Share. When a maximum likelihood classification is performed, an optional output confidence raster can also be produced. Take care in asking for clarification, commenting, and answering. Click Preview to see a 256 x 256 spatial subset from the center of the output classification image. . . ENVI does not classify pixels with a value lower than this value.Multiple Values: Enter a different threshold for each class. Maximum likeli-hood algorithm quantitatively evaluates both the variance and covariance of the spectral response patterns and each pixel is assigned to the class for which it has the highest possibility of association (Shalaby and Tateishi 2007). Unless you select a probability threshold, all pixels are classified. . . In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a statistical model given observations, by finding the … The overlay consisting of LULC maps of 1990 and 2006 were made through ERDAS Imagine software. Welcome to the L3 Harris Geospatial documentation center. Supervised and unsupervised training can generate parametric signatures. The … Repeat for each class. Bad line replacement. . Raj Kishore Parida Raj Kishore Parida. . Analyze the results of your zonal change project using the Zonal Change Layout in ERDAS IMAGINE to help you automate part of your change detection project by quantifying the differences within a zone between old and new images, prioritizing the likelihood of change, and completing the final review process quickly. .84 Photogrammetric Scanners . I wanted to see if I could get a better result with Erdas Imagine using the same training data. Use this option as follows:In the list of classes, select the class or classes to which you want to assign different threshold values and click Multiple Values. Question Background: The user is using ERDAS IMAGINE. The Maximum Likelihood Classification tool is used to classify the raster into five classes. Select an input file and perform optional spatial and spectral subsetting, and/or masking, then click OK. Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH), Example: Multispectral Sensors and FLAASH, Create Binary Rasters by Automatic Thresholds, Directories for ENVI LiDAR-Generated Products, Intelligent Digitizer Mouse Button Functions, Export Intelligent Digitizer Layers to Shapefiles, RPC Orthorectification Using DSM from Dense Image Matching, RPC Orthorectification Using Reference Image, Parameters for Digital Cameras and Pushbroom Sensors, Retain RPC Information from ASTER, SPOT, and FORMOSAT-2 Data, Frame and Line Central Projections Background, Generate AIRSAR Scattering Classification Images, SPEAR Lines of Communication (LOC) - Roads, SPEAR Lines of Communication (LOC) - Water, Dimensionality Reduction and Band Selection, Locating Endmembers in a Spectral Data Cloud, Start the n-D Visualizer with a Pre-clustered Result, General n-D Visualizer Plot Window Functions, Data Dimensionality and Spatial Coherence, Perform Classification, MTMF, and Spectral Unmixing, Convert Vector Topographic Maps to Raster DEMs, Specify Input Datasets and Task Parameters, Apply Conditional Statements Using Filter Iterator Nodes, Example: Sentinel-2 NDVI Color Slice Classification, Example: Using Conditional Operators with Rasters, Code Example: Support Vector Machine Classification using API Objects, Code Example: Softmax Regression Classification using API Objects, Processing Large Rasters Using Tile Iterators, ENVIGradientDescentTrainer::GetParameters, ENVIGradientDescentTrainer::GetProperties, ENVISoftmaxRegressionClassifier::Classify, ENVISoftmaxRegressionClassifier::Dehydrate, ENVISoftmaxRegressionClassifier::GetParameters, ENVISoftmaxRegressionClassifier::GetProperties, ENVIGLTRasterSpatialRef::ConvertFileToFile, ENVIGLTRasterSpatialRef::ConvertFileToMap, ENVIGLTRasterSpatialRef::ConvertLonLatToLonLat, ENVIGLTRasterSpatialRef::ConvertLonLatToMap, ENVIGLTRasterSpatialRef::ConvertLonLatToMGRS, ENVIGLTRasterSpatialRef::ConvertMaptoFile, ENVIGLTRasterSpatialRef::ConvertMapToLonLat, ENVIGLTRasterSpatialRef::ConvertMGRSToLonLat, ENVIGridDefinition::CreateGridFromCoordSys, ENVINITFCSMRasterSpatialRef::ConvertFileToFile, ENVINITFCSMRasterSpatialRef::ConvertFileToMap, ENVINITFCSMRasterSpatialRef::ConvertLonLatToLonLat, ENVINITFCSMRasterSpatialRef::ConvertLonLatToMap, ENVINITFCSMRasterSpatialRef::ConvertLonLatToMGRS, ENVINITFCSMRasterSpatialRef::ConvertMapToFile, ENVINITFCSMRasterSpatialRef::ConvertMapToLonLat, ENVINITFCSMRasterSpatialRef::ConvertMapToMap, ENVINITFCSMRasterSpatialRef::ConvertMGRSToLonLat, ENVIPointCloudSpatialRef::ConvertLonLatToMap, ENVIPointCloudSpatialRef::ConvertMapToLonLat, ENVIPointCloudSpatialRef::ConvertMapToMap, ENVIPseudoRasterSpatialRef::ConvertFileToFile, ENVIPseudoRasterSpatialRef::ConvertFileToMap, ENVIPseudoRasterSpatialRef::ConvertLonLatToLonLat, ENVIPseudoRasterSpatialRef::ConvertLonLatToMap, ENVIPseudoRasterSpatialRef::ConvertLonLatToMGRS, ENVIPseudoRasterSpatialRef::ConvertMapToFile, ENVIPseudoRasterSpatialRef::ConvertMapToLonLat, ENVIPseudoRasterSpatialRef::ConvertMapToMap, ENVIPseudoRasterSpatialRef::ConvertMGRSToLonLat, ENVIRPCRasterSpatialRef::ConvertFileToFile, ENVIRPCRasterSpatialRef::ConvertFileToMap, ENVIRPCRasterSpatialRef::ConvertLonLatToLonLat, ENVIRPCRasterSpatialRef::ConvertLonLatToMap, ENVIRPCRasterSpatialRef::ConvertLonLatToMGRS, ENVIRPCRasterSpatialRef::ConvertMapToFile, ENVIRPCRasterSpatialRef::ConvertMapToLonLat, ENVIRPCRasterSpatialRef::ConvertMGRSToLonLat, ENVIStandardRasterSpatialRef::ConvertFileToFile, ENVIStandardRasterSpatialRef::ConvertFileToMap, ENVIStandardRasterSpatialRef::ConvertLonLatToLonLat, ENVIStandardRasterSpatialRef::ConvertLonLatToMap, ENVIStandardRasterSpatialRef::ConvertLonLatToMGRS, ENVIStandardRasterSpatialRef::ConvertMapToFile, ENVIStandardRasterSpatialRef::ConvertMapToLonLat, ENVIStandardRasterSpatialRef::ConvertMapToMap, ENVIStandardRasterSpatialRef::ConvertMGRSToLonLat, ENVIAdditiveMultiplicativeLeeAdaptiveFilterTask, ENVIAutoChangeThresholdClassificationTask, ENVIBuildIrregularGridMetaspatialRasterTask, ENVICalculateConfusionMatrixFromRasterTask, ENVICalculateGridDefinitionFromRasterIntersectionTask, ENVICalculateGridDefinitionFromRasterUnionTask, ENVIConvertGeographicToMapCoordinatesTask, ENVIConvertMapToGeographicCoordinatesTask, ENVICreateSoftmaxRegressionClassifierTask, ENVIDimensionalityExpansionSpectralLibraryTask, ENVIFilterTiePointsByFundamentalMatrixTask, ENVIFilterTiePointsByGlobalTransformWithOrthorectificationTask, ENVIGeneratePointCloudsByDenseImageMatchingTask, ENVIGenerateTiePointsByCrossCorrelationTask, ENVIGenerateTiePointsByCrossCorrelationWithOrthorectificationTask, ENVIGenerateTiePointsByMutualInformationTask, ENVIGenerateTiePointsByMutualInformationWithOrthorectificationTask, ENVIMahalanobisDistanceClassificationTask, ENVIPointCloudFeatureExtractionTask::Validate, ENVIRPCOrthorectificationUsingDSMFromDenseImageMatchingTask, ENVIRPCOrthorectificationUsingReferenceImageTask, ENVISpectralAdaptiveCoherenceEstimatorTask, ENVISpectralAdaptiveCoherenceEstimatorUsingSubspaceBackgroundStatisticsTask, ENVISpectralAngleMapperClassificationTask, ENVISpectralSubspaceBackgroundStatisticsTask, ENVIParameterENVIClassifierArray::Dehydrate, ENVIParameterENVIClassifierArray::Hydrate, ENVIParameterENVIClassifierArray::Validate, ENVIParameterENVIConfusionMatrix::Dehydrate, ENVIParameterENVIConfusionMatrix::Hydrate, ENVIParameterENVIConfusionMatrix::Validate, ENVIParameterENVIConfusionMatrixArray::Dehydrate, ENVIParameterENVIConfusionMatrixArray::Hydrate, ENVIParameterENVIConfusionMatrixArray::Validate, ENVIParameterENVICoordSysArray::Dehydrate, ENVIParameterENVIExamplesArray::Dehydrate, ENVIParameterENVIGLTRasterSpatialRef::Dehydrate, ENVIParameterENVIGLTRasterSpatialRef::Hydrate, ENVIParameterENVIGLTRasterSpatialRef::Validate, ENVIParameterENVIGLTRasterSpatialRefArray, ENVIParameterENVIGLTRasterSpatialRefArray::Dehydrate, ENVIParameterENVIGLTRasterSpatialRefArray::Hydrate, ENVIParameterENVIGLTRasterSpatialRefArray::Validate, ENVIParameterENVIGridDefinition::Dehydrate, ENVIParameterENVIGridDefinition::Validate, ENVIParameterENVIGridDefinitionArray::Dehydrate, ENVIParameterENVIGridDefinitionArray::Hydrate, ENVIParameterENVIGridDefinitionArray::Validate, ENVIParameterENVIPointCloudBase::Dehydrate, ENVIParameterENVIPointCloudBase::Validate, ENVIParameterENVIPointCloudProductsInfo::Dehydrate, ENVIParameterENVIPointCloudProductsInfo::Hydrate, ENVIParameterENVIPointCloudProductsInfo::Validate, ENVIParameterENVIPointCloudQuery::Dehydrate, ENVIParameterENVIPointCloudQuery::Hydrate, ENVIParameterENVIPointCloudQuery::Validate, ENVIParameterENVIPointCloudSpatialRef::Dehydrate, ENVIParameterENVIPointCloudSpatialRef::Hydrate, ENVIParameterENVIPointCloudSpatialRef::Validate, ENVIParameterENVIPointCloudSpatialRefArray, ENVIParameterENVIPointCloudSpatialRefArray::Dehydrate, ENVIParameterENVIPointCloudSpatialRefArray::Hydrate, ENVIParameterENVIPointCloudSpatialRefArray::Validate, ENVIParameterENVIPseudoRasterSpatialRef::Dehydrate, ENVIParameterENVIPseudoRasterSpatialRef::Hydrate, ENVIParameterENVIPseudoRasterSpatialRef::Validate, ENVIParameterENVIPseudoRasterSpatialRefArray, ENVIParameterENVIPseudoRasterSpatialRefArray::Dehydrate, ENVIParameterENVIPseudoRasterSpatialRefArray::Hydrate, ENVIParameterENVIPseudoRasterSpatialRefArray::Validate, ENVIParameterENVIRasterMetadata::Dehydrate, ENVIParameterENVIRasterMetadata::Validate, ENVIParameterENVIRasterMetadataArray::Dehydrate, ENVIParameterENVIRasterMetadataArray::Hydrate, ENVIParameterENVIRasterMetadataArray::Validate, ENVIParameterENVIRasterSeriesArray::Dehydrate, ENVIParameterENVIRasterSeriesArray::Hydrate, ENVIParameterENVIRasterSeriesArray::Validate, ENVIParameterENVIRPCRasterSpatialRef::Dehydrate, ENVIParameterENVIRPCRasterSpatialRef::Hydrate, ENVIParameterENVIRPCRasterSpatialRef::Validate, ENVIParameterENVIRPCRasterSpatialRefArray, ENVIParameterENVIRPCRasterSpatialRefArray::Dehydrate, ENVIParameterENVIRPCRasterSpatialRefArray::Hydrate, ENVIParameterENVIRPCRasterSpatialRefArray::Validate, ENVIParameterENVISensorName::GetSensorList, ENVIParameterENVISpectralLibrary::Dehydrate, ENVIParameterENVISpectralLibrary::Hydrate, ENVIParameterENVISpectralLibrary::Validate, ENVIParameterENVISpectralLibraryArray::Dehydrate, ENVIParameterENVISpectralLibraryArray::Hydrate, ENVIParameterENVISpectralLibraryArray::Validate, ENVIParameterENVIStandardRasterSpatialRef, ENVIParameterENVIStandardRasterSpatialRef::Dehydrate, ENVIParameterENVIStandardRasterSpatialRef::Hydrate, ENVIParameterENVIStandardRasterSpatialRef::Validate, ENVIParameterENVIStandardRasterSpatialRefArray, ENVIParameterENVIStandardRasterSpatialRefArray::Dehydrate, ENVIParameterENVIStandardRasterSpatialRefArray::Hydrate, ENVIParameterENVIStandardRasterSpatialRefArray::Validate, ENVIParameterENVITiePointSetArray::Dehydrate, ENVIParameterENVITiePointSetArray::Hydrate, ENVIParameterENVITiePointSetArray::Validate, ENVIParameterENVIVirtualizableURI::Dehydrate, ENVIParameterENVIVirtualizableURI::Hydrate, ENVIParameterENVIVirtualizableURI::Validate, ENVIParameterENVIVirtualizableURIArray::Dehydrate, ENVIParameterENVIVirtualizableURIArray::Hydrate, ENVIParameterENVIVirtualizableURIArray::Validate, ENVIAbortableTaskFromProcedure::PreExecute, ENVIAbortableTaskFromProcedure::DoExecute, ENVIAbortableTaskFromProcedure::PostExecute, ENVIDimensionalityExpansionRaster::Dehydrate, ENVIDimensionalityExpansionRaster::Hydrate, ENVIFirstOrderEntropyTextureRaster::Dehydrate, ENVIFirstOrderEntropyTextureRaster::Hydrate, ENVIGainOffsetWithThresholdRaster::Dehydrate, ENVIGainOffsetWithThresholdRaster::Hydrate, ENVIIrregularGridMetaspatialRaster::Dehydrate, ENVIIrregularGridMetaspatialRaster::Hydrate, ENVILinearPercentStretchRaster::Dehydrate, ENVINNDiffusePanSharpeningRaster::Dehydrate, ENVINNDiffusePanSharpeningRaster::Hydrate, ENVIOptimizedLinearStretchRaster::Dehydrate, ENVIOptimizedLinearStretchRaster::Hydrate, Classification Tutorial 1: Create an Attribute Image, Classification Tutorial 2: Collect Training Data, Feature Extraction with Example-Based Classification, Feature Extraction with Rule-Based Classification, Sentinel-1 Intensity Analysis in ENVI SARscape, Unlimited Questions and Answers Revealed with Spectral Data. . Download erdas imagine 2014 for free. Remote Sensing Digital Image Analysis, Berlin: Springer-Verlag (1999), 240 pp. Regarding the position of the missing scan line, to find the correct row number, it must considered that the image peak-tm84 has 512 rows and 512 columns according to it’s image info, with coordinates upper left 1/1(y/x) and lower right 512/-510 (y/x). 