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1007-4619(2014)02-0320-15 Journal of Remote Sensing 遥感学报
Effect of different radiation correction methods of Landsat
TM data on land-cover remote sensing classification
CHEN Chenxin1,2,HU Changmiao1,HUO Lianzhi1,TANG Ping1
1. Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100101 ,China;
2. Technology and Engineering Center for Space Utilization,Chinese Academy of Sciences,Beijing 100094,China
Abstract:Radiometric correction,which is often used as a preliminary step for remote sensing classification,is required in the e
fficient extraction of land-cover information from remote sensing images,particularly in areas with rough terrain. The objective of
this study is to examine the effect of different radiation correction methods of Landsat TM data on land-cover remote sensing classifi-
cation. Three different radiometric correction methods (ATCOR 3,FLAASH and Look-Up Table (LUT) ) were introduced,and
the Maximum Likelihood Classifier (MLC)and Support Vector Machines (SVMs)were used for classifiers. The training samples
were s elected from three corrected images based on geographical coordinates and classifier training to classify the three corrected
images mutually. The results indicate the following. (1)The samples selected from the classification image for classifier training
significantly improve classification accuracy compared with the samples selected from other images. (2)The classification results of
the three radiometric correction images are different,whereas the results of the ATCOR3 and FLAASH methods are similar. (3)
The effect of radiometric correction on the classification accuracy of each category is different:radiometric correction affects
“Forest”significantly and other categories variably.
Key words:radiation correction,Landsat TM,MLC,SVMs,land-cover classification,accuracy assessment
CLC number:TP79 Document code:A
Citation format:Chen C X,Hu C M,Huo L Z and Tang P. 2014. Effect of different radiation correction methods of Landsat TM d
ata on land-cover remote sensing classification. Journal of R emote Sensing,1 8(2) : 320 - 334 [DOI:10 . 11834 /
jrs. 20143211]
Received:2013-08-26;Accepted:2013-11 -27;Version of record first published:2013-12-04
Foundation:High Technology Research and Development Project of China (863 Program) ( No. 2009AA122002)
First author biography:CHEN Chenxin (1983—) ,male,research assistant. He majors in image processing. E-mail:chencx@ csu. ac. cn
Corresponding author biography:TANG Ping(1968—) ,female,professor. Her main research interest is image processing. E-mail:tangping@ radi.
ac. cn
1 INTRODUCTION
Landsat TM images have been widely used for land-cover
mapping via image classification and for identifying changes via
change detection (Fox,et al. ,1983;Jensen,et al. ,1993;
Yuan,et al. ,2005;Sexton,et al. ,2013). However,con-
straints exist in using Landsat data for land-cover studies. Land-
sat data is affected by many factors,including illumination chan-
ges,atmospheric effects,and topographic effects in areas with
rough terrain. Given that the electromagnetic signals collected by
satellites in the solar spectrum is modified by the scattering and a
bsorption of gas and aerosols while travelling through the a
tmosphere from the Earth's surface to the sensor (Song,et al. ,
2001) ,the image information is often inconsistent with the actu-
al surface;this discrepancy affects the accuracy of information e
xtraction. Therefore,atmospheric and topographic correction is
used as a primary task before further processing (Honkavaara,
Hakala,et al. ,2012).
Radiometric correction is often used for multi-temporal
(Zhang,et al. ,2006;Vicente-Serrano,et al. ,2008;Tan,et
al. ,2012;Xu,et al. ,2012 ) ,multi-sensor (Xu & Zhang
2013) ,and large-scale remote sensing data analyses (Olthof,et
al. ,2005 ;Hansen & Loveland,2012 ). Studies have shown
that superior r emote sensing information can be extracted from
radiometric corrected images,such as images from ecological ap-
plications (Gu,et al. ,2011)and land-use / land-cover classifi-
cation (Meyer,et al. ,1993 ;Pons & Solesugranes,1994;Colby
& Keating 1998;Dorren,et al. ,2003;Kobayashi & Sanga-
Ngoie,2009;Habib,e t al. ,2011 ). However,few studies
have reported the effects of TM radiometric correction on sample
collection strategies and land-cover classification.
This research investigated the effects of the different radia-
tion correction methods of Landsat TM data on land-cover remote
sensing classification. The study was conducted as follows.
First,radiometric correction was performed on images by using
three different radiation correction methods. Second,samples
were selected in one corrected image;the samples were used for
other image classification procedures. Finally,the classification
accuracy was performed. The position and number of training
samples remained unchanged. The classification algorithms used
in this study were the Maximum Likelihood Classifier (MLC )
and Support Vector Machines (SVMs).
