FPGA Coprocessor for Accelerated Classification of Images

An effort related to that described in the preceding article focuses on developing a spaceborne processing platform for fast and accurate onboard classification of image data, a critical part of modern satellite image processing. The approach again has been to exploit the versatility of recently developed hybrid Virtex-4FX field-programmable gate array (FPGA) to run diverse science applications on embedded processors while taking advantage of the reconfigurable hardware resources of the FPGAs. In this case, the FPGA serves as a coprocessor that implements legacy C-language support-vector-machine (SVM) image-classification algorithms to detect and identify natural phenomena such as flooding, volcanic eruptions, and sea-ice break-up. The FPGA provides hardware acceleration for increased onboard processing capability than previously demonstrated in software. The original C-language program demonstrated on an imaging instrument aboard the Earth Observing-1 (EO-1) satellite implements a linear-kernel SVM algorithm for classifying parts of the images as snow, water, ice, land, or cloud or unclassified. Current onboard processors, such as on EO-1, have limited computing power, extremely limited active storage capability and are no longer considered state-of-the-art. Using commercially available software that translates C-language programs into hardware description language (HDL) files, the legacy C-language program, and two newly formulated programs for a more capable expanded-linear-kernel and a more accurate polynomial-kernel SVM algorithm, have been implemented in the Virtex-4FX FPGA. In tests, the FPGA implementations have exhibited significant speedups over conventional software implementations running on general-purpose hardware

Implementing Legacy-C Algorithms in FPGA Co-Processors for Performance Accelerated Smart Payloads

Accurate, on-board classification of instrument data is used to increase science return by autonomously identifying regions of interest for priority transmission or generating summary products to conserve transmission bandwidth. Due to on-board processing constraints, such classification has been limited to using the simplest functions on a small subset of the full instrument data. FPGA co-processor designs for SVM1 classifiers will lead to significant improvement in on-board classification capability and accuracy

Claritas rise, Mars: Pre-Tharsis magmatism?

Claritas rise is a prominent ancient (Noachian) center of tectonism identified through investigation of comprehensive paleotectonic information of the western hemisphere of Mars. This center is interpreted to be the result of magmatic-driven activity, including uplift and associated tectonism, as well as possible hydrothermal activity. Coupled with its ancient stratigraphy, high density of impact craters, and complex structure, a possible magnetic signature may indicate that it formed during an ancient period of Mars' evolution, such as when the dynamo was in operation. As Tharsis lacks magnetic signatures, Claritas rise may pre-date the development of Tharsis or mark incipient development, since some of the crustal materials underlying Tharsis and older parts of the magmatic complex, respectively, could have been highly resurfaced, destroying any remanent magnetism. Here, we detail the significant characteristics of the Claritas rise, and present a case for why it should be targeted by the Mars Odyssey, Mars Reconnaissance Orbiter, and Mars Express spacecrafts, as well as be considered as a prime target for future tier-scalable robotic reconnaissance