Remote Sensing Classification Accuracy

Slides:

Advertisements

Similar presentations

Training Fields Parallel Pipes Maximum Likelihood Classifier Class 11. Supervised Classification.

Advertisements

Categorical Data Analysis

Accuracy Assessment of Thematic Maps

AA_Vis_1. Students examine both the “true color” And “false-color Infrared” images provided by GLOBE AA_Vis_2.

Analysis of frequency counts with Chi square

Agreement at Multiple Resolutions for Real and Categorical Maps Chris Ayres – George Kariuki Kristopher Kuzera

Credibility: Evaluating what’s been learned. Evaluation: the key to success How predictive is the model we learned? Error on the training data is not.

12.The Chi-square Test and the Analysis of the Contingency Tables 12.1Contingency Table 12.2A Words of Caution about Chi-Square Test.

Accuracy Assessment Chapter 14. Significance Accuracy of information is surprisingly difficult to address We may define accuracy, in a working sense,

Lecture 14: Classification Thursday 18 February 2010 Reading: Ch – 7.19 Last lecture: Spectral Mixture Analysis.

AA_Vis_1. Using the software “MultiSpec,” students follow protocols to prepare a “clustered” image of their GLOBE Study Site.

Lecture 14: Classification Thursday 19 February Reading: “Estimating Sub-pixel Surface Roughness Using Remotely Sensed Stereoscopic Data” pdf preprint.

February 15, 2006 Geog 458: Map Sources and Errors

Copyright, © Qiming Zhou GEOG1150. Cartography Quality Control and Error Assessment.

Global Land Cover: Approaches to Validation Alan Strahler GLC2000 Meeting JRC Ispra 3/02.

Image Classification: Redux

Accuracy Assessment. 2 Because it is not practical to test every pixel in the classification image, a representative sample of reference points in the.

Chapter 9 Accuracy assessment in remotely sensed categorical information 遥感类别信息精度评估 Jingxiong ZHANG 张景雄 Chapter 9 Accuracy assessment in remotely sensed.

Methods of Validating Maps of Deforestation and Selective Logging Carlos Souza Jr. Instituto do Homem e Meio Ambiente da Amazônia—Imazon.

Ten State Mid-Atlantic Cropland Data Layer Project Rick Mueller Program Manager USDA/National Agricultural Statistics Service Remote Sensing Across the.

Image Classification: Accuracy Assessment

1 Evaluating Model Performance Lantz Ch 10 Wk 5, Part 2 Right – Graphing is often used to evaluate results from different variations of an algorithm. Depending.

Classification & Vegetation Indices

For testing significance of patterns in qualitative data Test statistic is based on counts that represent the number of items that fall in each category.

Image Classification Digital Image Processing Techniques Image Restoration Image Enhancement Image Classification Image Classification.

1 Remote Sensing and Image Processing: 6 Dr. Mathias (Mat) Disney UCL Geography Office: 301, 3rd Floor, Chandler House Tel:

Summer Session 09 August Tips for the Final Exam Make sure your answers clear, without convoluted language. Read questions carefully – are you answering.

Image Classification 영상분류

Accuracy Assessment Having produced a map with classification is only 50% of the work, we need to quantify how good the map is. This step is called the.

Height Growth [m/3yr] An example: Stand biomass estimation by LiDAR.

1 In this case, each element of a population is assigned to one and only one of several classes or categories. Chapter 11 – Test of Independence - Hypothesis.

Accuracy of Land Cover Products Why is it important and what does it all mean Note: The figures and tables in this presentation were derived from work.

1 Joint Research Centre (JRC) Using remote sensing for crop and land cover area estimation

CHI SQUARE TESTS.

Chuvieco and Huete (2009): Fundamentals of Satellite Remote Sensing, Taylor and Francis Emilio Chuvieco and Alfredo Huete Fundamentals of Satellite Remote.

Error & Uncertainty: II CE / ENVE 424/524. Handling Error Methods for measuring and visualizing error and uncertainty vary for nominal/ordinal and interval/ratio.

Evaluating Results of Learning Blaž Zupan

Lecture 20 – review Labs: questions Next Wed – Final: 18 March 10:30-12:20 Thursday, 12 March.

1 Chi-square Test Dr. T. T. Kachwala. Using the Chi-Square Test 2 The following are the two Applications: 1. Chi square as a test of Independence 2.Chi.

1 Estimation of Diffused pollution loads declination by purchasing Land of Riparian buffer zone assigned to Dae-cheong water resource area using Remote.

APPLIED REMOTE SENSING TECHNOLOGY TO ANALYZE THE LAND COVER/LAND USE CHANGE AT TISZA LAKE By Yudhi Gunawan * and Tamás János ** * Department of Land Use.

1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Chapter 12 Tests of Goodness of Fit and Independence n Goodness of Fit Test: A Multinomial.

Environmental Modeling Validating GIS Models. 1. A Habitat Model Issues: ► Mapping Florida Scrub Jay habitat in the Kennedy Space Center in the Kennedy.

Housekeeping –5 sets of aerial photo stereo pairs on reserve at SLC under FOR 420/520 –June 1993 photography.

Statistics 300: Elementary Statistics Section 11-3.

Part 4: Contextual Classification in Remote Sensing * There are different ways to incorporate contextual information in the classification process. All.

