An Efficient Implementation of Automatic Cloud Cover Assessment (ACCA) on a Reconfigurable Computer Esam El-Araby Mohamed Taher Tarek El-Ghazawi Jacqueline Le Moigne The George Washington University The George Washington University The George Washington University NASA Goddard Space Flight Center (GSFC) Washington, DC 20037 USA Washington, DC 20037 USA Washington, DC 20037 USA Greenbelt, MD 20771 USA esam@gwu.edu mtaher@gwu.edu tarek@gwu.edu lemoigne@backserv.gsfc.nasa.gov Abstract – Clouds have a critical role in many studies, e.g. weather- and climate-related studies. However, they represent a source of errors in many applications, and the presence of cloud contamination can hinder the use of satellite data. This requires a cloud detection process to mask out cloudy pixels from further processing. The trend for remote sensing satellite missions has always been towards smaller size, lower cost, more flexibility, and higher computational power. Reconfigurable Computers (RCs) combine the flexibility of traditional microprocessors with the power of Field Programmable Gate Arrays (FPGAs). Therefore, RCs are a promising candidate for on-board preprocessing. This paper presents the design and implementation of an RC-based real-time cloud detection system. We investigate the potential of using RCs for on-board preprocessing by prototyping the Landsat 7 ETM+ ACCA algorithm on one of the state-of-the art reconfigurable platforms, SRC-6E. Although a reasonable amount of investigations of the ACCA cloud detection algorithm using FPGAs has been reported in the literature, very few details/results were provided and/or limited contributions were accomplished. Our work has been proven to provide higher performance and higher detection accuracy. I. INTRODUCTION The trend for remote sensing satellite missions has always been towards smaller size, lower cost, and more flexibility. On-board processing, as a solution, permits a good utilization of expensive resources. Instead of storing and forwarding all captured images, data processing can be performed on-orbit prior to downlink resulting in the reduction of communication bandwidth as well as in simpler and faster subsequent computations to be performed on ground stations. Consequently, on-board processing can reduce the cost and the complexity of the On-The-Ground/Earth processing systems. Furthermore, it enables autonomous decisions to be taken on-board which can potentially reduce the delay between image capture, analysis and action. This leads to faster critical decisions which are crucial for future reconfigurable web sensors missions as well as planetary exploration missions. The presence of cloud contamination can hinder the use of satellite data, and this requires a cloud detection process to mask out cloudy pixels from further processing. The Landsat 7 ETM+ (Enhanced Thematic Mapper) ACCA (Automatic Cloud Cover Assessment) algorithm [1] is a compromise between the simplicity of earlier Landsat algorithms, e.g. ACCA for Landsat 4 and 5, and the complexity of later approaches such as the MODIS (Moderate Resolution Imaging Spectroradiometer) cloud mask. Reconfigurable Computers (RCs) combine the flexibility of traditional microprocessors with the power of Field Programmable Gate Arrays (FPGAs). These platforms have always been reported to outperform the conventional platforms in terms of throughput and processing power within the domain of cryptography, and image processing applications [2]. In addition, they are characterized by lower form/wrap factors compared to parallel platforms, and higher flexibility than ASIC solutions. Therefore, RCs are a promising candidate for on-board preprocessing. The SRC-6E Reconfigurable Computer is one example of this category of hybrid computers [3] and is used here for this purpose. This paper presents the design and implementation of an RC-based real-time cloud detection system. We investigate the potential of using RCs for on-board preprocessing by prototyping the Landsat 7 ETM+ ACCA algorithm on one of the state-of-the art reconfigurable platforms, SRC-6E. Although a reasonable amount of investigations of the ACCA cloud detection algorithm using FPGAs has been reported in the literature [4], [5], very few details/results were provided and/or limited contributions were accomplished. Our work is unique in providing higher performance and higher detection accuracy. II. DESCRIPTION OF THE AUTOMATIC CLOUD COVER ASSESSMENT (ACCA) ALGORITHM