高分辨率遥感影像样本库动态构建与智能解译应用

doi:10.11947/j.AGCS.2024.20230469

摘要/Abstract

摘要：

在人工智能时代，遥感影像解译朝着自动化智能化方向发展，高质量的样本数据集是其核心。我国积累了海量优质的时空地理信息基础数据及衍生产品，是深度学习驱动的遥感影像智能解译样本的重要来源。盘活现有数据资源，可推动人工智能与遥感解译的应用深度与广度。本文基于现有数据资源，针对样本数据集区域受限、时效性不强、类型单一等问题，研究了面向深度学习的高分遥感影像智能解译样本库动态构建技术。首先，分析了要素提取、地表覆盖分类、变化检测方面的公开样本数据集的特点，提出业务驱动的样本应需生成-动态构建-智能应用思路；其次，研究了基于历史解译成果的样本自动生成、SAM大模型提示学习引导的样本清洗精化方法及实现过程；再次，设计了具有区域性、时序性、尺度性、多传感器、多类型的样本库，以及顾及空间-时间-地类关系的动态样本数据库架构，研究了样本数据集“量化-检索-组合”动态重构过程，实现时空样本的动态管理与多维检索；最后，开展了地表覆盖分类、要素提取、变化检测等智能解译应用，验证了本文研究思路及方法的可行性，以期推动基于已有基础数据的样本数据集的有效利用，以及样本构建-管理-应用及数据-模型-业务的互联互通，为高分遥感影像智能解译样本库构建与应用提供参考思路。

关键词: 高分辨率遥感影像, 样本库, 样本精化, 动态构建, 智能解译, 深度学习, 地表覆盖分类, 变化检测

Abstract:

In the era of artificial intelligence, the interpretation of remote sensing images is moving towards automation and intelligence. High-quality sample datasets are crucial for this development. A massive amount of high-quality spatio-temporal geospatial information and derived products has been accumulated in China, which serve as an important source for deep learning-driven intelligent interpretation of remote sensing images. Leveraging existing data resources can promote the depth and breadth of artificial intelligence and remote sensing interpretation applications. Aiming at the limitations of existing sample datasets, such as regional restrictions, limited timeliness and sample types, this paper proposes a dynamic construction technique for a high-resolution remote sensing image intelligent interpretation sample datasets based on existing data resources. Firstly, a business-driven framework for sample datasets demand generation, dynamic construction, and intelligent application is proposed based on the characteristics analysis of publicly available sample datasets for feature extraction, land cover classification, and change detection. Secondly, this research investigates methods and implementation processes for sample generation based on historical interpretation results, as well as sample refinement through SAM (segment anything model) large model prompt learning-guided cleaning. Furthermore, a sample dataset with regional, temporal, scale, multi-sensor, and multi-type features is designed, along with spatial-temporal-land cover relationships. The study explores the dynamic reconstruction process of sample dataset quantification-retrieval-combination, achieving dynamic management of spatiotemporal samples and multidimensional retrieval. Finally, intelligent interpretation applications such as land cover classification, feature extraction, and change detection are conducted to validate the feasibility of the proposed methods. The aim is to promote the effective utilization of sample datasets based on existing foundational data, as well as the interconnection of sample construction-management-application and data-model-business, providing reference ideas for the construction and application of high-resolution remote sensing image intelligent interpretation sample datasets.

Key words: high-resolution remote sensing images, sample datasets, sample refinement, dynamic construction, intelligent interpretation, deep learning, land cover classification, change detection

中图分类号:

P237

顾海燕, 杨懿, 李海涛, 孙立坚, 丁少鹏, 刘世琦. 高分辨率遥感影像样本库动态构建与智能解译应用[J]. 测绘学报, 2024, 53(6): 1165-1179.

Haiyan GU, Yi YANG, Haitao LI, Lijian SUN, Shaopeng DING, Shiqi LIU. Dynamic construction of high-resolution remote sensing image sample datasets and intelligent interpretation applications[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1165-1179.

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地理国情信息分类码				深度学习的自定义编码
代码	一级	二级	三级	代码	类别
100	种植土地			1	水田
110		水田		1
120		旱地		2	旱地
130		果园		3	园地
131			乔灌果园	3
132			藤本果园	3
133			草本果园	3
140		茶园		3
150		桑园		3
160		橡胶园		3
170		苗圃		3
180		花圃		3
190		其他经济苗木		3
191			其他乔灌经济苗木	3
192			其他藤本经济苗木	3
193			其他草本经济苗木	3

项目	内容
分区	东北、华北、华东、华中、华南、西南、西北
时相	覆盖全年多时相，主要考虑夏季和冬季两个差异度最大的时相，其中冬季时相的时间跨度设定为12月—次年3月，夏季时相的时间跨度设定为6—9月
地形地貌	平地、丘陵地、山地、高山地
影像类型	分辨率优于2 m的高分辨率遥感影像（如北京二号、高分一号、高分二号、资源三号等）
数据集类型	要素提取、地表覆盖分类、变化检测

序号	字段名	字段简写	字段含义	填写规则	填写方式	类型	示例
1	FID	FID	序号ID	自增ID	自动	Int	27
2	TreeID	TreeID	Tree编码	样本的Tree编码，采用3位序列编码，同层最大编码不超过1000个节点	自动	String	003029
3	SamSet Name	YBMC	样本集名称	对应并总结概括样本集中样本内容	人工	String	内蒙古耕地
4	SampSet Nano	YBJX	样本集名称简写	样本集名称的简写形式	人工	String	蒙耕地
5	SampSetDate	JCJSJ	创建时间	采集时间	自动	Date	2022-08-30
6	Creator	JCJR	创建人	操作人员姓名	人工	String	张三
7	SampBak	JBak	备注	样本集的辅助性说明	人工	String	该样本集主要针对草原生态监测有关的科研样本
8	SampSet Link	YBJ	样本集	关联样本信息表	自动	String	YB3715_2022

序号	字段名	字段简写	字段含义	填写规则	填写方式	类型	示例
1	FID	FID	数据记录ID	自增ID	自动	Int	99
2	SampleID	YBID	样本ID	样本唯一标识码：数据源_X_Y	自动	Int	GF1293_75
3	Classification scheme	YBCS	分类体系码	—	自动	Int	2
4	Classification Code	CC	分类码	—	自动	Int
5	Longitude	X	经度	度（小数）	自动	Double	118.653 498 7
6	Latitude	Y	纬度	度（小数）	自动	Double	41.349 348 47
7	Elevation	Z	高程	整数（米）	自动	Double	155
8	DataSource	SJY	数据源	数据源信息表中序号	自动	Int	14
9	SampleWidth	YBKD	样本宽度	样本宽度（像素）	人工	Int	1024
10	Sample Height	YBGD	样本高度	样本高度（像素）	人工	Int	1024
11	SampleDate	CJSJ	采集时间	采集时间	自动	Date	2021-08-30
12	Operator	CJR	采集人	人名	人工	String	张某
13	Bak	Bak	备注	辅助性说明	人工	String	钢架大棚房
14	SampleImage	YBIM	样本影像	大对象	自动	Blob
15	SampleLabel	YBLB	样本标签	大对象	自动	Blob

序号	国产光学卫星名称	卫星名称简写	全色分辨率/m	多光谱分辨率/m	幅宽/km
1	北京二号	BJ2	0.8	3.2	24
2	高分一号	GF1	2	8	35
3	高分二号	GF2	0.8	3.2	45
4	高分六号	GF6	2	8	90
5	资源三号	ZY3	2.1	5.8	50
6	高景一号	GJ1	0.5	2	12