CHEN Chenxin,et al. :Effect of different radiation correction methods of Landsat
TM data on land-cover remote sensing classification 321
2 STUDY AREA AND DATA
2. 1 Study area
The study area is the north central mountain regions of
Shaanxi Province in northwest China. Fig. 1 shows the location of
the study area (The Landsat TM scene is displayed as an RGB
image (R = NIR,G = red,B = green)). The region is loca-
ted in the Loess Plateau and has a cold arid climate or cold semi-
arid climate. The study area covers a wide range of mountain
chains and some small hills. The dominant land covers include
forests,bare soil,and farmlands. Farmlands are mixed with
scattered rural villages in flat low areas,and steep mountainous
areas are dominated by forest species.
2. 2 Data
The final data set used in this study comprised an original
Landsat TM image and three radiometric corrections images with
spatial resolutions of 30 m. The original image was acquired on
May 12,2000 with good visibility and without clouds from a
Landsat satellite (Path:127,Row:35). The geometric correc-
tion was conducted by the data provider.
2. 3 Classification system and sample selection
The classification system and class definitions of Gong,e
t al. (2013)were used in this study. According to the land-cov-
er characteristics of the study area,this paper divided land-cover
categories into six categories,namely,bare soil,crop,forest,i
mpervious area,water,and hill.
Table 1 shows the characteristics,description,and distribu-
tion of the samples in the training and test sets among the six
land-cover classes that characterize the scene under investiga-
tion. These samples were collected by geological image interpre-
tation via Google Earth. The samples were extracted from differ-
ent spatial positions in the study area. First,the visual interpre-
tation of the spatial position of the classes was conducted by u-
sing a Landsat TM image. The spatial positions were navigated in
Google earth for a “field”test. High-quality sample sets were
then obtained. Second,the sample was optimized and duplicate
sample points were removed. Finally,the samples were randomly
split into training samples and test samples according to a certain
proportion. The total training and testing samples of all six clas-
ses were 2445 and 477 ,respectively (Table 1).
3 RADIOMETRIC CORRECTION
In this paper,three different approaches were used for radi-
ation correction:FLAASH ([2013 - 08 - 01]http:/ / www. ex-
elisvis. com / docs / FLAASH. html) ,ATCOR3( [2013 - 08 - 01]
http:/ /www. rese. ch / pdf / atcor3 _manual. pdf) ,and the Look
Up Table (LUT)method (Liang,et al. ,1997 ) ,which is wide-
ly used by Landsat TM/Enhanced TM + data irradiation correc-
tion. FLAASH and ATCOR are commercial atmospheric correc-
tion software modules that support the vast majority of multi-spec-
tral and h yper-spectral r emote sensing data for atmospheric cor-
rection processing. FLAASH and ATCOR are both based on ra-
diative transfer theory but differ in radiative transfer equations.
The LUT a pproach is a highly flexible approach that is widely a-
dopted by national researchers.
The FLAASH atmospheric correction package was developed
by Spectral Science,American Aerodynamics Research Labora-
tory,and Spectral Information Technology A pplications Center.
FLAASH works at a 400 nm to 2500 nm wavelength range and u-
ses MODTRAN 4 to generate a series of atmospheric parameter
LUTs. The radiative transfer model of FLAASH considers the
proximity effect but not terrain correction.
322 Journal of Remote Sensing 遥感学报 2014,18(2)
ATCOR radiometric correction software was developed by
the German Aerospace Center. ATCOR has three versions:A
TCOR2,ATCOR3,and ATCOR4 (Richter,2005). ATCOR2
and ATCOR3 has been integrated in remote sensing software ER-
DAS. We adopted ATCOR3 in the current study to process
Landsat TM data. ATCOR3 was mainly used for the atmospheric
and terrain corrections of the remote sensing images of mountains
and regions. ATCOR3 contains MODTRAN4-calculated LUTs of
various atmospheric conditions,atmospheric transmittance,path
radiance,and direct and diffuse solar radiation flux. ATCOR3
can use built-in modules to calculate the slope,aspect,moun-
tainous terrain shading,and other data to complete terrain cor-
rection. For areas with large incidence angles,ATCOR3 pro-
vides a reliable empirical bi-directional r eflectance distribution
function. ATCOR3 uses different algorithms for visible and infra-
red radiation correction,and provides history-removing modules.
LUT-based atmospheric correction assumes that the surface
is Lambertian. The radiative transfer equation is expressed as
L = Lp+ T Eρ
π(1-ρS)(1)
where Lis the apparent radiance,Lpis the atmospheric path ra-
diation,ρis the surface reflectance,Eis the surface irradiance,
Sis the atmospheric albedo,and Tis the transmittance of the
surface to the top of the atmosphere. Parameters E,T,Lp,and
Scan be directly calculated by 6S (Second Simulation of Satel-
lite Signal in the Solar Spectrum) ( Vermote,et al. ,1997 ).ρ
can be calculated when Lis known.
The LUT algorithm for Landsat TM data was developed
based on the method of Liang,et al. (1997) ,which uses dense
dark vegetation (Kaufman,et al. ,1997 )to estimate the aerosol
thickness at the vegetation and the moving window interpolation
method to estimate the distribution of the whole aerosol scene.
