase in urban area over time (1972-2011) with accelerated phases of growth, linked to local and global economic conditions, occurring during specific periods. Physical urban growth has now outpaced population growth, indicating urban sprawl. This growth has occurred at the expense of sand and has included a significant increase in vegetation and water bodies unlike other desert cities in the Gulf region. The results demonstrated that urban growth has promoted a heat sink effect during daytime and that all urban land use types contributed to this effect. Urban albedo was not responsible for the daytime urban heat sink; other factors including the specific heat capacity of urban materials, urban geometry and proximity to the Gulf were mainly responsible. Furthermore, increases in vegetation cover and impervious surface cover over time have contributed to the daytime (morning) urban heat sink. At night-time, urban geometry and proximity to the Gulf were the major influences upon the formation of urban heat islands. This research contributes to better understanding of urbanisation in desert cities as demonstrated through Dubai, revealing previously unknown spatiotemporal variations in urban areas across the city through the use of a time-series of satellite images. The findings provide new insights into the impacts of land cover, land use, proximity to water and urban geometry on the formation of urban heat sinks and urban heat islands in the desert environment