Lossless compression of hyperspectral images

Band ordering and the prediction scheme are the two major aspects of hyperspectral imaging which have been studied to improve the performance of the compression system. In the prediction module, we propose spatio-spectral prediction methods. Two non-linear spectral prediction methods have been proposed in this thesis. NPHI (Non-linear Prediction for Hyperspectral Images) is based on a band look-ahead technique wherein a reference band is included in the prediction of pixels in the current band. The prediction technique estimates the variation between the contexts of the two bands to modify the weights computed in the reference band to predict the pixels in the current band. EPHI (Edge-based Prediction for Hyperspectral Images) is the modified NPHI technique wherein an edge-based analysis is used to classify the pixels into edges and non-edges in order to perform the prediction of the pixel in the current band. Three ordering methods have been proposed in this thesis. The first ordering method computes the local and global features in each band to group the bands. The bands in each group are ordered by estimating the compression ratios achieved between the entire band in the group and then ordering them using Kruskal\u27s algorithm. The other two methods of ordering compute the compression ratios between b-neighbors in performing the band ordering

Distributed Source Coding Techniques for Lossless Compression of Hyperspectral Images

This paper deals with the application of distributed source coding (DSC) theory to remote sensing image compression. Although DSC exhibits a significant potential in many application fields, up till now the results obtained on real signals fall short of the theoretical bounds, and often impose additional system-level constraints. The objective of this paper is to assess the potential of DSC for lossless image compression carried out onboard a remote platform. We first provide a brief overview of DSC of correlated information sources. We then focus on onboard lossless image compression, and apply DSC techniques in order to reduce the complexity of the onboard encoder, at the expense of the decoder's, by exploiting the correlation of different bands of a hyperspectral dataset. Specifically, we propose two different compression schemes, one based on powerful binary error-correcting codes employed as source codes, and one based on simpler multilevel coset codes. The performance of both schemes is evaluated on a few AVIRIS scenes, and is compared with other state-of-the-art 2D and 3D coders. Both schemes turn out to achieve competitive compression performance, and one of them also has reduced complexity. Based on these results, we highlight the main issues that are still to be solved to further improve the performance of DSC-based remote sensing systems

Adaptive multispectral GPU accelerated architecture for Earth Observation satellites

In recent years the growth in quantity, diversity and capability of Earth Observation (EO) satellites, has enabled increase’s in the achievable payload data dimensionality and volume. However, the lack of equivalent advancement in downlink technology has resulted in the development of an onboard data bottleneck. This bottleneck must be alleviated in order for EO satellites to continue to efficiently provide high quality and increasing quantities of payload data. This research explores the selection and implementation of state-of-the-art multidimensional image compression algorithms and proposes a new onboard data processing architecture, to help alleviate the bottleneck and increase the data throughput of the platform. The proposed new system is based upon a backplane architecture to provide scalability with different satellite platform sizes and varying mission’s objectives. The heterogeneous nature of the architecture allows benefits of both Field Programmable Gate Array (FPGA) and Graphical Processing Unit (GPU) hardware to be leveraged for maximised data processing throughput

Dual link image coding for earth observation satellites

The conventional strategy to download images captured by satellites is to compress the data on board and then transmit them via the downlink. It often happens that the capacity of the downlink is too small to accommodate all the acquired data, so the images are trimmed and/or transmitted through lossy regimes. This paper introduces a coding system that increases the amount and quality of the downloaded imaging data. The main insight of this paper is to use both the uplink and the downlink to code the images. The uplink is employed to send reference information to the satellite so that the onboard coding system can achieve higher efficiency. This reference information is computed on the ground, possibly employing extensive data and computational resources. The proposed system is called dual link image coding. As it is devised in this paper, it is suitable for Earth observation satellites with polar orbits. Experimental results obtained for data sets acquired by the Landsat 8 satellite indicate significant coding gains with respect to conventional methods

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

An Overview of Multi-Processor Approximate Message Passing

Approximate message passing (AMP) is an algorithmic framework for solving linear inverse problems from noisy measurements, with exciting applications such as reconstructing images, audio, hyper spectral images, and various other signals, including those acquired in compressive signal acquisiton systems. The growing prevalence of big data systems has increased interest in large-scale problems, which may involve huge measurement matrices that are unsuitable for conventional computing systems. To address the challenge of large-scale processing, multiprocessor (MP) versions of AMP have been developed. We provide an overview of two such MP-AMP variants. In row-MP-AMP, each computing node stores a subset of the rows of the matrix and processes corresponding measurements. In column- MP-AMP, each node stores a subset of columns, and is solely responsible for reconstructing a portion of the signal. We will discuss pros and cons of both approaches, summarize recent research results for each, and explain when each one may be a viable approach. Aspects that are highlighted include some recent results on state evolution for both MP-AMP algorithms, and the use of data compression to reduce communication in the MP network