A semi-empirical smooth terrain correction method was used
after the LUT-based atmospheric correction. First,the terrain
slope angle was smoothened. Thereafter,the smoothed c osine
terrain correction was used. Smooth terrain treatment was used to
eliminate the non-Lambertian effects caused by abnormal radia-
tion. Shuttle radar topography mission data (Version 4. 1)was
used for atmospheric correction. The spatial resolution of the ele-
vation data was 90 m.
4 EXPERIMENT AND RESULT
The effects of different radiometric corrections on the classi-
fication results were assessed by comparing the images in d
ifferent classifications after radiometric corrections. To evaluate
the effect of radiometric correction on classification accuracy,we
conducted nine experiments by using two classifiers,namely,
MLC and SVMs.
4. 1 Classifier
A number of methods have been used for remote sensing
classification. These methods can be grouped as supervised / un-
supervised,parametric / nonparametric,hard / soft,per pixel,per
s ubpixel,or per field (Lu & Weng,2007). We selected two
widely used supervised approaches,MLC and SVMs,to test the
effects of radiometric correction on the classification process.
MLC is a parametric classifier based on statistical theory
and is one of the most widely used classifiers (Huang,et al. ,
2002). Among remote sensing classification methods,MLC can
clearly explain parameter abilities,can be easily integrated with
prior knowledge,and can be easily implemented. By using the
statistical parameters (mean vector and variance-covariance m
CHEN Chenxin,et al. :Effect of different radiation correction methods of Landsat
TM data on land-cover remote sensing classification 323
atrix) ,the probability of a pixel belonging to a predefined set of
classes is calculated. The pixel is then assigned to the class with
the highest probability (Tso & Mather,2009). SVMs are based
on the structural risk minimization principle and their p opularity
within the remote sensing community is constantly i ncreasing be-
cause of their properties and intrinsic effectiveness (Lorenzo &
Lorenzo,2006). The Radial Basis Function (RBF)was chosen
as the kernel function of SVMs,and RBF model p arameters
were established by using the cross-validation method in the mod-
el training process. The data was normalized to [0,1]during the
process by LIBSVM (Chang & Lin,2011).
4. 2 Flowchart
Fig. 2 shows the experimental workflow. First,by using
three radiometric correction methods to correct the original Land-
sat TM image,corrected images RC_A,RC_B,and RC_C were
used in the next process. Second,training samples (Samp _ a,
Samp_b,and Samp_c)were selected from the above three i mages
by using the same regions of interest and the classifiers (MLC and
SVMs)were obtained. Finally,the classifiers were used for the
land-cover classification of the four images. The a ccuracy of the
classified image was assessed by using a range of reference data
through the confusion matrix. The producer and user accuracies,
along with the overall accuracy and Kappa coefficient statistic for
each class,were calculated from the confusion matrix.
Fig. 2 Experimental workflow
4. 3 Classification result
Fig. 3 shows the classification maps with the best accuracies
obtained by MLC and SVMs. The left image shows the classifica-
tion result of the RC_A image when samples were collected by u-
sing MLC. The right image shows the classification result of the
RC_C image when samples were collected by using SVMs.
Fig. 3 Resultant classification images with the best accuracies obtained by MLC and SVMs
The classification accuracies obtained for the MLC and
SVMs are reported in terms of overall accuracy and Kappa coeffi-
cient in Table 2. These results show that the use of samples from
the classification image provides higher accuracies than other
combinations. F urthermore,the classification results of the
three r adiometric correction image are different,whereas the r
esults for RC_A (ATCOR3)and RC_B (FLAASH)are similar.
The result for RC_C is different from the results of RC _ A and
R C_B.
Table 2 Comparison of classification accuracy on the basis of different sample collection
strategies after radiometric correction (MLC,SVMs)
Samples RC_A RC_B RC_C
OA /% Kappa OA /% Kappa OA /% Kappa
RC_A 93. 29 ,93. 50 0. 920 ,0. 922 91. 61 ,88. 26 0. 899 ,0. 859 77. 57 ,88. 05 0. 731 ,0. 857
RC_B 90. 36 ,87 . 63 0. 884,0 . 851 93. 29,93 . 50 0. 920,0 . 922 74. 21,88 . 05 0. 691 ,0. 857
RC_C 78. 05 ,84 . 49 0 . 702,0. 814 79. 20,85. 95 0 . 750,0. 831 91 . 20,93. 71 0. 894,0 . 925
324 Journal of Remote Sensing 遥感学报 2014,18(2)
Table 3 provides the category classification accuracy of e
xperiments that have high overall classification accuracies for
MLC and SVMs,i. e. ,the samples collected from the classifica-
tion image.