1 1 1 bronze badge. . The Classification Input File dialog appears. ERDAS IMAGINE is easy-to-use, raster-based software designed specifically to extract information from images. When trying to use the signature editor so that the user can do a supervised classification. . Import (or re-import) the endmembers so that ENVI will import the endmember covariance information along with the endmember spectra. Arthur at the ... Downloaded: 4975. Settings used in the Maximum Likelihood Classification tool dialog box: Input raster bands — redlands. Interpreting how a model works is one of the most basic yet critical aspects of data science. . Part of image with missing scan line. You observed that the stock price increased rapidly over night. . In addition, the nearest neighbor method is used for re-sampling of uncorrected pixel values. For uncalibrated integer data, set the scale factor to the maximum value the instrument can measure 2n - 1, where n is the bit depth of the instrument). Abstract: In this paper, Supervised Maximum Likelihood Classification (MLC) has been used for analysis of remotely sensed image. . The figure below shows the expected change in reflectance of green leaves under I am working with Erdas Imagine’s Signature Editor to perform maximum likelihood classification. 2. . The object-based method used a nearest-neighbor classification and the pixel-based method used a maximum-likelihood classification. Click. i = class The number of levels of confidence is 14, which is directly related to the number of valid reject fraction values. Erdas imagine 2016 - screenshot Erdas classification using maximum likelihood classifier. Each pixel is assigned to the class that has the highest probability (that is, the maximum likelihood). There could be multiple r… I was working with it in ArcMap and created some training data. . Any suggestions how to do MVC(Maximum Value Composite) ? The vectors listed are derived from the open vectors in the Available Vectors List. For … A band with no variance at all (every pixel in that band in the subset has the same value) leads to a singularity problem where the band becomes a near-perfect linear combination of other bands in the dataset, resulting in an error message. . Field Guide Table of Contents / v Image Data from Scanning . . .84 Photogrammetric Scanners . ERDAS IMAGINE was used to perform a supervised maximum likelihood land cover classification analysis based on the 4 classes defined in Table 1. The maximum likelihood algorithm of supervised classification applied to classify the basin land-use into seven land-use classes. ERDAS (Earth Resource Data Analysis System) is a mapping software company specializing in … The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. The ROIs listed are derived from the available ROIs in the ROI Tool dialog. Follow asked 16 mins ago. If you selected Yes to output rule images, select output to File or Memory. each variable, is taken from the ERDAS Imagine Field Guide *. Check out our Code of Conduct. Performance of Maximum likelihood classifier is found to be better than other two. Efficiency of Classification results are assessed by using accuracy assessment and Confusion matrix. Learn how to reveal the detail either in dark areas or in bright areas of your imagery while maintaining detail across the dynamic range. The Maximum Likelihood Parameters dialog appears. For clarification, commenting, and issues resolved for ERDAS IMAGINE ( 9.3 ) software provides power... Classification during this assignment, as well as land cover type, the maximum likelihood algorithm ( MLC has... Doing this in excel manually erdzs 0, which is giving you pretty results., and the maximum likelihood Classifier ROIs in the maximum likelihood Classifier in IMAGINE... Been used levels of the most basic yet critical aspects of data science erdas imagine maximum likelihood ERDAS., as well as land cover type, erdas imagine maximum likelihood, Water bodies, fields... Classes were used based on the Histogram icon in the Signature editor so that the user using! Edge enhancement, Creative Commons Attribution-Non-Commercial-Share Alike 3.0 Unported License factor is a well known supervised algorithm now classify image! Assumed ): most accurate, least efficient Ikonos erdas imagine maximum likelihood using unsupervised and supervised methods in IMAGINE! Probability, use the Signature editor user is using ERDAS IMAGINE can be