Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin

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

Sparse Coding Based Feature Representation Method for Remote Sensing Images

In this dissertation, we study sparse coding based feature representation method for the classification of multispectral and hyperspectral images (HSI). The existing feature representation systems based on the sparse signal model are computationally expensive, requiring to solve a convex optimization problem to learn a dictionary. A sparse coding feature representation framework for the classification of HSI is presented that alleviates the complexity of sparse coding through sub-band construction, dictionary learning, and encoding steps. In the framework, we construct the dictionary based upon the extracted sub-bands from the spectral representation of a pixel. In the encoding step, we utilize a soft threshold function to obtain sparse feature representations for HSI. Experimental results showed that a randomly selected dictionary could be as effective as a dictionary learned from optimization. The new representation usually has a very high dimensionality requiring a lot of computational resources. In addition, the spatial information of the HSI data has not been included in the representation. Thus, we modify the framework by incorporating the spatial information of the HSI pixels and reducing the dimension of the new sparse representations. The enhanced model, called sparse coding based dense feature representation (SC-DFR), is integrated with a linear support vector machine (SVM) and a composite kernels SVM (CKSVM) classifiers to discriminate different types of land cover. We evaluated the proposed algorithm on three well known HSI datasets and compared our method to four recently developed classification methods: SVM, CKSVM, simultaneous orthogonal matching pursuit (SOMP) and image fusion and recursive filtering (IFRF). The results from the experiments showed that the proposed method can achieve better overall and average classification accuracies with a much more compact representation leading to more efficient sparse models for HSI classification. To further verify the power of the new feature representation method, we applied it to a pan-sharpened image to detect seafloor scars in shallow waters. Propeller scars are formed when boat propellers strike and break apart seagrass beds, resulting in habitat loss. We developed a robust identification system by incorporating morphological filters to detect and map the scars. Our results showed that the proposed method can be implemented on a regular basis to monitor changes in habitat characteristics of coastal waters

Sparse representation based hyperspectral image compression and classification

Abstract This thesis presents a research work on applying sparse representation to lossy hyperspectral image compression and hyperspectral image classification. The proposed lossy hyperspectral image compression framework introduces two types of dictionaries distinguished by the terms sparse representation spectral dictionary (SRSD) and multi-scale spectral dictionary (MSSD), respectively. The former is learnt in the spectral domain to exploit the spectral correlations, and the latter in wavelet multi-scale spectral domain to exploit both spatial and spectral correlations in hyperspectral images. To alleviate the computational demand of dictionary learning, either a base dictionary trained offline or an update of the base dictionary is employed in the compression framework. The proposed compression method is evaluated in terms of different objective metrics, and compared to selected state-of-the-art hyperspectral image compression schemes, including JPEG 2000. The numerical results demonstrate the effectiveness and competitiveness of both SRSD and MSSD approaches. For the proposed hyperspectral image classification method, we utilize the sparse coefficients for training support vector machine (SVM) and k-nearest neighbour (kNN) classifiers. In particular, the discriminative character of the sparse coefficients is enhanced by incorporating contextual information using local mean filters. The classification performance is evaluated and compared to a number of similar or representative methods. The results show that our approach could outperform other approaches based on SVM or sparse representation. This thesis makes the following contributions. It provides a relatively thorough investigation of applying sparse representation to lossy hyperspectral image compression. Specifically, it reveals the effectiveness of sparse representation for the exploitation of spectral correlations in hyperspectral images. In addition, we have shown that the discriminative character of sparse coefficients can lead to superior performance in hyperspectral image classification.EM201

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

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Técnicas de compresión de imágenes hiperespectrales sobre hardware reconfigurable

Tesis de la Universidad Complutense de Madrid, Facultad de Informática, leída el 18-12-2020Sensors are nowadays in all aspects of human life. When possible, sensors are used remotely. This is less intrusive, avoids interferces in the measuring process, and more convenient for the scientist. One of the most recurrent concerns in the last decades has been sustainability of the planet, and how the changes it is facing can be monitored. Remote sensing of the earth has seen an explosion in activity, with satellites now being launched on a weekly basis to perform remote analysis of the earth, and planes surveying vast areas for closer analysis...Los sensores aparecen hoy en día en todos los aspectos de nuestra vida. Cuando es posible, de manera remota. Esto es menos intrusivo, evita interferencias en el proceso de medida, y además facilita el trabajo científico. Una de las preocupaciones recurrentes en las últimas décadas ha sido la sotenibilidad del planeta, y cómo menitoirzar los cambios a los que se enfrenta. Los estudios remotos de la tierra han visto un gran crecimiento, con satélites lanzados semanalmente para analizar la superficie, y aviones sobrevolando grades áreas para análisis más precisos...Fac. de InformáticaTRUEunpu

Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation

Remote sensing (RS) image retrieval is of great significant for geological information mining. Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues: feature extraction, similarity metric and relevance feedback. Due to the complexity and multiformity of ground objects in high-resolution remote sensing (HRRS) images, there is still room for improvement in the current retrieval approaches. In this paper, we analyze the three core issues of RS image retrieval and provide a comprehensive review on existing methods. Furthermore, for the goal to advance the state-of-the-art in HRRS image retrieval, we focus on the feature extraction issue and delve how to use powerful deep representations to address this task. We conduct systematic investigation on evaluating correlative factors that may affect the performance of deep features. By optimizing each factor, we acquire remarkable retrieval results on publicly available HRRS datasets. Finally, we explain the experimental phenomenon in detail and draw conclusions according to our analysis. Our work can serve as a guiding role for the research of content-based RS image retrieval