Table 3 Summary of experiment combinations that have high classification accuracies (MLC,SVMs)
Class name RC_A—RC_A RC _B—RC_B RC_C —RC_C
PA /% UA / % PA / % UA / % PA/ % UA /%
Bare soil 90. 00,90 . 00 91. 14,90 . 00 90. 00,88 . 75 87. 80,88 . 75 88. 75,91 . 25 85. 54 ,
87. 95
Crop 95. 00 ,95. 00 96. 20 ,96. 20 93. 75 ,95. 00 97. 40 ,98. 70 85. 00 ,96. 25 98. 55 ,97. 47
Forest 96. 25 ,98. 75 100. 0 ,98. 75 97. 50 ,98. 75 98. 73 ,97. 53 97. 50 ,97. 50 98. 73 ,98. 73
Hill 91. 25 ,92. 50 82. 02 ,84. 09 90. 00 ,91. 25 83. 72 ,82. 95 86. 25 ,90. 00 82. 14 ,85. 71
Impervious 90. 00 ,90. 00 92. 31 ,93. 51 91. 25 ,92. 50 93. 59 ,94. 87 92. 50 ,92. 50 85. 06 ,93. 67
Water 97. 40 ,94 . 81 100. 0,100 . 0 97. 40,94 . 81 100. 0,100 . 0 97. 40,94 . 81 100. 0 ,100. 0
Overall accuracy / % 93. 29 ,93. 50 93. 29 ,93. 50 91. 20 ,93. 71
Kappa 0. 920 ,0. 922 0. 920 ,0 . 922 0. 894 ,
0. 925
Note:PA:production accuracy,UA:use accuracy.
Fig. 4 provides the changes in different categories in each
band after three radiometric corrections. The three radiation c
orrection methods have similar results for RC_ A and RC _ B,
whereas the result of RC_C is slightly different from the results
of the former. The surface reflectance values of each class w
ith the LUT method are significantly lower than the FLAASH
and ATCOR methods after correction,specifically for the first
band.
Fig. 4 Changes of different categories in each band before and after radiometric correction
CHEN Chenxin,et al. :Effect of different radiation correction methods of Landsat
TM data on land-cover remote sensing classification 325
By comparing the classification results of three radiometric
correction images,the classification accuracy shows varying d
egrees of changes for almost all categories. For specific catego-
ries,the classification result of “Forest”changes significantly a
fter radiometric correction,thus indicating that this category is
sensitive to any radiometric correction (Fig. 5). This result can
be attributed to the use of the dark vegetation method in radio-
metric correction to estimate aerosol thickness. The obtained re-
sults r equire further analysis.
Fig. 5 Classification accuracy of different categories
after radiometric correction (MLC)
5 CONCLUSION
The radiometric correction of remote sensing imagery is a
prerequisite of data processing for many research applications.
The three different radiometric correction methods described in
Section 3 were applied to correct the original Landsat TM image.
Nine experiments were conducted by using MLC and SVMs,i
ncluding selected training samples from three corrected images
and classifier training,to classify the three corrected images m
utually and examine the effect of different radiation correction
methods of Landsat TM data on land-cover remote sensing classi-
fication.
The following preliminary conclusions are obtained:
(1)The samples selected from the classification image for
classifier training significantly improves the classification accura-
cy compared with the sample selected from other images,e. g. ,
the overall classification accuracy of RC_A—RC_A is 9 3. 29%
by MLC,followed by RC_B—RC_B (93. 29% )and RC_C—RC
_C (91. 20% ). These results are significantly higher than the
results obtained by using other combinations.
(2)The classification results of the three radiometric cor-
rection images are different,whereas the results of ATCOR3 and
FLAASH are similar. The overall classification accuracy of RC_
C has obvious differences with the overall classification accura-
cies of RC_A and RC _B,thus indicating that ATCOR3 is con-
sistent with the FLAASH method. By contrast,the LUT method
exhibits some deviations probably because of the error in aerosol
distribution estimation (Fig. 4).
(3)The effects of radiometric correction on the classifica-
tion accuracy for each category are different. Radiometric correc-
tion affects“Forest”significantly and other categories variably.
The SVMs approach produces higher classification accuracy than
MLC;however,both produce similar r esults.
The study area of this paper is small,thus limiting the
scope of the study,specifically on global topographies and land-
cover types. The future direction of this study will be directed to-
ward expanding the scope of the study area by using the results of
this study. The results of this paper do not reflect the effects of
terrain correction on classification accuracy. Follow-up studies
will focus on the sample selection and experiment process d
esign.
Acknowledgements:The authors would like to express
their sincerest appreciations to their laboratory colleagues for
their valuable comments and assistance during this work.
