Remote Sensing Data Intelligent Interpretation Task Scheduling Algorithm Based on Heterogeneous Platform

HAO Lijiang; TIAN Luyun; SUN Peng; CHEN Jian; LIU Pengying; HE Guangjun; LOU Shuqin

doi:10.11999/JEIT251072

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HAO Lijiang, TIAN Luyun, SUN Peng, CHEN Jian, LIU Pengying, HE Guangjun, LOU Shuqin. Remote Sensing Data Intelligent Interpretation Task Scheduling Algorithm Based on Heterogeneous Platform[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251072

Citation:

HAO Lijiang, TIAN Luyun, SUN Peng, CHEN Jian, LIU Pengying, HE Guangjun, LOU Shuqin. Remote Sensing Data Intelligent Interpretation Task Scheduling Algorithm Based on Heterogeneous Platform[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251072

Citation:

HAO Lijiang, TIAN Luyun, SUN Peng, CHEN Jian, LIU Pengying, HE Guangjun, LOU Shuqin. Remote Sensing Data Intelligent Interpretation Task Scheduling Algorithm Based on Heterogeneous Platform[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251072

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Remote Sensing Data Intelligent Interpretation Task Scheduling Algorithm Based on Heterogeneous Platform

doi: 10.11999/JEIT251072 cstr: 32379.14.JEIT251072

HAO Lijiang^{1, 2},
TIAN Luyun^{1, 3
,
,},
SUN Peng^{1, 2},
CHEN Jian^{1, 3},
LIU Pengying^{1, 2},
HE Guangjun¹,
LOU Shuqin²

1.
State Key Laboratory of Space Information System and Integrated Application, Beijing 100095, China
2.
Beijing Jiaotong University, Beijing 100084, China
3.
North western Polytechnical University, Xian 710072, China

Funds: The Civil Aerospace Technology Pre-Research Project of China’s 14th Five-Year Plan (D040201)

Received Date: 2025-10-11
Accepted Date: 2026-01-05
Rev Recd Date: 2026-01-05

Available Online: 2026-01-12

Abstract

Abstract

Objective Intelligent remote sensing data intelligent interpretation tasks executed on heterogeneous platforms exhibit diverse task types, heterogeneous resources, sensitivity to real-time environmental disturbances, and inter-task resource contention. These characteristics often lead to load imbalance and reduced resource utilization across platforms. Therefore, adaptive and efficient scheduling of complex multi-task workloads in resource-heterogeneous environments remains a central challenge in heterogeneous platform task scheduling. Methods A Heterogeneous Remote Sensing–Intelligent Task Scheduling (HRS-ITS) algorithm is proposed. The CP-SAT optimizer is enhanced by incorporating four score factors—data affinity, load balancing, makespan prediction, and cross-device transmission efficiency—as optimization objectives to generate an initial task–resource mapping. An adaptive resource-scaling–based Dueling Double Deep Q-Network (D3QN) model is then constructed to optimize task execution sequences for makespan reduction. Resource allocation is dynamically adjusted to eliminate idle time during task queuing, enabling dynamic resource perception and configuration optimization. Results and Discussions By integrating static optimization with dynamic adaptation, the HRS-ITS algorithm improves scheduling efficiency and resource utilization on heterogeneous platforms, providing an effective solution for complex remote sensing data intelligent interpretation tasks. Conclusions The proposed framework combines global optimization with dynamic adaptation to achieve computationally efficient real-time remote sensing processing. It provides a basis for extension to more complex task dependencies and larger-scale clusters.
- Heterogeneous platform,
- Remote sensing data intelligent interpretation tasks,
- Task scheduling,
- CP-SAT optimizer,
- D3QN

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References(28)

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