高分遥感图像相对辐射校正中的伪不变地物自动提取和优化选择

doi:10.12082/dqxxkx.2021.200266

地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (5): 903-917.doi: 10.12082/dqxxkx.2021.200266

高分遥感图像相对辐射校正中的伪不变地物自动提取和优化选择

施海霞¹(), 韦玉春²^,³^,⁴, 徐晗泽宇²^,³^,⁴^,^*(), 周爽²^,³^,⁴, 程琪²^,³^,⁴

1.江苏省土地勘测规划院,南京 210000
2.南京师范大学地理科学学院,南京 210023
3.南京师范大学虚拟地理环境教育部重点实验室,南京 210023
4.江苏省地理信息资源开发与利用协同创新中心,南京 210023

收稿日期:2020-05-26 修回日期:2020-08-10 出版日期:2021-05-25 发布日期:2021-07-25
通讯作者: *徐晗泽宇（1993— ）,男,甘肃嘉峪关人,博士生,研究方向为遥感图像分析。E-mail:xuhanzeyu@njnu.edu.cn
作者简介:施海霞（1979— ）,女,江苏南京人,高级工程师,主要研究方向为土地利用及地理信息应用研究。E-mail:178537186@qq.com
基金资助:
江苏省研究生科研创新计划项目(KYCX20_1179);国家自然科学基金项目(41471283)

Automatic Extraction and Optimal Selection of the Pseudo Invariant Features for the Relative Radiometric Normalization in High-Resolution Remote Sensing Imagery

SHI Haixia¹(), WEI Yuchun²^,³^,⁴, XU Hanzeyu²^,³^,⁴^,^*(), ZHOU Shuang²^,³^,⁴, CHENG Qi²^,³^,⁴

1. Jiangsu Provincial Land Survey and Planning Institute, Nanjing 210000, China
2. School of Geography, Nanjing Normal University, Nanjing 210023, China
3. Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
4. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

Received:2020-05-26 Revised:2020-08-10 Online:2021-05-25 Published:2021-07-25
Contact: XU Hanzeyu
Supported by:
The Postgraduate Research and Practice Innovation Program of Jiangsu Province(KYCX20_1179);National Natural Science Foundation of China(41471283)

摘要/Abstract

摘要：

相对辐射校正是遥感变化检测中重要的预处理过程,伪不变地物（Pseudo-Invariant Features,PIF）是多时相影像中相对不变的地物,是相对辐射校正中的重要依据。针对高分遥感图像变化检测中相对辐射校正的要求,本文提出了一个自动提取和优化选择PIF的流程和方法：首先计算两期图像的亮度、光谱特征和空间特征的变化向量,然后对各变化向量的像元值从低到高进行排序,经多数投票后提取PIF,最后使用“迭代线性回归—去除异常值”方法选择获得最终PIF。以2016年11月27日和2017年7月18日的2期“北京二号”高空间分辨率多光谱影像为例,选择地物占比不同的两个实验区对流程和方法进行了验证,并与多元变化检测和迭代加权多元变化检测的PIF提取方法进行了比较。使用两期WorldView-2影像和Landsat-8 OLI影像对方法的适用性进行了验证。结果表明：① 2个实验区提取的PIF精度分别为98.74%和98.71%,PIF像元合理分布于未变化区域、包括了影像中主要的地物类型;② 使用本文方法提取的PIF建立的相对辐射校正模型具有显著的线性拟合效果（p<0.000 1）;③ 本文方法考虑了图像亮度、光谱信息以及空间信息的差异,使用参数少,可操作性高;④ 与多元变化检测和迭代加权多元变化检测方法相比,本文方法提取的PIF更为合理,建立的辐射校正方程拟合效果更佳;⑤ 本文方法适用于具有相同波段设置的中、高空间分辨率光学遥感影像。

关键词: 相对辐射校正, 高空间分辨率遥感图像, 伪不变地物, 遥感变化检测, 图像特征, 北京二号, 遥感信息提取, 特征选择

Abstract:

Relative radiometric normalization is an important process in the remote sensing image change detection. Identifying the Pseudo-Invariant Features (PIF) with invariant or near-invariant radiometric reflectance over a certain period in multi-temporal images is a key to radiometric normalization. This paper proposed a novel method for automatic extraction and optimal selection of PIF. First, the change vectors including brightness, and spectral and spatial domains of bitemporal images were generated. Then, the pixels of each change vector were sorted from the lowest to the highest value, and the majority vote algorithm was used to extract the initial PIF. Finally, the PIF was selected by the iterative linear regression and outlier analysis. Taking two multi-spectral high-resolution Tripesat-2 images acquired on November 27, 2016 and July 18, 2017 as example, two typical regions with different land cover types were selected to test the proposed method. The proposed method was compared with Multivariate Alteration Detection (MAD) and Iteratively Reweighted Multivariate Alteration Detection (IR-MAD) methods. The applicability of the proposed method was also validated by two WorldView-2 and Landsat OLI images. The results show that: (1) the accuracy of the PIF that extracted within two regions were 98.74% and 98.71%, respectively. The extracted PIF was distributed in the unchanged areas and covered the main land cover types in the images; (2) the linear regression models of the relative radiometric normalization using the PIF extracted by the proposed method were significant (p<0.000 1); (3) the differences in image brightness, spectral domain, and spatial domain were taken into account in this method with less parameters and high operability; (4) compared with MAD and IR-MAD, the proposed method showed a better performance in extraction precision and an significant linear regression model of the relative radiometric normalization; and (5) the proposed method was suitable for other medium- or high-resolution remote sensing images with same bands.