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陈趁新 等: Landsat TM 数据不同辐射校正方法对土地覆盖遥感分类的影响 327
Landsat TM 数据不同辐射校正方法对土地
覆盖遥感分类的影响
陈趁新1,2,胡昌苗1,霍连志1,唐娉1
1. 中国科学院 遥感与数字地球研究所,北京 100101;
2. 中国科学院 空间应用工程与技术中心,北京 100094
摘 要: 本文主要是探索 Landsat TM 数据不同辐射校正方法对土地覆盖遥感分类的影响。介绍了使用的 3种不同
辐射校正方法( ATCOR3、FLAASH 以及查找表) 和两种分类算法。在分类实验部分,根据样本的地理坐标在 3景校
正影像中分别采集训练样本并训练各自的分类器,并交叉用于其他辐射校正影像的土地覆盖遥感分类。实验结果
表明: ( 1) 用于分类器训练的样本采集自待分类影像时的分类精度明显高于采集自其他影像的分类精度; ( 2)3种
辐射校正影像的分类结果存在差异,其中使用 ATCOR3 和FLAASH 方法校正后影像的分类结果有更相近的精度;
(3) 辐射校正对分类类别的影响不同,其中对森林类型影响最大,对裸地等其他类别影响相对较小。
关键词: 辐射校正,Landsat TM,MLC,SVMs,土地覆盖遥感分类,精度评价
中图分类号: TP79 文献标志码: A
引用格式: 陈趁新,胡昌苗,霍连志,唐娉. 2014. Landsat TM 数据不同辐射校正方法对土地覆盖遥感分类的影响.遥感学报,1
8(2) : 320 - 334
Chen C X,Hu C M,Huo L Z and Tang P. 2014. Effect of different radiation correction methods of Landsat TM d
ata on land-cover remote sensing classification. Journal of R emote Sensing,1 8(2) : 320 - 334 [DOI:10. 11834 /
jrs. 20143211]
收稿日期:2013-08-26;修订日期:2013-11-27;优先数字出版日期: 2013-12 -04
基金项目:国家高技术研究发展计划( 863 计划) ( 编号: 2009AA122002)
第一作者简介:陈趁新( 1983—) ,男,助理研究员,研究方向为遥感图像处理。E-mail:chencx@ csu. ac. cn
通信作者简介:唐娉( 1968—) ,女,研究员,现从事遥感图像处理。E-mail:tangping@ radi. ac. cn
1引 言
近几十年来,基于 Landsat TM 数据的遥感影像
分类和变化检测技术被广泛应用于土地覆盖制图
和变化情况监测研究( Fox 等,1983;Jensen 等,
1993;Yuan 等,2005 ;Sexton 等,2013 )。然而,影像
数据在生成过程中受到多种因素的影响,使得这些
数据用于土地覆盖研究时存在着各种不确定性。
主要的影响包括太阳光照条件变化、大气条件差异
以及山区的复杂地形条件等,其中大气中的分子和
气溶胶的散射与吸收,使卫星传感器接收到的地物
反射的电磁波信号在从地球表面经过大气层传输
的过程中发生了改变( Song 等,2001) ,传感器记录
的影像数据与地表实际情况不相符,对信息提取精
度造成了影响。因此,在应用前对 Landsat TM 数据
进行辐射校正,包括大气校正和地形校正,以提高
影像的质量( Honkavaara 等,2012) 已经成为通识。
用于多时相( Zhang 等,2006 ;Vicente - Serrano
等,2008;Tan 等,2012 ;Xu 等,2012)、多传感器( Xu
和Zhang,2013) 以及大尺度( Olthof 等,2005;Hansen
和Loveland,2012) 遥感数据分析的辐射校正的方
法较多。研究表明,经过辐射校正可以提取更加精
确的地物信息,比如生态应用( Gu 等,20 11 ) 和土
地利用和覆盖分类( Meyer 等,1993;Pons 和Sole-
sugranes,1994;Colby 和Keating 1998;Dorren 等,
2003;Kobayashi 和Sanga-Ngoie,2009;Habib 等,
2011)。然而,对于遥感影像经过不同辐射校正方
法校正后对土地覆盖遥感分类结果影响的研究
较少。
本文以 Landsat TM 数据为例,初步探索不同辐
328 Journal of Remote Sensing 遥感学报 2014,18(2)
射校正方法对土地覆盖遥感分类精度的影响。研
究的过程如下: 首先利用 3种不同辐射校正方法
(ATCOR3,FLAASH 以及查找表) 对图像进行辐射
校正,然后在一个校正结果上选择样本分别用于其
他校正结果进行土地覆盖分类,比较分类的精度。
在比较时,训练样本和检查样本的位置和数量均保
持不变。分类算法分别是最大似然 MLC 和支持向
量机 SVMs。
