Full text: Proceedings, XXth congress (Part 2)

  
AREA ACCURACY ASSESSMENT FOR 
CHINESE LAND-USE DYNAMIC DETECTION OF REMOTE SENSING PROGRAM 
SUN Xiao-xia* ZHANG Ji-xian^" LI Hai-tao? 
“Chinese Academy of Surveying and Mapping, Haidian District: Beijing 100039, sun.xiaoxia@163.com,lhtao@163.net 
? Lab. Of Land Use, The Ministry of Land Resources, Haidian District Beijing, 100029, stecsm@public.bta.net.cn 
KEY WORDS: Remote sensing, Land Use, Change Detection, Sampling, Accuracy , Monitoring 
ABSTRACT: 
A suitable area accuracy assessment scheme for change detection is presented. The technique consists of two parts which are the 
necessary stages of accuracy assessment:(l)sampling method and (2)accuracy assessment. A area stratified random sampling is 
applied based on the fact that area errors is related to a certain area range (called stratum in following sampling). The approach 
involves subdividing the area range into strata,and within each stratum a spectific number of sample polygons are randomly chosen. 
Three experiments about polygons of remote sensing monitoring have been done which aim at finding out the distribution law of 
area error,the conclusions that the error of polygon area follows normal distribution and the mean is zero are drawed.This give 
accuracy assessment of remote sensing-derived change detection a science foundation. Based on relative error theory and error 
spread law ,three area accuracy indices including relative root mean square,relative error,and average relative error are built.Using 
these indices,the formulas of single polygon error and the whole monitoring region error are produced. Experimental results and 
practical work show that the method in this paper is simple, effective and applicable. 
1. INTRODUCTION 
  
  
  
  
  
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1.1 General Instructions 0 
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The Ministry of Land and Resources P.R.China is conducting a 0 EU n 
continuous Land use Dynamic Detection project using 0 050m 
Thematic Mapper(TM) 30-meter and SPOT(10-meter or 5 - 
: pn at nes ut 40 -25 -10 5 20 35 850 65 
meter)data.The objective of this large project is providing the Lt 
C : ze : S : relative error % 
government a generalized, consistent, and reliable land use Y : à s 
change data every year. The accuracy of product is very Figurel. The histogram of relative error. 
important not only for users but also for makers, and how to 
assess the accuracy of remote sensing-derived change detection 
is the focus problem currently. 
The rest of the paper is orgnized as follows. We carry out the 
experiments about area error of polygons of remote sensing 
monitoring in section 2.In section 3, We present area stratified 
random sampling technique and an approach to calculate 
sample size. In section 4, the area accuracy indices are built and 
an example of area accuracy assessment is given in section 5.In n 
section 6, We conclude with discussions. T- X—H, (1) 
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2.2 Mean Test 
In order to test whether the mean of relative error is zero, or 
whether the error is systematic, we make a hypothesis testing 
on monitoring polygons from a variety of images. We use 
normal population test technique and construct statistic: 
2. EXPERIMENTS ON ERROR DISTRIBUTION 
Three experiments are made as follows, aiming at finding out Original — hypothesis H p=p,=0 , alternative 
0 
  
the distribution law of area error. hypothesis H, : t 0, rejective range: IT| 2t 0-0, a 
2.1 Histogram Experiment 0.01. The result is shown in Table 2. 
196 polygons from the monitoring results using radarsat image image n u S ITI i {n=} 
of Beiling region are sampled. We divided the sample polygons 
into four stratums according to their areas there are 75 ones 
  
  
  
  
  
  
  
  
  
  
  
: Ir SPOT(10m) 25 0.03683 15.9% 11553 2.7969 
under 10mu , 43 ones within 10~20 mu, 50 ones within 20~50 IRS(S.8m) 26 0.03508 1 115% | 15552 27874 
mu, 28 ones above 50 mu. In each stratum , relative error is IKONOS(1m) 40. | 001206 | 46% | 16444 | 2.7079 
calculated. The histogram of relative error is shown in Figure |. SAR(6.25m) 31 0.04300 | 163% | 1.4688 2.7500 
Clearly, Figl displays normal distribution law of area errors in 
every area range. Table2. The result of mean test. 
  
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