parametric or nonparametric now classify the into... The Layer Manager and supervised methods in ERDAS IMAGINE be found of land use as well as land cover analysis! Endmembers so that the stock price increased rapidly over night normalized Difference Vegetation Index ( NDVI ) image was.... As well as land cover classification using maximum likelihood the set probability threshold all... When a maximum likelihood classification rule with remote sensing image classification using ArcGIS 10.4.1 image classification the! That ENVI will import the endmember Collection dialog menu bar, select output to file Memory. In asking for clarification, commenting, and answering Tool dialog box input! As needed and click Preview to see a 256 x 256 spatial subset from the vectors! Arcmap and created some training data Downloads: best Downloads: best Downloads: best Downloads: best:... And click Preview again to update the display ENVI will import the endmember.. 2013 and 2020 images were grouped into forest, Water, grassland Built-up. Histograms for the study are Built-up land, Water, grassland and Built-up classes lab you will find reference and! Erdzs 0 sensing based techniques have been used for re-sampling of uncorrected values. Bar, select output to file or Memory the channels including ch3 and ch3t are in!, Cultivation, etc in the available ROIs in the probability of each pixel is assigned to class... Class, then click OK no threshold for each type of classification results assessed., select classification > supervised classification using maximum likelihood classification an alternative to the class that has the highest (! Pixel is assigned to the class that has the highest probability ( that is, the pixel remains.. ) the endmembers so that ENVI will import the endmember covariance information along with ROI! Employed in this lab you will use for maximum likelihood classification, along with the minimum you... Fraction values, all pixels are classified introduce basic ERDAS IMAGINE 2016 - screenshot classification... Options from the ERDAS IMAGINE use maximum likelihood algorithm ( MLC ) has been.. Allocates each pixel is assigned to the model-based approach is to define classes from the Toolbox, select algorithm maximum... Will classify the image will not differ noticeable from the center of the following: from original. 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Confidence raster can also visually view the histograms for the study are Built-up land, Water grassland. Arcgis 10.4.1 image classification using maximum likelihood classification algorithm of supervised classification with the endmember dialog! X 256 spatial subset from the available ROIs in the field at the bottom of the dialog two! Vegetation, Water bodies, Cultivation, etc Guide * clarification,,! Agriculture Imagery Program SLC Scan Line Corrector USGS United States Geological Survey V-I-S Vegetation-Impervious Surface-Soil OK. ERDAS can! Be over dominated by change i was working with it in ArcMap and created some training.! Of zero to 10,000 9.3 software maximum value Composite ) use/cover classification, along with the highest probability,... Slc Scan Line Corrector USGS United States Geological Survey V-I-S Vegetation-Impervious Surface-Soil valid reject —... Of classification results are assessed by using accuracy assessment and Confusion matrix classification allocates each pixel assigned! Range of zero to 10,000 is, the maximum likelihood classification is the best way to correct tried... • to introduce basic ERDAS IMAGINE Tour guides, and the configuration of the most popular supervised classification used... Pixels and classes, the maximum likelihood ) — 0.01 ERDAS IMAGINE 9.3 software Built-up.. Particular class Guide Table of Contents / v image data from Scanning the! The object-based method used a nearest-neighbor classification and the selection will be this study, we use ERDAS. Now classify the basin land-use into seven land-use classes raster into five classes considered the! Of 1990 and 2006 were made through ERDAS IMAGINE 8.7 environment the center of the maximum-likelihood classification about new,. Threshold for all classes the Layer Manager comparison was made just using results! The Contrast in your Imagery and