2研究区域与数据
2. 1 研究区域
本文选择陕西省中北部山区作为研究区域
( 图 1)。该地区地处黄土高原,气候特点为寒冷干
旱或半干旱。研究区域内分布有大量山体及一些
较小的丘陵地貌,区域内分布较为广泛的土地覆盖
类型包括森林,裸土和农田,在平坦的低洼地区,农
田与分散的农村居民点呈现混合分布状态,陡峭的
山区地形主要以森林类型分布为主。
2. 2 数据
本文使用的数据包括经过几何校正处理的原
始Landsat TM 数据,以及经过 3种辐射校正方法校
正处理的数据,空间分辨率均为 30 m。数据轨道条
带号:127,行编号: 35,获取时间为 2000 - 05 - 12,
整个区域内无云,可视性较好。
图1研究区域及实验数据( R、G、B分别使用近红外、红和绿波段显示)
2. 3 分类体系及样本选择
本文的分类体系和类别定义参考了 Gong 等人
(2013) 使用 30 m 分辨率的 Landsat TM 数据对全球
的土地覆盖进行制图研究中的内容,并结合试验区
土地覆盖的特点,将该区域土地覆盖类别分为 6类,
包括: 裸土( bare lands)、农田( croplands)、森林( for-
est)、不透水层( impervious areas )、水 体 ( water
bodies) 和山体。
表1给出了6类土地覆盖类别的特征描述,以
及在 Landsat TM 影像和 Google Earth 中的视觉表
现。为了获取高质量且有代表性的样本数据。样
本选取遵循在整个研究区域内均衡分布的原则。
首先,基于 Landsat TM 整景影像目视判读地物类别
分布的空间位置,再同步定位到具有更高分辨率的
Google Earth 影像中进行“实地”验证,据此获取了
大量的高质量样本集; 其次,对样本进行了优化,去
除重复和非典型的样本点; 最后,按照一定比例将
样本集随机的拆分为训练样本和测试样本。6个地
类的训练样本共2445 个,检验样本共 477 个。关于
样本情况详见表1。
陈趁新 等: Landsat TM 数据不同辐射校正方法对土地覆盖遥感分类的影响 329
3辐射校正
本文对 Landsat TM 数据采用以下 3种不同的
辐射校正方法处理: FLAASH( [2013 - 08 - 01]ht-
tp:/ / www. eselisvis. com / docs / FLAASH. html )、AT-
COR3( [2013 - 08 - 01]http:/ / www. rese . ch / pdf / at-
cor3_manual. pdf) 与传统的查找表 LUT(LookUp Ta-
ble) 方法( Liang 等,1997) ,这 3种辐射校正是目前
Landsat TM / ETM + 数据辐射处理被广泛采用的手
段。其中 FLAASH 与ATCOR3 是目前有代表性的
商用大气校正软件模块,支持目前绝大多数多光
谱、高光谱遥感数据的大气校正处理,两者采用的
辐射传输理论存在一些差异; 查找表方法一直被研
究者大量采用,大气校正处理上具有很大的灵活性。
FLAASH 模型是由 Spectral Science 公司、美国
空气动力研究实验室( AFRL) 与波谱信息技术应用
中心( SITAC) 联合开发的大气校正软件包,工作的
波段范围是 400—2500 nm,利用 MODTRAN 4 生成
一系列的大气参数查找表,核心的辐射传输模型考
虑了邻近效应,但目前没有考虑地形校正。
ATCOR 是由德国宇航中心发展起来的辐射校
正软件,包括 ATCOR2、ATCOR3 和ATCOR4 3 个版
本( Richter,2005 ) ,其中 ATCOR2、ATCOR3 已经集
成于全球用户群最大的遥感软件 ERDAS 中,本文
采用 ATCOR3 处理 Landsat TM 数据。ATCOR3 主
要是对山区的卫星遥感影像进行耦合大气和地形
的辐射校正。ATCOR3 预先将 MODTRAN 4 计算的
各种大气状况下的大气透过率、程辐射、直射和散
射太阳辐射通量存储为查找表。ATCOR3 在进行地
形纠正时,能利用自带的模块计算坡度、坡向、天空
可视因子、山区阴影等数据。对于入射角较大的区
域,ATCOR3 提供了一种可靠的双向反射分布函数
BRDF 经验纠正函数进行地物的 BRDF 校正。A
TCOR3采用不同的算法可以同时进行可见光和红外
波段的辐射校正,与此同时,它还提供了薄云、薄雾
去除模块。
基于查找表 LUT 的大气校正假设地表为朗伯
体,采用的辐射传输公式如下:
L = L p+ T Eρ
π(1-ρS)(1)
式中,L为表观辐射亮度,Lp为大气程辐射,
ρ为地
表反射率,
E为地表辐照度,S为大气反照率,T为地
表到大气顶层的透过率。使用 6S(Second Simulation
of Satellite Signal in the Solar Spectrum) ( Vermote 等,
1997) 可直接计算出参数 E、T、Lp与S,于是,已知 L
情况下便可计算出 ρ。
Landsat TM 数据的查找表算法参考 Liang 等人
