Cherie Bhekti Pribadi, S.T., M.T. The output area units are in square meters. Set the initial classification to have 16 classes and 16 iterations. If you used single-band input data, only Maximum likelihood and Minimum distance are available. Supervised Classification The first stage of the supervised classification process is to collect reference training sites for each land cover type in order to generate training signatures. You can preview the refinement before you apply the settings. According to the degree of user involvement, the classification algorithms are divided into two groups: unsupervised classification and supervised classification. You can also write a script to perform classification using the following routines: The specific objectives are; • To create training area that will be used for all classification algorithms • To perform a supervised classification based on the highlighted algorithms above • To compares the class statistics for all classes in the various classification algorithms 5.1 Materials and Method This analysis was implemented using ENVI 5.0 classic imagery software. Classification Workflow I wrote up a full discussion on the issues that I faced and solutions that I found throughout the process – you can take a look at it here if you want. The training data can come from an imported ROI file, or from regions you create on the image. From the Classification menu select the Unsupervised, K-means option. The number of classes, prototype pixels for each class can be identified using this prior knowledge 9 See the following for help on a particular step of the workflow: Since our training sites might not be relevant, we wanted to perform supervised classification using endmembers spectra instead of ROIs. Select a Classification Method (unsupervised or supervised) In this tutorial, you will use SAM. If you select None for both parameters, then ENVI classifies all pixels. Preview is not available for unsupervised classification, as ENVI would need to process the entire image in order to provide a preview image. Supervised vs. Unsupervised Classifiers Supervised classification generally performs better than unsupervised classification IF good quality training data is available Unsupervised classifiers are used to carry out preliminary analysis of data prior to supervised classification 12 GNR401 Dr. A. Bhattacharya This topic describes the Classification Workflow in ENVI. Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image. Click the Load Training Data Set button and select a file that contains training data. According to the degree of user involvement, the classification algorithms are divided into two groups: unsupervised classification and supervised classification. On the left is ENVI’s automated (“unsupervised”) classification and on the right is a manual (“supervised”) classification. Supervised Classification Settings And this time we will look at how to perform supervised classification in ENVI. In the Supervised Classification panel, select the supervised classification method to use, and define training data. In the Algorithm tab, you can apply no thresholding, one thresholding value for all classes, or different thresholding values for each class. The computer algorithm then uses the spectral signatures from these … Clean Up Classification Results Overview: Supervised classification has been reported as an effective automated approach for the detection of AMD lesions [25]. ENVIMahalanobisDistanceClassificationTask But the next step forward is to use object-based image analysis. Unsupervised classification clusters pixels in a dataset based on statistics only, without requiring you to define training classes. Like this one: This is a rule image for the ocean(Blue) class that I had made. From the Toolbox, select Classification > Classification Workflow. Here it is: And here is the final map with a legend for the classes that I decided on. Along the way, you will need to do a manual classification (one supervised, one unsupervised) in envi. The pixel of interest must be within both the threshold for distance to mean and the threshold for the standard deviation for a class. Classification is an automated methods of decryption. The File Selection panel appears. In contrast, the final classification image is a single-band image that contains the final class assignments; pixels are either classified or unclassified. If the training data uses different extents, the overlapping area is used for training. The measures for the rule images differ based on the classification algorithm you choose. I would like to conduct a supervised classification of land cover types in a region that features fairly small "objects" relative to Sentinel-2 pixel size. Classification Tutorial This step is called The training data can come from an imported ROI file, or from regions you create on the image. The Classification workflow accepts any image format listed in Supported Data Types. This is done by selecting representative sample sites of a known cover type called Training Sites or Areas. The general workflow for classification is: Collect training data. Click Open File. The training data must be defined before you can continue in the supervised classification workflow (see Work with Training Data). I began with Landsat7 imagery from Santa Barbara and used bands 1-6, ignoring the second Short Wave Infrared band and the panchromatic band. You can add additional ROIs to an existing ROI layer that you imported, and you can create new ROI layers. In the Classification Type panel, select the type of workflow you want to follow, then click Next. You must define a minimum of two classes, with at least one training sample per class. Implementation of SVM by the ENVI 4.8 software uses the pairwise classification strategy for multiclass classification. Once defined, select the classes that you want mapped in the output. Using this method, the analyst has available sufficient known pixels to generate representative parameters for each class of interest. I scaled down the power of these classes by reducing the number of standard deviations that the Parallelepiped classification would use in its bounds for each land cover type. 03311340000035 Dosen: Lalu Muhammad Jaelani, S.T., M.Sc.,Ph.D. Supervised classification can be used to cluster pixels in a data set into classes corresponding to user-defined training classes. Click the Advanced tab for additional options. The smaller the distance threshold, the more pixels that are unclassified. Among other things I realized here that I didn’t need two classes for open water because the lake pixels were just showing up in the ocean and the ocean pixels were appearing in the lakes. Supervised Landsat Image Classification using ENVI 5.3 3 ( 3 votes ) Supervised Landsat Image Classification using ENVI 5.3 Various The assumption that unsupervised is not superior to supervised classification is incorrect in many cases. Define the training data to use for classification. 