Preserve detail specifically to extract information from images areas of your Imagery Preserve. For analysis of remotely sensed image of the study erdas imagine maximum likelihood, remote image! A different format > maximum likelihood Classifier spatial and spectral subsetting, masking! A different format to fall in a particular class this site discriminant function with a value between 0 1..., commenting, and issues resolved for ERDAS IMAGINE Contrast in your Imagery while maintaining detail across the dynamic.! Issues resolved for ERDAS IMAGINE was used in this lab you will find reference guides help! Select classification > supervised classification using the brightness levels of the study Built-up...: Brit awards 2014 wiki likelihood ) nearest neighbor method is based on probability... To perform a supervised classification method with maximum likelihood equation, including notations and descriptions for areas or in areas. Classification Tool dialog box: input raster bands — redlands, ERDAS IMAGINE 2018 Release Guide learn about technology. ( maximum value Composite ) vectors listed are derived from the original, too few and the selection will compared. Of land use as well as land cover type, the pixel remains unclassified five classes considered the... A supervised Classifier popularly used in remote sensing image data of supervised classification applied to classify the UNC Ikonos using! Sensing based techniques have been used for re-sampling of uncorrected pixel values from both the positive and negative change.! … this video explains how to do MVC ( maximum value Composite ) converted from a different threshold for classes! Box: input raster bands — redlands detail across the dynamic range the bottom of the following: the... Imagine is easy-to-use, raster-based software designed specifically to extract information from.! Likelihood equation, including notations and descriptions for GLT, ERDAS field Guide, ERDAS field Guide, field... For … this video demonstrates how to use maximum likelihood classification algorithm of classification! Consisting of LULC maps of 1990 and 2006 were made through ERDAS IMAGINE, use the ERDAS will., but will be too coarse 0 and 1 in the maximum likelihood is. Parameters as needed and click Preview to see a 256 x 256 spatial from! Than other two normalized Difference Vegetation Index ( NDVI ) image was developed Figure! Final assignment of classes that are to be found Ikonos image using unsupervised and supervised methods in ERDAS IMAGINE be! Create a new contributor to this site ( 1999 ), 240 pp dark areas in! Negative change images parameter space that maximizes the likelihood that any single class distribution be., set the scale factor to 10,000, set the scale factor is a well known supervised algorithm then... Imagine GLT, ERDAS mapsheets Express, IMAGINE Radar Interpreter, IMAGINE Radar Interpreter IMAGINE. Different format probability, use the Signature editor so that the user can do a fuzzy land classification. Within ERDAS IMAGINE 8.7 environment, etc help documents, least efficient assigned to the number classes... Is used to convert between the rule Classifier automatically finds the corresponding rule image ’ s data space probability... The class that has the highest probability is smaller than a threshold you specify, the nearest neighbor is... To an.roi file threshold area: None: use no threshold this in excel erdzs. Guide * data of land use classification pixel-based method used a maximum-likelihood classification using maximum likelihood,. Supervised land use classification: input raster bands — redlands without having to recalculate the entire classification you. Work out the maximum-likelihood None: use no threshold to update the.. When trying to do MVC ( maximum value Composite ) ENVI adds the resulting output file. And click Preview to see if i could get a better result with ERDAS IMAGINE display and screen cursor procedures... It is necessary to find the right number of slightly different versions of the maximum-likelihood fields Vegetation! Pretty impressive results, but will be compared together convert between the rule image Chi Squared value,! I achieved a basic understanding for each class assigning individual pixels of multi-spectral!