(1997) 的做法自主开发的,即采用暗植被法( DDV)
(Kaufman 等,1997) 估算暗植被处的气溶胶厚度,并
330 Journal of Remote Sensing 遥感学报 2014,18(2)
采用移动窗口内插的方式估算整景数据气溶胶的
分布。
查找表大气校正后的地形校正采用的是一种
基于地形抹平的半经验校正方法,该方法首先通过
对地形的坡度角进行抹平处理,然后基于抹平后的
地形采用 COS( 余弦) 校正完成地形辐射校正,简称
为SCOS 校正( Smoothed COS Correction )。地形抹
平处理用来消除地表非朗伯效应造成的校正辐射
异常,也有压制因地形数据异常造成的影响的
作用。
大气校正采用的高程数据( DEM) 为 90 m 空间
分辨率的 Shuttle Radar Topography Mission(SRTM)
数据( 版本 SRTM4. 1 )。
4分类实验与结果分析
为了评价不同辐射校正方法对土地覆盖遥感
分类结果的影响,共设计了 9组实验,分别从3景辐
射校正影像中提取训练样本用于 3景影像的分类。
每组实验分别使用 MLC 和SVMs 作为分类器进行
分类实验。
4. 1 分类器
遥感影像分类算法较多。有监督分类和非监
督分类,参数分类和非参数分类,软分类和硬分类
的分类方法,或者基于像元、亚像元和对象的分类
方法等( Lu 和Weng,2007 )。本文选用两种目前广
泛应用的分类算法,即 MLC 和SVMs,作为辐射校正
方法对分类精度评价分类器。
MLC 是一种基于统计理论的参数分类器,是广
泛使用的分类器之一( Huang 等,2002)。在遥感分
类方法中,MLC 具有明确的参数解释能力、易于集
成先验知识以及易于实现等算法优势。根据统计
参数( 均值向量和方差—协方差矩阵) ,计算出像元
属于每个预定义类别的概率,并将该像元划分到概
率最高的类别完成分类( Tso 和Mather,2009 )。
SVMs 是一种基于结构风险最小化原则的分类算
法,并以其独有的特性和高效性使得该分类器在
遥感分类领域具有较高的知名度( Lorenzo 和L
orenzo,2006)。为不失一般性,本文选用径向基函
数Radial Basis Function(RBF ) 作为 SVMs 的核函
数,并在模型训练过程中采用交叉验证的方法选
择RBF 的模型参数。在处理过程中将数据归一化
到[0,1]之间,实验中使用 LIBSVM 库( Chang 和
Lin,2011 )。
4. 2 实验流程
图2给出了实验流程。首先使用3种不同辐射
校正方法对原始 TM 影像进行辐射校正,得到辐射
校正影像 RC_A、RC_B 和RC_C,并作为下一步处
理的数据源; 其次,根据同一地理坐标内的样本区
域数据,分 别在上述 3景影像中独立采集样本
(Samp_a、Samp_b、Samp _c) ,并分别训练得到分类
器( MLC 和SVMs) ; 最后,使用这些分类器分别用于
每景影像的土地覆盖遥感分类。对分类结果的精
度评价通过一系列测试样本的混淆矩阵得出,并以
生产者精度、使用者精度、总体精度以及 Kappa 系数
等作为分类结果精度评价的统计量。
图2实验流程
4. 3 分类结果
图3给出了使用 MLC 和SVMs 两种分类器在
各组实验中的部分分类结果。其中图3(a) 为当样
本采集自 RC_A 影像时训练得到的 MLC 对RC _ A
影像进行分类的结果; 图 3(b) 为当样本采集自 RC_
C影像时训练得到的 SVMs 对RC_C 影像进行分类
的结果。
各组实验的总体精度 OA 和Kappa 系数统计如
表2所示。通过表 2可以看出,样本采集策略对分
类精度的影响,即当样本采集影像和分类影像为同
一景影像时分类精度最高。此外,3景辐射校正影
像之间的分类结果存在一定差异,其中 RC_ A(AT-
COR3) 和 R C_B(FLAASH) 方法校正图像的分类结
果有更相近的精度,而辐射校正影像 RC _C 的分类
精度与前两种方法存在差别。
陈趁新 等: Landsat TM 数据不同辐射校正方法对土地覆盖遥感分类的影响 331
图3部分分类结果图
表2辐射校正后不同样本采集策略与分类精度结果统计( MLC,SVMs)
样本 RC_A RC_B RC_C
OA / % Kappa OA / % Kappa OA/ % Kappa
RC_A 93. 29 ,93 . 50 0. 920,0. 922 91. 61 ,88. 26 0 . 899,0. 859 77 . 57,88 . 05 0. 731 ,0. 857
RC_B 90. 36 ,87. 63 0 . 884,0. 851 93 . 29,93 . 50 0 . 920,0. 922 74. 21 ,88. 05 0. 691,0 . 857
RC_C 78. 05 ,84. 49 0 . 702,0. 814 79 . 20,85 . 95 0 . 750,0. 831 91. 20 ,93. 71 0. 894,0 . 925
分类精度较高的实验组合是当样本采集影像