Accuracy Assessment Accuracy Assessment Error Matrix Sampling Method

Potential impacts of map error on land cover change detection Nick Cuba Clark University 2/25/12 1.

Accuracy Assessment of Thematic Maps THEMATIC ACCURACY.

26. Classification Accuracy Assessment

Chapter 11 – Test of Independence - Hypothesis Test for Proportions of a Multinomial Population In this case, each element of a population is assigned.

26. Classification Accuracy Assessment

HIERARCHICAL CLASSIFICATION OF DIFFERENT CROPS USING

Accuracy Assessment of Thematic Maps

Class 12 Assessment of Classification Accuracy

Statistical Techniques

Incorporating Ancillary Data for Classification

By Yudhi Gunawan * and Tamás János **

The GISCO task force “Remote Sensing for Statistics”

REMOTE SENSING Multispectral Image Classification

Chi Square Two-way Tables

Housekeeping 5 sets of aerial photo stereo pairs on reserve at SLC under FOR 420/520 June 1993 photography.

Assessment of data quality

pub/ hurtado/5336

network of simple neuron-like computing elements

Lecture 20 – review Thursday, 11 March 2010 Labs: questions

ALI assignment – see amended instructions

Inference for Two Way Tables

Overall Classification Accuracy = 87.86%

Inference for Two-way Tables

Presentation transcript:

Remote Sensing Classification Accuracy

1. Select Test Areas Selecte test areas in an image to evaluate the accuracy of a classification Test areas should be representative categorically and geographically Sampling methods: uniform wall-to-wall, random, stratified random sampling Sample size: 50 - 100 pixels each category

http://aria.arizona.edu/slg/Vandriel.ppt

2. Error Assessment A classification is not complete until its accuracy is assessed Error matrix KHAT statistics

Error Matrix Also called confusion matrix and contingency table Compares the ground truth and the results of the classification for the test areas Can be used to evaluate the result of classifying the training set pixels and the results of classifying the actual full-scene

Error Matrix Diagonal cells are correctly classified pixels Classified Reference Data Data Water Sand Forest Urban Corn Hay Row Total Water 480 0 5 0 0 0 485 Sand 0 52 0 20 0 0 72 Forest 0 0 313 40 0 0 353 Urban 0 16 0 126 0 0 142 Corn 0 0 0 38 342 79 459 Hay 0 0 38 24 60 359 481 Col Total 480 68 356 248 402 438 1992 Diagonal cells are correctly classified pixels correctly classified pixels 1672 Overall accuracy = ------------------------------- = ------- = 84% total pixels evaluated 1992

Error Matrix Classified Reference Data Data Water Sand Forest Urban Corn Hay Row Total Water 480 0 5 0 0 0 485 Sand 0 52 0 20 0 0 72 Forest 0 0 313 40 0 0 353 Urban 0 16 0 126 0 0 142 Corn 0 0 0 38 342 79 459 Hay 0 0 38 24 60 359 481 Col Total 480 68 356 248 402 438 1992 In this case, the non-diagonal column cells are omission errors e.g. omission error for forest = 43/356 = 12% The non-diagonal row cells are commission errors e.g. commission error for corn 117/459 = 25%

Error Matrix Omission error = 1 (100%) - producer's accuracy Classified Reference Data Data Water Sand Forest Urban Corn Hay Row Total Water 480 0 5 0 0 0 485 Sand 0 52 0 20 0 0 72 Forest 0 0 313 40 0 0 353 Urban 0 16 0 126 0 0 142 Corn 0 0 0 38 342 79 459 Hay 0 0 38 24 60 359 481 Col Total 480 68 356 248 402 438 1992 correctly classified in each category producer's accuracy = ---------------------------------------------- the total pixels used in the category (col total) Omission error = 1 (100%) - producer's accuracy

Error Matrix Commission error = 1 (100%) - user's accuracy Classified Reference Data Data Water Sand Forest Urban Corn Hay Row Total Water 480 0 5 0 0 0 485 Sand 0 52 0 20 0 0 72 Forest 0 0 313 40 0 0 353 Urban 0 16 0 126 0 0 142 Corn 0 0 0 38 342 79 459 Hay 0 0 38 24 60 359 481 Col Total 480 68 356 248 402 438 1992 correctly classified in each category user's accuracy = ------------------------------------------------------- the total pixels used in the category (row total) Commission error = 1 (100%) - user's accuracy

KHAT Statistics A measure of the difference between the actual agreement between reference data and the results of classification, and the chance agreement between the reference data and a random classifier

KHAT Statistics ^ observed accuracy - chance agreement k = -------------------------------------------------- 1 - chance agreement The KHAT value usually ranges from 0 to 1 0 indicates the classification is not any better than a random assignment of pixels 1 indicates that the classification is 100% improvement from random assignment

KHAT Statistics r r N × S xii - S (xi+ × x+i) ^ i=1 i=1 k = ----------------------------------- r N2 - S (xi+ × x+i) i=1 r - number of rows in the error matrix xii - number of obs in row i and column i (the diagonal cells) xi+ - total obs of row i x+i - total obs of column i N - total of obs in the matrix

KHAT

KHAT Statistics KHAT considers both omission and commission errors

Readings Chapter 7