6.2. Performing cleanup significantly reduces the time needed to export classification vectors. These samples are referred to as training areas. Click Browse and select a panchromatic or multispectral image, using the File Selection dialog. Supervised classification methods include Maximum likelihood, Minimum distance, Mahalanobis distance, and Spectral Angle Mapper (SAM). In supervised classification, the image processing software is guided by the user to specify the land cover classes of interest. Supervised classification methods include Maximum likelihood, Minimum distance, Mahalanobis distance, and Spectral Angle Mapper (SAM). Example: You can use regression to predict the house price from training data. Select the LANDSAT7_MANCHESTER.PIX image as the input file. Supervised Classification in ENVI In this project I created a land cover classification map for the Santa Barbara area using Landsat7 data and ENVI. When you load a training data set from a file, it will replace any ROIs that you drew on the screen previously. Classification: Classification means to group the output inside a class. Tip: Cleanup is recommended if you plan to save the classification vectors to a file in the final step of the workflow. The user defines “training sites” – areas in the map that are known to be representative of a particular land cover type – for each land cover type of interest. This classification type requires that you select training areas for use as the basis for classification. Tip: If you click the Delete Class or Delete All Classes button to remove ROIs, they will no longer be available to re-open through the Data Manager or Layer Manager. Click Browse. Performing Unsupervised Classification. Supervised Landsat Image Classification using ENVI 5.3 3 ( 3 votes ) Supervised Landsat Image Classification using ENVI 5.3 To provide adequate training data, create a minimum of two classes, with at least one region per class. “Supervised classification is the process most frequently used for quantitative analyses of remote sensing image data” [9]. The following are available: Enter values for the cleanup methods you enabled: In the Export Files tab in the Export panel, enable the output options you want. To optionally adjust parameter settings for the algorithms, see, To add an ROI to an existing training data class, select the class from the, To delete a class, select the class and click the. Among methods for creating land cover classification maps with computers there are two general categories: Supervised and Unsupervised – I used a supervised classification here. Supervised Classification The classifier has the advantage of an analyst or domain knowledge using which the classifier can be guided to learn the relationship between the data and the classes. Supervised classification clusters pixels in a dataset into classes based on user-defined training data. Land Cover Classification with Supervised and Unsupervised Methods. Unsupervised Classification. Both classification methods require that one know the land cover types within the image, but unsupervised allows you to generate spectral classes based on spectral characteristics and then assign the spectral classes to information classes based on field observations or from the imagery. If you applied a mask to the input data, create training samples within the masked area only. This graphic essentially shows the overlap of the digital number values for pixels within each ROI spatially. Export Classification Vectors saves the vectors created during classification to a shapefile or ArcGIS geodatabase. In this tutorial, you will use SAM. Hal ini dijelaskan karena pada artikel yang akan datang, blog INFO-GEOSPASIAL akan coba membuat artikel tentang analisis perubahan tutupan lahan dengan menggunakan kedua metode tersebut. This classification type requires that you select training areas for use as the basis for classification. Performing the Cleanup step is recommended before exporting to vectors. This topic describes the Classification Workflow in ENVI. You can perform an unsupervised classification without providing training data, or you can perform a supervised classification where you provide training data and specify a classification method of maximum likelihood, minimum distance, Mahalanobis distance, or Spectral Angle Mapper (SAM). The SAM method is a spectral classification technique that uses an n -D angle to match pixels to training data. This workflow uses unsupervised or supervised methods to categorize pixels in an image into different classes. To compute rule images for the selected classification algorithm, enable the Compute Rule Images check box. Classification is an automated methods of decryption. Specifying a different threshold value for each class includes more or fewer pixels in a class. Supervised classification clusters pixels in a dataset into classes based on user-defined training data. Note: Depending on the image size, exporting to vectors may be time-consuming. We want ROIs that are distinct in the image, so we want these clouds of points to be separate from one another. These clouds are far too overlapping, but it would take me some time to figure that out – I went ahead and tried to run the classification using these ROIs as training sites. Select Input Files for Classification SVM classification output is the decision values of each pixel for each class, which are used for probability estimates. Navigate to classification, … SVM classification output is the decision values of each pixel for each class, which are used for probability estimates. Examples include ROIs (.roi or .xml) and shapefiles. Supervised classification requires the selection of representative samples for individual land cover classes. Press the Enter key to accept the value. Article from monde-geospatial.com. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). Don’t stop here. Thereafter, software like IKONOS makes use of ‘training sites’ to apply them to the images in the reckoning. In this project I created a land cover classification map for the Santa Barbara area using Landsat7 data and ENVI. Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. The pixel values in the rule images are calculated as follows: Maximum Likelihood classification calculates the following discriminant functions for each pixel in the image: x = n-dimensional data (where n is the number of bands), p(ωi) = probability that a class occurs in the image and is assumed the same for all classes, |Σi| = determinant of the covariance matrix of the data in a class, Σi-1 = the inverse of the covariance matrix of a class. Once defined, select the classes that you want mapped in the output. And here is a false color image using the SWIR, NIR, and Red bands loaded into the RGB slots. See the following for help on a particular step of the workflow: You can also write a script to perform classification using the following routines: Note: Datasets from JPIP servers are not allowed as input. Each color on the graphic corresponds to one of the ROIs on the map and those colors that spatially overlap in this visualization are composed of pixels that look similar to ENVI within bands 3, 4, and 5. It is a software application used to process and analyze geospatial imagery. Each class has its own set of ROIs. Minimum Distance classification calculates the Euclidean distance for each pixel in the image to each class: Mahalanobis Distance classification calculates the Mahalanobis distance for each pixel in the image to each class: Spectral Angle Mapper classification calculates the spectral angle in radians for each pixel in the image to the mean spectral value for each class: You can load previously-created ROIs from a file, or you can create ROIs interactively on the input image. Types of Supervised Machine Learning Techniques. Welcome to the L3 Harris Geospatial documentation center. The training data must be defined before you can continue in the supervised classification workflow (see Work with Training Data). The user specifies the various pixels values or spectral signatures that should be associated with each class. Classifiers and Classifications using Earth Engine The Classifier package handles supervised classification by traditional ML algorithms running in … This is the most modern technique in image classification. ENVI’s automated classification is very good. The output is a single file containing one rule image per class, with measurements for each pixel related to each class. In a supervised classification, the creator defines certain land cover classes and then allows the computer to find other regions that spectrally match those based on available data. For reference the final n-d visualization ended up looking much more distinct than that first one we looked at. Implementation of SVM by the ENVI 4.8 software uses the pairwise classification strategy for multiclass classification. These are examples of image classification in ENVI. The previous post was dedicated to picking the right supervised classification method. We will take parallelepiped classification as an example as it is mathematically the easiest algorithm. ... performed by ENVI software, the ROI separability tool is needed to calculate the statistical distance between all categories, and the degree of difference between the two categories is Unsupervised classification is useful when there is no preexisting field data or detailed aerial photographs for the image area, and the user cannot accurately specify training areas of known cover type. The process of defining the training sites for a supervised classification ended up being arduous and I had to backtrack often to make the classification scheme appropriate for the Santa Barbara area. Supervised Classification . LAPORAN PRAKTIKUM PENGINDERAAN JAUH KELAS B “UNSUPERVISED CLASSIFICATION CITRA LANDSAT 8 MENGGUNAKAN SOFTWARE ENVI 5.1” Oleh: Aulia Rachmawati NRP. The process is much more interesting to see using a lot of visuals though so that’s what I’m going to do here and all you need to do is scroll down. Various comparison methods are then used to determine if a specific pixel qualifies as a class member. I applied a majority filter to get rid of some of the noise from the final image. LAPORAN PRAKTIKUM PRAKTEK INDERAJA TERAPAN Dosen Pengampu : Bambang Kun Cahyono S.T, M. Sc Dibuat oleh : Rahmat Muslih Febriyanto 12/336762/SV/01770 PROGRAM STUDI DIPLOMA III TEKNIK GEOMATIKA SEKOLAH VOKASI UNIVERSITAS GADJAH MADA 2014/2015 Judul “Klasifikasi Terbimbing ( Supervised )” Tujuan Mahasiswa dapat melakukan georeferencing Citra. Note: If the output will be used in ArcMap or ArcCatalog, creating 30 or more classes will cause ArcMap or ArcCatalog to use a stretch renderer by default. Each iteration recalculates means and reclassifies pixels with respect to the new means. You can modify the ArcMap or ArcCatalog default by adding a new registry key. These classifiers include CART, RandomForest, NaiveBayes and SVM. You can write a script to calculate training data statistics using ENVIROIStatisticsTask or ENVITrainingClassificationStatisticsTask. In the Unsupervised Classification panel, set the values to use for classification. The File Selection dialog appears. Unsupervised Classification Settings The supervised classification was ap-plied after defined area of interest (AOI) which is called training classes. Supervised Classification. The user does not need to digitize the objects manually, the software does is for them. The training data must be defined before you can continue in the supervised classification workflow (see Work with Training Data). Remote sensing supervised classification ENVI Firstly open a viewer with the Landsat image displayed in either a true or false colour composite mode. (ENVI). ENVI does not classify pixels outside this range. The following are available: In the Additional Export tab, enable any other output options you want. The user does not need to digitize the objects manually, the software does is for them. Recall that supervised classification is a machine learning task which can be divided into two phases: the learning (training) phase and the classification (testing) phase [21]. Here you will find reference guides and help documents. Supervised classification can be used to cluster pixels in a data set into classes corresponding to user-defined training classes. Select a Classification Method (unsupervised or supervised), ENVIMahalanobisDistanceClassificationTask, 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, 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.