Evolution of real-time onboard processing and classification of remotely sensed data

Mahendra H N

doi:10.17485/ijst/v13i20.459

Abstract

Objectives: To provide a technical review of current hardware architecture, techniques, problems, and practices used for real-time on-board data processing and classification of Remotely Sensed (RS) data. Method: The major issues of data processing such as power limitation and downlink bandwidth are considered for analysis. Performance of traditional Central Processing Unit (CPU) and onboard Graphics Processing Unit (GPU), Field Programmable Gate Array (FPGA)based data processing are presented in Table 3. Different hardware architecture used for onboard data classification such as FPGA, Advanced RISC Microcontroller (ARM), and Digital Signal Processor (DSP) based system performance are reported in Tables 5 and 6 respectively. Findings: In general satellite data processing, immediate action cannot be taken against natural disasters because of the time taken in processing data at the ground station. Also the downlink bandwidth available between satellite and ground station many not be sufficient to transfer large size of data. One of the solutions to resolve this issue is to process the data onboard, so that data size will be reduced and can be downlink to the ground station for different applications such as urban planning, agriculture, defense/security purposes, biological threat detection, fire tracking on wild land, risk/hazard prevention and also helps to take immediate action during natural disasters. The existing hardware module and its architecture have been studied and concluded with a comparative result. These results aid the researchers to come up with a more optimized design and hardware architecture for data preprocessing and classification. Keywords: Remote Sensing; pre-processing; classification; field programmable gate array; digital signal processor; graphics processing unit; central processing unit

Highlights

Onboard processing of remotely sensed data has an attractive solution for reducing the time of obtaining and processing data at the ground station[1]
In [18] and [19], they have designed FPGA (Field Programmable Gate Array) based hardware architecture to classify the hyperspectral images in real-time at satellite platforms
In[18], the design of ZYNQ FPGA using Support Vector Machine (SVM) for real-time classification of Remotely Sensed (RS) data and the performance of ZYNQ is compared with the standard design

Summary

Introduction

Onboard processing of remotely sensed data has an attractive solution for reducing the time of obtaining and processing data at the ground station[1]. The traditional remote sensing will lead to several problems like the requirement of large downlink bandwidth and generates long delays This procedure cannot apply where we need real-time results. The onboard processing can be done in two ways: 1) Satellite data is pre-processed to correct the radiometric, geometric and atmospheric correction, downlink to ground station for further processing, 2) Satellite data is pre-processed and processed as per the requirement of application such as classification, downlink the results to ground station for decision making.

Data Pre-Processing

Analysis of Remote Sensing Image Preprocessing

Classification of Remotely Sensed Data

Analysis of Real -Time Classification of Remotely Sensed Data

Findings

Conclusion