Key words: relative radiometric normalization, high-resolution satellite images, pseudo-invariant features, remote sensing change detection, image feature, TripleSat-2, remote sensing information extraction, feature selection

施海霞, 韦玉春, 徐晗泽宇, 周爽, 程琪. 高分遥感图像相对辐射校正中的伪不变地物自动提取和优化选择[J]. 地球信息科学学报, 2021, 23(5): 903-917.DOI:10.12082/dqxxkx.2021.200266

SHI Haixia, WEI Yuchun, XU Hanzeyu, ZHOU Shuang, CHENG Qi. Automatic Extraction and Optimal Selection of the Pseudo Invariant Features for the Relative Radiometric Normalization in High-Resolution Remote Sensing Imagery[J]. Journal of Geo-information Science, 2021, 23(5): 903-917.DOI:10.12082/dqxxkx.2021.200266

图/表 17

表1

“北京二号”影像的主要参数

类型	参数
卫星轨道信息	太阳同步轨道（SSO）轨道高度：651 km 升交点地方时（LTAN）：10 : 30
侧摆能力	整星侧摆 $±$ 45°
影像分辨率/m	全色：0.8;多光谱：3.2
幅宽/km	24
信噪比	> 100:1
MTF	全色：> 0.1;多光谱：> 0.2
量化值/bit	10
成像模式	多景模式沿轨立体跨轨立体条带模式（约4000 km）区域成像（约40 km×40 km）
重复观测周期/d	1
波段范围/nm	蓝：440~510 绿：510~590 红：600~670 全色：450~650 近红外：760~910
运行寿命/a	7

表1

图1

图2

图3

图4

图5

图6

图7

表2

图8

图9

图10

图11

表3

图12

图13

表4

参考文献 35

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	波段	R1				R2
	波段	一元线性回归方程	R²	RMSE		一元线性回归方程	R²	RMSE
原始图像	B1	y=0.6978x+0.0685	0.4958	0.0131		y=0.7654x+0.0530	0.5033	0.0119
	B2	y=0.6133x+0.0774	0.4140	0.0163		y=0.6533x+0.0670	0.3924	0.0162
	B3	y=0.6212x+0.0718	0.3818	0.0227		y=0.6976x+0.0597	0.3334	0.0262
	B4	y=0.3412x+0.1846	0.1067	0.0419		y=0.3629x+0.1941	0.1205	0.0398
PIF_initial	B1	y=0.7740x+0.0499	0.8917	0.0066		y=0.8233x+0.0377	0.8719	0.0065
	B2	y=0.7542x+0.0487	0.8715	0.0079		y=0.7685x+0.0419		0.8572	0.0077
	B3	y=0.7688x+0.0403	0.8378	0.0115	y=0.8027x+0.0312		0.8194	0.0118
	B4	y=0.6207x+0.1148	0.3251	0.0370	y=0.5447x+0.1404		0.2572	0.0369
PIF_select	B1	y=0.7748x+0.0507	0.9540	0.0037	y=0.8242x+0.0374	0.9562	0.0026
	B2	y=0.7630x+0.0479	0.9624	0.0037	y=0.7760x+0.0405		0.9529	0.0032
	B3	y=0.7724x+0.0415	0.9720	0.0041	y=0.8073x+0.0304		0.9737	0.0032
	B4	y=0.7547x+0.0820	0.9267	0.0093	y=0.6577x+0.1155		0.7884	0.0140

	波段	R1				R2
	波段	一元线性回归方程	R²	RMSE		一元线性回归方程	R²	RMSE
PIF_MAD	B1	y=0.6823x+0.0682	0.6434	0.0090		y=0.7945x+0.0460	0.6680	0.0084
	B2	y=0.5894x+0.0769	0.5655	0.0108		y=0.6883x+0.0582	0.6028	0.0106
	B3	y=0.6092x+0.0676	0.5554	0.0148		y=0.7523x+0.0454	0.6073	0.0156
	B4	y=0.3837x+0.1764	0.1169	0.0427		y=0.4517x+0.1786	0.1575	0.0404
PIF_IR-MAD	B1	y=0.7700x+0.0570	0.6549	0.0085		y=0.8699x+0.0373	0.6342	0.0082
	B2	y=0.6665x+0.0693	0.5500	0.0109	y=0.7440x+0.0542		0.5060	0.0119
	B3	y=0.6839x+0.06264	0.4901	0.0164	y=0.8381x+0.0412		0.4656	0.0198
	B4	y=0.4750x+0.1638	0.1871	0.0376	y=0.5272x+0.1701		0.1990	0.0381

波段	WorldView-2			Landsat-8 OLI
波段	一元线性回归方程	R²	RMSE	一元线性回归方程	R²	RMSE
B1	y=0.6550x-0.0076	0.8672	0.0050	y=0.9660x+0.0033	0.9882	0.0017
B2	y=1.1694x-0.0186	0.8408	0.0096	y=0.9457x+0.0046	0.9899	0.0018
B3	y=0.6819x-0.0054	0.9112	0.0057	y=0.9534x+0.0054	0.9909	0.0024
B4	y=0.2154x+0.0845	0.8446	0.0135	y=0.9391x+0.0015	0.9826	0.0070

高分遥感图像相对辐射校正中的伪不变地物自动提取和优化选择

Automatic Extraction and Optimal Selection of the Pseudo Invariant Features for the Relative Radiometric Normalization in High-Resolution Remote Sensing Imagery

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