On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery

Abstract

With the development of remote-sensing technology, optical remote-sensing imagery processing has played an important role in many application fields, such as geological exploration and natural disaster prevention. However, relative radiation correction and geometric correction are key steps in preprocessing because raw image data without preprocessing will cause poor performance during application. Traditionally, remote-sensing data are downlinked to the ground station, preprocessed, and distributed to users. This process generates long delays, which is a major bottleneck in real-time applications for remote-sensing data. Therefore, on-board, real-time image preprocessing is greatly desired. In this paper, a real-time processing architecture for on-board imagery preprocessing is proposed. First, a hierarchical optimization and mapping method is proposed to realize the preprocessing algorithm in a hardware structure, which can effectively reduce the computation burden of on-board processing. Second, a co-processing system using a field-programmable gate array (FPGA) and a digital signal processor (DSP; altogether, FPGA-DSP) based on optimization is designed to realize real-time preprocessing. The experimental results demonstrate the potential application of our system to an on-board processor, for which resources and power consumption are limited.

Real-Time Corrections for a Low-Cost Hyperspectral Instrument

“…The processing time is comparable in order of magnitude to the time reported in [12], in which complete radiometric and geometric corrections are achieved in 0.445 ms/MB (0.89 s for a 2 GB frame). The procedure in [12] is optimized for a field-programmable gate array (FPGA) with a digital signal processor and it is focused on geo-referencing rather than calibration. Real-time calibration has also been demonstrated by [13] for airborne hyperspectral imagery campaigns correcting for spectral shifts, keystone, and smile.…”

Inference in Supervised Spectral Classifiers for On-Board Hyperspectral Imaging: An Overview

“…As a result, low-power consumption architectures such as field-programmable gate array (FPGAs) [125,126] and efficient GPU architectures [110] have emerged as an alternative to transfer part of the processing from the ground segment to the remote sensing sensor. A variety of techniques have been adapted to be carried out on-board [127], ranging from pre-processing methods, such as data calibration [128], correction [129], compression [123,130] and georeferencing [131], to final user applications, for instance data unmixing [126], object detection [132] and classification [110,133]. In the context of classification, usually, the training of supervised methods should be performed offline (in external systems), so that only the trained model will be implemented in the device (which will only perform the inference operation).…”

Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing

“…Accordingly, many applications for autonomous vehicles have emerged, and can be classified into safety applications (e.g., localization and navigation, obstacle detection, accident avoidance, remote-sensing, etc.) [7][8][9][10] and non-safety applications (e.g., media sharing, infotainment, file transfer, gaming, etc.) [11][12][13].…”