Accuracy assessment for the satellite image classification

2008

In order to develop forest management strategies in tropical forest in Malaysia, surveying the forest resources and monitoring the forest area affected by logging activities is essential. There are tremendous effort has been done in classification of land cover related to forest resource management in this country as it is a priority in all aspects of forest mapping using remote sensing and related technology such as GIS. In fact classification process is a compulsory step in any remote sensing research. Therefore, the main objective of this paper is to assess classification accuracy of classified forest map on Landsat TM data from difference number of reference data (200 and 388 reference data). This comparison was made through observation (200 reference data), and interpretation and observation approaches (388 reference data). Five land cover classes namely primary forest, logged over forest, water bodies, bare land and agricultural crop/mixed horticultural can be identified by the differences in spectral wavelength. Result showed that an overall accuracy from 200 reference data was 83.5 % (kappa value 0.7502459; kappa variance 0.002871), which was considered acceptable or good for optical data. However, when 200 reference data was increased to 388 in the confusion matrix, the accuracy slightly improved from 83.5% to 89.17%, with Kappa statistic increased from 0.7502459 to 0.8026135, respectively. The accuracy in this classification suggested that this strategy for the selection of training area, interpretation approaches and number of reference data used were importance to perform better classification result.

Engineering & Technology Review

Assessing the accuracy of the classification map is an essential area in remote sensing digital image process. This is because a poorly classified map will result in inestimable errors of spatial analysis and modeling arising from the use of such data. This study was designed to evaluate different supervised classification algorithms in terms of accuracy assessment with a view of recommending an appropriate algorithm for image processing. The analysis was carried out using Andoni L.G.A. Rivers State, Nigeria as the study area. Supervised classification of ETM+ 2014 Landsat image of the study area was carried out using ENVI 5.0 software. Seven land use/land cover categories were identified on the image data and appropriate information classes were also assigned using region of interest. The classifiers adopted for the study include SAM, SVM, and MDC and each classifier was set using appropriate thresholds and parameters. The output error matrix of the classified map produced overall ...

Springer eBooks, 2023

This chapter will enable you to assess the accuracy of an image classification. You will learn about different metrics and ways to quantify classification quality in Earth Engine. Upon completion, you should be able to evaluate whether your classification needs improvement and know how to proceed when it does. • Learning how to perform accuracy assessment in Earth Engine. • Understanding how to generate and read a confusion matrix. • Understanding overall accuracy and the kappa coefficient.

1981

A working conference was held in Sioux Falls, South Dakota November 12-14, 1980 dealing with Landsat classification Accuracy Assessment Procedures. Thirteen formal presentations were made on three general topics: (1) sampling procedures, (2) statistical analysis techniques, and (3) examples of projects which included accuracy assessment and the associated costs, logistical problems and value of the accuracy data to the remote sensing specialist and the resource manager. Nearly twenty conference attendees participated in two discussion sessions addressing various issues associated with accuracy assessment. This paper presents an account of the accomplishments of the conference.

IEEE Digital Library, 2016

Classification of Land Use Land Cover (LULC) data from satellite images is extremely remarkable to design the thematic maps for analysis of natural resources like Forest, Agriculture, Water bodies, urban areas etc. The process of Satellite Image Classification involves grouping the pixel values into significant categories and estimating areas by counting each category pixels. Manual classification by visual interpretation technique is accurate but time consuming and requires field experts. To overcome these difficulties, the present research work investigated efficient and effective automation of satellite image classification. Automated classification approaches are broadly classified in to i) Supervised Classification ii) Unsupervised Classification iii) Object Based Classification. This paper presents classification capabilities of K-Means, Parallel Pipe and Maximum Likelihood classifiers to classify multispectral spatial data. Using statistical inference, classified results are validated with reference data collected from field experts. Among three, Maximum Likelihood classifier (MLC) inflate a extensive credit with Overall accuracy and Kappa Factor.

International Journal of Remote Sensing, 2009

The classification accuracy statement is the basis of the evaluation of a classification's fitness for purpose. Accuracy statements are also used for applications such as the evaluation of classifiers, with attention focused especially on differences in the accuracy with which data are classified. Many factors influence the value of a classification accuracy assessment and evaluation programme. This paper focuses on the size of the testing set(s), and its impacts on accuracy assessment and comparison. Testing set size is important as an inappropriately large or small sample could lead to limited and sometimes erroneous assessments of accuracy and of differences in accuracy. In this paper the basic statistical principles of sample size determination are outlined. Some of the basic issues of sample size determination for accuracy assessment and accuracy comparison are discussed. With the latter, the researcher should specify the effect size (minimum meaningful difference), significance level and power used in an analysis and ideally also fit confidence limits to estimates. This will help design a study as well as aid interpretation. In particular, it will help avoid problems such as under-powered analyses and provide a richer information base for classification evaluation. Central to the argument is a discussion of Type II errors and their control. The paper includes equations that could be used to determine sample sizes for common applications in remote sensing, using both independent and related samples.

2012

The error matrix is the most common way of expressing the accuracy of remote sensing image classifications, such as land cover. However, it and the measures that can be calculated from it have been criticised for not providing any indication of the spatial distribution of errors. Other research has identified the need for methods to analyse the spatial non-stationarity of error and to visualise the spatial variation in classification uncertainty.

1980

2014

SUMMARY Image classification is an important operation in remotely sensed data analysis. It involves the extraction of identified features and features of interest into themes or classes. The final map resulting from classification exercise is called thematic map. Both the raw data and final output ‐ thematic maps are susceptible to machine and human errors. Therefore, the level at which a classified map represents the reality it portrays remains uncertain until its accuracy is determined.Accuracy assessment is the measurement of the rate and level to which classified image agrees with the reference (ground) data it represents.Accuracy of any image classification may be tested in four different ways - field checks at selected points, map overlays, statistical analysis of numerical data, and using confusion matrix calculations. The confusion matrix is the most widely used measure of image classification accuracy assessment. It is a simple cross-tabulationof the mapped class label aga...

International Conference on Information and Communication Technology for Intelligent Systems,Springer , 2020

In this work, we are creating a system to classify satellite images in order to extract information using image processing techniques. Classification of satellite images into used and unused areas and also subclassing of each of the classes into four different classes has been carried out. Used satellite images further classified into residential, industries, highways, crop lands, and unused images are classified further into forest, river, deserts, and beaches. Manual classification by using image interpretation technique requires more time and field experts. So in our work, we focused with efficient automatic satellite image classification. Convolutional neural network is used for feature extraction and classification of satellite images. CNN is a deep neural networks which is most suitable when we deal with images. CNN will help to provide higher classification accuracy. Confusion matrix is used to estimate the overall classification accuracy.