和分类影像相同时的分类结果。表3给出了当样本 采集自 3景辐射校正影像自身并训练 MLC 和SVMs
后对各自影像分类时的分类精度。
表3各组实验中分类精度较高的类别精度统计( MLC,SVMs)
类名 RC_A—RC_A RC_B—RC_B RC_C—RC_C
生产精度/% 使用精度/% 生产精度/% 使用精度/ % 生产精度/ % 使用精度/ %
裸土 90. 00 ,90. 00 91. 14 ,90. 00 90. 00,88. 75 87. 80 ,88. 75 88. 75 ,91. 25 85. 54 ,87. 95
农田 95. 00 ,95. 00 96. 20 ,96. 20 93. 75,95. 00 97. 40 ,98. 70 85. 00 ,96. 25 98. 55 ,97. 47
森林 96. 25 ,98. 75 100. 0 ,98. 75 97. 50,98. 75 98. 73 ,97. 53 97. 50 ,97. 50 98. 73 ,98. 73
山体 91. 25 ,92. 50 82. 02 ,84. 09 90. 00,91. 25 83. 72 ,82. 95 86. 25 ,90. 00 82. 14 ,85. 71
不透水层 90. 00 ,90. 00 92. 31,93. 51 91. 25 ,92. 50 93. 59 ,94. 87 92. 50 ,92 . 50 85 . 06,93 . 67
水体 97. 40 ,94. 81 100. 0 ,100. 0 97. 40,94. 81 100. 0 ,100. 0 97. 40 ,94. 81 100. 0 ,100. 0
总体精度/ % 93. 29 ,93. 50 93. 29 ,93. 50 91. 20 ,93. 71
Kappa 0. 920 ,0. 922 0. 920 ,0. 922 0. 894 ,0. 925
从图 4可以看出,各地物类别在经过 3种辐射
校正后的地表反射率( 进行了整型变换) 的变化情
况。3种辐射校正方法得到的结果比较接近,尤其
是RC_A 和RC_B,而 RC_C 的结果与前两种校正方
法稍有区别。尤其是第 1波段,LUT 辐射校正后各
地类的地表反射率值明显低于 FLAASH 和ATCOR
方法。
比较辐射校正后影像的分类结果可以看出,土
地覆盖类别的精度均有不同程度的变化。对于具
体地物类别,如图 5所示,森林类别在辐射校正后分
类精度变化较为明显,表明辐射校正对该类别的影
响较大。这可能与辐射校正均采用暗植被法进行
气溶胶厚度估计的方法相关,但详细原因需要更进
一步分析。
332 Journal of Remote Sensing 遥感学报 2014,18(2)
图4辐射校正后各个地物类别在各波段上 DN 值的变化
图5辐射校正后不同地物类别精度变化
( 以 MLC 分类结果为例)
5结 论
对于遥感应用领域来说,辐射校正是开展进一
步工作的前提条件。本文在第3节介绍了3种不同
的辐射校正方法并应用于 Landsat TM 数据的辐射
校正。本文使用 MLC 和SVMs 进行了 9组实验的
比较,包括从 3景辐射校正影像中提取训练样本并
分别用于3景影像的分类,以此探索基于不同辐射
方法校正后不同样本采集策略对土地覆盖遥感分
类精度的影响。
通过本文实验结果,初步得到以下结论:
(1) 样本采集影像与分类影像相同时分类精度
最高。例如使用 MLC 分类时,RC _ A—RC _ A 组合
的总体分类精度为 93. 29% ,RC _B—RC _ B 组合和
RC_C—RC _ C 组合总体精度分别为 93. 29% 和9
1. 20 % ,均明显高于同组其他分类组合的分类精度;
(2) 从分类结果来看,在不同辐射校正方法的
影像上进行分类结果之间存在差异,其中,ATCOR3
和FLAASH 方法校正影像的分类结果有更相近的
精度。值得注意的是,RC_C 的分类总体精度与前
两者( RC_A 和RC _B) 差异较为明显,表明对于本
文实验数据,
ATCOR3 与FLAASH 的校正结果较为
一致,而自主开发的查找表算法的结果与之有一定
的偏差,这种偏差很可能是由于气溶胶分布估算误
差的影响造成的( 图4) ;
陈趁新 等: Landsat TM 数据不同辐射校正方法对土地覆盖遥感分类的影响 333
(3) 辐射校正对每个类别的分类精度影响不
同,受影响最大的是“森林”类别,其他类别受影响
的程度各有不同。值得注意的是,SVMs 分类时的
总体精度和稳定性均高于 MLC,二者分类精度的变
化趋势类似。
本文研究区域较小,相对全球范围内各种复
杂地形地貌和土地覆盖类别来说,本文研究存在
一定的局限性,下一步将以本文研究结果为基础,
逐步扩大研究范围。另外本文的结果没有体现出
地形校正对于分类精度的影响,后续的研究将从
样本选择和实验流程设计等多方面进一步展开
研究。
志 谢 本文的样本数据选取得到了中国科
学院遥感与数字地球研究所赵理君、丁玲、周增光
和张爱英等同学的大力协助,在此表示衷心的
感谢!
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