基于容器云技术的典型遥感智能解译算法集成

doi:10.11959/j.issn.2096-0271.2022015

摘要/Abstract

摘要：

针对当前航天遥感信息处理向云计算发展的趋势，将容器云技术应用于遥感数据的智能处理，通过在计算集群中部署遥感解译算法镜像与分布式存储服务，屏蔽复杂的环境依赖问题，并通过配置文件进行流程管理，形成了从开发到部署的整体技术路线，为遥感智能解译技术的集成提供了高效可靠的新思路。以几种典型的遥感智能解译算法为例，证明了该方案在智能解译模型的集成化开发部署上的高效性，为遥感智能解译技术的新型云端模式探索了可行性方案。

关键词: 人工智能, 深度学习, 遥感图像, 智能解译, 容器云技术

Abstract:

With the development of cloud computing technology in remote sensing data processing, container cloud technology was applied to remote sensing data processing, remote sensing intelligent processing algorithm image was deployed and storage services were distributed in the computing cluster, shielding complex environmental dependence problems.And management through configuration files was processed forming an overall technical route from development to deployment, which provides an efficient and reliable new scheme for the integration of remote sensing intelligent processing algorithms.Taking several typical remote sensing intelligent algorithms as examples, the efficiency of the scheme in the integrated development and deployment was proved, and the feasibility of the new cloud mode of remote sensing intelligent technology was explored.

Key words: artificial intelligence, deep learning, remote sensing image, intelligent processing, container cloud technology

中图分类号:

TP75

赵智韬, 赵理君, 张正, 唐娉. 基于容器云技术的典型遥感智能解译算法集成[J]. 大数据, 2022, 8(2): 58-74.

Zhitao ZHAO, Lijun ZHAO, Zheng ZHANG, Ping TANG. Integration of remote sensing intelligent processing algorithm using container cloud technology[J]. Big Data Research, 2022, 8(2): 58-74.

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对比项	传统深度学习	基于Kubernetes容器云平台的深度学习
GPU调度	手动调度	自动调度
数据存储	训练数据和模型文件难以保存	分布式存储GlusterFS
部署集成	困难，手动解决冲突	容器化集成部署

名称	数据类型	功能说明
Input_datadir	String	输入数据的挂载目录路径
Input_args	Array	输入参数列表
Output_dir	String	输出数据的存储路径
Output_weight	String	模型权重的保存路径
Phase_model	String	模型阶段(训练、推理)

名称	数据类型	功能说明
jobstate	String	记录容器当前运行情况
loss	Array	模型损失函数
nowstep	String	当前运行步骤
totalstep	String	最大运行步骤
data	Object	输出数据

IP地址	10.1.17.7	10.1.17.11	10.1.17.83
CPU	Inter Core i7-4790 CPU, 4 cores, 3.60 GHz	Intel Core Xeon E5-2620, 32 cores, 2.10 GHz	Intel Core Xeon E5-2620, 32 cores, 2.10 GHz
内存	24 GB RAM	126 GB RAM	126 GB RAM
硬盘空间	2 TB	16 TB	16 TB
GPU	1x NVIDIA GTX1080Ti	4x NVIDIA TITAN Xp	2x NVIDIA GTX2080Ti
操作系统	Ubuntu 18.04 LST	Ubuntu 16.04 LST	Ubuntu 20.04 LST
部署	Kubernetes 1.20, Docker 19.03, Dashboard	Kubernetes 1.20, Docker 19.03, Portainer 1.20	Kubernetes 1.20, Docker 19.03, Portainer 1.20

算法	概述	数据集	基础镜像
S2ANet	旋转框目标检测算法^[1]	DOTA-v1.0	mmdetecitonv1:s2anet
YOLOv3	水平框目标检测算法^[2]	DOTA-v1.0	tensorflow/tensorflow:1.12.0-gpu-py3
ResNet101	场景分类算法^[3]	UC Merced	pytorch/pytorch:1.6.0-cuda10.1-cudnn7-devel
UNet	地物分类算法^[4]	DSTL	tensorflow/tensorflow:1.10.0-gpu-py3
UNet-VGG16	变化检测算法^[5]	SpaceNet7	sn7_baseline_image