High-performance time-series quantitative retrieval from satellite images on a GPU cluster

The quality and accuracy of remote sensing instruments continue to increase, allowing geoscientists to perform various quantitative retrieval applications to observe the geophysical variables of land, atmosphere, ocean, etc. The explosive growth of time-series remote sensing (RS) data over large-scales poses great challenges on managing, processing, and interpreting RS ‘‘Big Data.’’ To explore these time-series RS data efficiently, in this paper, we design and implement a high-performance framework to address the time-consuming time-series quantitative retrieval issue on a graphics processing unit cluster, taking the aerosol optical depth (AOD) retrieval from satellite images as a study case. The presented framework exploits the multilevel parallelism for time-series quantitative RS retrieval to promote efficiency. At the coarse-grained level of parallelism, the AOD time-series retrieval is represented as multidirected acyclic graph workflows and scheduled based on a list-based heuristic algorithm, heterogeneous earliest finish time, taking the idle slot and priorities of retrieval jobs into account. At the fine-grained level, the parallel strategies for the major remote sensing image processing algorithms divided into three categories, i.e., the point or pixel-based operations, the local operations, and the global or irregular operations have been summarized. The parallel framework was implemented with message passing interface and compute unified device architecture, and experimental results with the AOD retrieval case verify the effectiveness of the presented framework.N/

Generación de una librería RVC – CAL para la etapa de determinación de endmembers en el proceso de análisis de imágenes hiperespectrales

El análisis de imágenes hiperespectrales permite obtener información con una gran resolución espectral: cientos de bandas repartidas desde el espectro infrarrojo hasta el ultravioleta. El uso de dichas imágenes está teniendo un gran impacto en el campo de la medicina y, en concreto, destaca su utilización en la detección de distintos tipos de cáncer. Dentro de este campo, uno de los principales problemas que existen actualmente es el análisis de dichas imágenes en tiempo real ya que, debido al gran volumen de datos que componen estas imágenes, la capacidad de cómputo requerida es muy elevada. Una de las principales líneas de investigación acerca de la reducción de dicho tiempo de procesado se basa en la idea de repartir su análisis en diversos núcleos trabajando en paralelo. En relación a esta línea de investigación, en el presente trabajo se desarrolla una librería para el lenguaje RVC – CAL – lenguaje que está especialmente pensado para aplicaciones multimedia y que permite realizar la paralelización de una manera intuitiva – donde se recogen las funciones necesarias para implementar dos de las cuatro fases propias del procesado espectral: reducción dimensional y extracción de endmembers. Cabe mencionar que este trabajo se complementa con el realizado por Raquel Lazcano en su Proyecto Fin de Grado, donde se desarrollan las funciones necesarias para completar las otras dos fases necesarias en la cadena de desmezclado. En concreto, este trabajo se encuentra dividido en varias partes. La primera de ellas expone razonadamente los motivos que han llevado a comenzar este Proyecto Fin de Grado y los objetivos que se pretenden conseguir con él. Tras esto, se hace un amplio estudio del estado del arte actual y, en él, se explican tanto las imágenes hiperespectrales como los medios y las plataformas que servirán para realizar la división en núcleos y detectar las distintas problemáticas con las que nos podamos encontrar al realizar dicha división. Una vez expuesta la base teórica, nos centraremos en la explicación del método seguido para componer la cadena de desmezclado y generar la librería; un punto importante en este apartado es la utilización de librerías especializadas en operaciones matriciales complejas, implementadas en C++. Tras explicar el método utilizado, se exponen los resultados obtenidos primero por etapas y, posteriormente, con la cadena de procesado completa, implementada en uno o varios núcleos. Por último, se aportan una serie de conclusiones obtenidas tras analizar los distintos algoritmos en cuanto a bondad de resultados, tiempos de procesado y consumo de recursos y se proponen una serie de posibles líneas de actuación futuras relacionadas con dichos resultados. ABSTRACT. Hyperspectral imaging allows us to collect high resolution spectral information: hundred of bands covering from infrared to ultraviolet spectrum. These images have had strong repercussions in the medical field; in particular, we must highlight its use in cancer detection. In this field, the main problem we have to deal with is the real time analysis, because these images have a great data volume and they require a high computational power. One of the main research lines that deals with this problem is related with the analysis of these images using several cores working at the same time. According to this investigation line, this document describes the development of a RVC – CAL library – this language has been widely used for working with multimedia applications and allows an optimized system parallelization –, which joins all the functions needed to implement two of the four stages of the hyperspectral imaging processing chain: dimensionality reduction and endmember extraction. This research is complemented with the research conducted by Raquel Lazcano in her Diploma Project, where she studies the other two stages of the processing chain. The document is divided in several chapters. The first of them introduces the motivation of the Diploma Project and the main objectives to achieve. After that, we study the state of the art of some technologies related with this work, like hyperspectral images and the software and hardware that we will use to parallelize the system and to analyze its performance. Once we have exposed the theoretical bases, we will explain the followed methodology to compose the processing chain and to generate the library; one of the most important issues in this chapter is the use of some C++ libraries specialized in complex matrix operations. At this point, we will expose the results obtained in the individual stage analysis and then, the results of the full processing chain implemented in one or several cores. Finally, we will extract some conclusions related with algorithm behavior, time processing and system performance. In the same way, we propose some future research lines according to the results obtained in this documen

IEEE Geoscience and Remote Sensing Letters: Vol. 10, No. 2. March 2013

1. One-Stationary Bistatic Side-Looking SAR Imaging Algorithm Based on Extended Keystone Transforms and Nonlinear Chirp Scaling / Zhongyu Li, et al 2. Supervised Graph Embedding for Polarimetric SAR Image Classification / Lei Shi, et al. 3. GPU Implementation of an Automatic Target Detection and Classification Algorithm for Hyperspectral Image Analysis / Sergio Bernabe, et al. 4. MUSIC Algorithms for Grid Diagnostics / Raffaele Solimene, Giovanni Leone 5. Extraction of Underwater Laver Cultivation Nets by SAR Polarimetric Entropy / Eun-Sung Won, Kazuo Ouchi, Chan-Su Yang 6. A Semisupervised Context-Sensitive Change Detection Technique via Gaussian Process / Keming Chen, et al. 7. Ground Moving Target Indication in SAR Images by Symetric Defocusing / Gaohuan Lv, Junfeng Wang, Xingzhao Liu 8. Some Statistical Properties of Surface Slopes via Remote Sensing Using Variable Reflection Angle Considering a Non-Gaussian Probability Density Function / Josue Alvarez-Borrego, Beatriz Martin-Atienza 9. Beam Position Agility in VPRF for Spaceborne Precipitation Radar / Danru Yu, Chonghui Zhao, Wei Xiang 10. Intercomparison of OSCAT Winds With Numerical-Model-Generaed Winds / Abhisek Chakraborty, et al. 11. SAR Image Despeckling Using a Space-Domain Filter with Alterable Window / Guang-Ting Li, et al. 12. SAR Target Configuration Recognition Using Locality Preserving Property and Gaussian Mixture Distribution / Ming Liu, et al. 13. Deorientation Effecr Investigation for Model-Based Decomposition Over Oriented Built-Up Areas / Si-Wei Chen, et al. 14. A New Method of Removing Unsyncronized Signal for ScanSAR Interferometry / Cunren Liang, et al. 15. Regional Algorithms for European Seas: a case study based on MERIS data / Tamito Kajiyama, Davide D\u27 Alimonte, Giuseppe Zibordi 16. A Multispectral Decomposition Technique for the Recovery of True SeaWiFS Top-of-Atmosphere Radiances / Wei Chen, Bo-Cai Gao 17. Automatic Generation of Srandard Deviation Attribute Profiles for Spectral-Spatial Classification of Remote Sensing Data / Prashanth R. Marpu, et al. 18. Interactive Segmentation for Change Detection in Multispectral Remote-Sensing Images / Haikel Hichri, et al. 19. A Comparison Between Coherent and Incoherent Similarity Measures in Terms of Crop Inventory / Olga Chesnokova, Esra Erten 20. Fast Filtering of LiDAR Point Cloud in rban Areas Based on Scan Line Segmentation and GPU Acceleration / Xiangyun Hu, Xiaokai Li, Yongjun Zhang etc