Lossless compression of satellite multispectral and hyperspectral images

En esta tesis se presentan nuevas técnicas de compresión sin pérdida tendientes a reducir el espacio de almacenamiento requerido por imágenes satelitales. Dos tipos principales de imágenes son tratadas: multiespectrales e hiperespectrales. En el caso de imágenes multiespectrales, se desarrolló un compresor no lineal que explota tanto las correlaciones intra como interbanda presentes en la imagen. Este se basa en la transformada wavelet de enteros a enteros y se aplica sobre bloques no solapados de la imagen. Diferentes modelos para las dependencias estadísticas de los coeficientes de detalle de la transformada wavelet son propuestos y analizados. Aquellos coeficientes que se encuentran en las subbandas de detalle fino de la transformada son modelados como una combinación afín de coeficientes vecinos y coeficientes en bandas adyacentes, sujetos a que se encuentren en la misma clase. Este modelo se utiliza para generar predicciones de otros coficientes que ya fueron codificados. La información de clase se genera mediante la cuantización LloydMax, la cual también se utiliza para predecir y como contextos de condicionamiento para codificar los errores de predicción con un codificador aritmético adaptativo. Dado que el ordenamiento de las bandas también afecta la precisión de las predicciones, un nuevo mecanismo de ordenamiento es propuesto basado en los coeficientes de detalle de los últimos dos niveles de la transformada wavelet. Los resultados obtenidos superan a los de otros compresores 2D sin pérdida como PNG, JPEG-LS, SPIHT y JPEG2000, como también a otros compresores 3D como SLSQ-OPT, JPEG-LS diferencial y JPEG2000 para imágenes a color y 3D-SPIHT. El método propuesto provee acceso aleatorio a partes de la imagen, y puede aplicarse para la compresión sin pérdida de otros datos volumétricos. Para las imágenes hiperespectrales, algoritmos como LUT o LAIS-LUT que revisten el estado del arte para la compresión sin pérdida para este tipo de imágenes, explotan la alta correlación espectral de estas imágenes y utilizan tablas de lookup para generar predicciones. A pesar de ello, existen casos donde las predicciones no son buenas. En esta tesis, se propone una modificación a estos algoritmos de lookup permitiendo diferentes niveles de confianza a las tablas de lookup en base a las variaciones locales del factor de escala. Los resultados obtenidos son altamente satisfactorios y mejores a los de LUT y LAIS-LUT. Se han diseñado dos compresores sin pérdida para dos tipos de imágenes satelitales, las cuales tienen distintas propiedades, a saber, diferente resolución espectral, espacial y radiométrica, y también de diferentes correlaciones espectrales y espaciales. En cada caso, el compresor explota estas propiedades para incrementar las tasas de compresión.In this thesis, new lossless compression techniques aiming at reducing the size of storage of satellite images are presented. Two type of images are considered: multispectral and hyperspectral. For multispectral images, a nonlinear lossless compressor that exploits both intraband and interband correlations is developed. The compressor is based on a wavelet transform that maps integers into integers, applied to tiles of the image. Different models for statistical dependencies of wavelet detail coefficients are proposed and analyzed. Wavelet coefficients belonging to the fine detail subbands are successfully modelled as an affine combination of neighboring coefficients and the coefficient at the same location in the previous band, as long as all these coefficients belong to the same landscape. This model is used to predict wavelet coefficients by means of already coded coefficients. Lloyd-Max quantization is used to extract class information, which is used in the prediction and later used as a conditioning context to encode prediction errors with an adaptive arithmetic coder. The band order affects the accuracy of predictions: a new mechanism is proposed for ordering the bands, based on the wavelet detail coefficients of the 2 finest levels. The results obtained outperform 2D lossless compressors such as PNG, JPEG-LS, SPIHT and JPEG2000 and other 3D lossless compressors such as SLSQ-OPT, differential JPEG-LS, JPEG2000 for color images and 3D-SPIHT. Our method has random access capability, and can be applied for lossless compression of other kinds of volumetric data. For hyperspectral images, state-of-the-art algorithms LUT and LAIS-LUT proposed for lossless compression, exploit high spectral correlations in these images, and use lookup tables to perform predictions. However, there are cases where their predictions are not accurate. In this thesis a modification based also on look-up tables is proposed, giving these tables different degrees of confidence, based on the local variations of the scaling factor. Our results are highly satisfactory and outperform both LUT and LAIS-LUT methods. Two lossless compressors have been designed for two different kinds of satellite images having different properties, namely, different spectral resolution, spatial resolution, and bitdepth, as well as different spectral and spatial correlations. In each case, the compressor exploits these properties to increase compression ratios.Fil:Acevedo, Daniel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina

Compresión sin pérdida de imágenes satelitales multiespectrales e hiperespectrales

En esta tesis se presentan nuevas técnicas de compresión sin pérdida tendientes a reducir el espacio de almacenamiento requerido por imágenes satelitales. Dos tipos principales de imágenes son tratadas: multiespectrales e hiperespectrales. En el caso de imágenes multiespectrales, se desarrolló un compresor no lineal que explota tanto las correlaciones intra como interbanda presentes en la imagen. Este se basa en la transformada wavelet de enteros a enteros y se aplica sobre bloques no solapados de la imagen. Diferentes modelos para las dependencias estadísticas de los coeficientes de detalle de la transformada wavelet son propuestos y analizados. Aquellos coeficientes que se encuentran en las subbandas de detalle fino de la transformada son modelados como una combinación afín de coeficientes vecinos y coeficientes en bandas adyacentes, sujetos a que se encuentren en la misma clase. Este modelo se utiliza para generar predicciones de otros coficientes que ya fueron codificados. La información de clase se genera mediante la cuantización LloydMax, la cual también se utiliza para predecir y como contextos de condicionamiento para codificar los errores de predicción con un codificador aritmético adaptativo. Dado que el ordenamiento de las bandas también afecta la precisión de las predicciones, un nuevo mecanismo de ordenamiento es propuesto basado en los coeficientes de detalle de los últimos dos niveles de la transformada wavelet. Los resultados obtenidos superan a los de otros compresores 2D sin pérdida como PNG, JPEG-LS, SPIHT y JPEG2000, como también a otros compresores 3D como SLSQ-OPT, JPEG-LS diferencial y JPEG2000 para imágenes a color y 3D-SPIHT. El método propuesto provee acceso aleatorio a partes de la imagen, y puede aplicarse para la compresión sin pérdida de otros datos volumétricos. Para las imágenes hiperespectrales, algoritmos como LUT o LAIS-LUT que revisten el estado del arte para la compresión sin pérdida para este tipo de imágenes, explotan la alta correlación espectral de estas imágenes y utilizan tablas de lookup para generar predicciones. A pesar de ello, existen casos donde las predicciones no son buenas. En esta tesis, se propone una modificación a estos algoritmos de lookup permitiendo diferentes niveles de confianza a las tablas de lookup en base a las variaciones locales del factor de escala. Los resultados obtenidos son altamente satisfactorios y mejores a los de LUT y LAIS-LUT. Se han diseñado dos compresores sin pérdida para dos tipos de imágenes satelitales, las cuales tienen distintas propiedades, a saber, diferente resolución espectral, espacial y radiométrica, y también de diferentes correlaciones espectrales y espaciales. En cada caso, el compresor explota estas propiedades para incrementar las tasas de compresión

3D multiresolution statistical approaches for accelerated medical image and volume segmentation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques together or on their own to segment medical volumes, the system takes a stack of 2D slices or a full 3D volumes acquired from medical scanners as a data input. Two main approaches have been implemented in this research for segmenting medical volume which are multi-resolution analysis and statistical modeling. Multi-resolution analysis has been mainly employed in this research for extracting the features. Higher dimensions of discontinuity (line or curve singularity) have been extracted in medical images using a modified multi-resolution analysis transforms such as ridgelet and curvelet transforms. The second implemented approach in this thesis is the use of statistical modeling in medical image segmentation; Hidden Markov models have been enhanced here to segment medical slices automatically, accurately, reliably and with lossless results. But the problem with using Markov models here is the computational time which is too long. This has been addressed by using feature reduction techniques which has also been implemented in this thesis. Some feature reduction and dimensionality reduction techniques have been used to accelerate the slowest block in the proposed system. This includes Principle Components Analysis, Gaussian Pyramids and other methods. The feature reduction techniques have been employed efficiently with the 3D volume segmentation techniques such as 3D wavelet and 3D Hidden Markov models. The system has been tested and validated using several procedures starting at a comparison with the predefined results, crossing the specialists’ validations, and ending by validating the system using a survey filled by the end users explaining the techniques and the results. This concludes that Markovian models segmentation results has overcome all other techniques in most patients’ cases. Curvelet transform has been also proved promising segmentation results; the end users rate it better than Markovian models due to the long time required with Hidden Markov models

Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included

3D multiresolution statistical approaches for accelerated medical image and volume segmentation

Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques together or on their own to segment medical volumes, the system takes a stack of 2D slices or a full 3D volumes acquired from medical scanners as a data input. Two main approaches have been implemented in this research for segmenting medical volume which are multi-resolution analysis and statistical modeling. Multi-resolution analysis has been mainly employed in this research for extracting the features. Higher dimensions of discontinuity (line or curve singularity) have been extracted in medical images using a modified multi-resolution analysis transforms such as ridgelet and curvelet transforms. The second implemented approach in this thesis is the use of statistical modeling in medical image segmentation; Hidden Markov models have been enhanced here to segment medical slices automatically, accurately, reliably and with lossless results. But the problem with using Markov models here is the computational time which is too long. This has been addressed by using feature reduction techniques which has also been implemented in this thesis. Some feature reduction and dimensionality reduction techniques have been used to accelerate the slowest block in the proposed system. This includes Principle Components Analysis, Gaussian Pyramids and other methods. The feature reduction techniques have been employed efficiently with the 3D volume segmentation techniques such as 3D wavelet and 3D Hidden Markov models. The system has been tested and validated using several procedures starting at a comparison with the predefined results, crossing the specialists’ validations, and ending by validating the system using a survey filled by the end users explaining the techniques and the results. This concludes that Markovian models segmentation results has overcome all other techniques in most patients’ cases. Curvelet transform has been also proved promising segmentation results; the end users rate it better than Markovian models due to the long time required with Hidden Markov models.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Development of Some Efficient Lossless and Lossy Hybrid Image Compression Schemes

Digital imaging generates a large amount of data which needs to be compressed, without loss of relevant information, to economize storage space and allow speedy data transfer. Though both storage and transmission medium capacities have been continuously increasing over the last two decades, they dont match the present requirement. Many lossless and lossy image compression schemes exist for compression of images in space domain and transform domain. Employing more than one traditional image compression algorithms results in hybrid image compression techniques. Based on the existing schemes, novel hybrid image compression schemes are developed in this doctoral research work, to compress the images effectually maintaining the quality