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http://www.asprs.org/a/publications/proceedings/pecora16/Pradhan_D.pdf
INTEGRATING SVM TOOLS IN ERDAS IMAGINE 8.7 FOR USDA’S CONSERVATION RESERVE PROGRAM MAPPING AND COMPLIANCE MONITORING ... ABSTRACT Our overall goal is to investigate the utility of Support Vector Machine (SVM) based semi-supervised classification ... 8.7 for USDA's Conservation Reserve Program Mapping and Compliance Monitoring Author: Pradhan ...
https://en.wikipedia.org/wiki/Support_vector_machine
Support-vector machine weights have also been used to interpret SVM models in the past. Posthoc interpretation of support-vector machine models in order to identify features used by the model to make predictions is a relatively new area of research with special significance in the biological sciences. History
https://community.hexagongeospatial.com/t5/Spatial-Modeler-Tutorials/Machine-Learning-Example-Classifying-Features/ta-p/22616
There are two broad categories of Machine Learning classifiers introduced in ERDAS IMAGINE 2018. One deals with the broader category of Machine Learning which can be thought of as traditional classifiers (supervised or unsupervised) and includes Random …
https://www.hexagongeospatial.com/products/power-portfolio/erdas-imagine
Hexagon Geospatial ERDAS IMAGINE supplies tools for all remote sensing, photogrammetry, and GIS needs. Geospatial Division. ... Support Resources M.App Exchange ... Machine and Deep Learning algorithms that can be trained to automatically analyze massive amounts of data are improving geospatial workflows and advancing image processing.
http://www.thaiscience.info/journals/Article/WJST/10958633.pdf
Land Use Classification using Support Vector Machine and Maximum Likelihood Algorithms by Landsat 5 TM Images . Abbas TAATI*, Fereydoon SARMADIAN, Amin MOUSAVI, Chamran Taghati Hossien POUR and Amir Hossein Esmaile SHAHIR. Department of Soil Science Engineering, University of Tehran, Karaj, Iran
https://community.hexagongeospatial.com/t5/IMAGINE-Discussions/ERDAS-IMAGINE-2018-Machine-Learning-Classification/m-p/23824
Hello, Is there anyone with an idea of how to refine the classification result from Machine Learning using ERDAS IMAGINE 2018? The attached screenshot is a result from raster classification using Machine Learning operators. The result doenst seem homogenoues or smooth/uniform. Same to the resu...
https://gis.stackexchange.com/questions/63688/vector-machine-classification-in-arcgis
Is it possible to perform a vector machine classification of raster image using the ArcGIS software package. This supervised classification method is available in the ENVI software but not for ArcG...
https://imagemnl.com/wp-content/uploads/2018/08/ERDAS_IMAGINE_2018_Release_Guide.pdf
2018 (32-bit) rather than ERDAS IMAGINE 2018 (64-bit). SUPPORT OF NEW FEATURE / VECTOR DATA FORMATS . Support is now added for reading Features from PostGIS database (PostgreSQL 9.4 / PostGIS 2.2). Features are accessed via a PostGIS Feature Proxy (pfp). Support for reading features from and writing to GeoCSV is also added with the following ...
https://www.hexagongeospatial.com/products/power-portfolio/erdas-imagine/erdas-imagine-product-release-details
ERDAS IMAGINE interface now runs natively in 64-bit, enabling embedded components such as the 2D View and Spatial Model Editor to leverage more of your available system memory and CPUs. Along with streamlined algorithms, this also provides more efficient execution of ERDAS IMAGINE in general. New operators enable machine-learning classification
http://www.asprs.org/a/publications/proceedings/reno2006/0034.pdf
drainage basin and less comparing the methods of unsupervised, Artificial Neural Networks (ANN) and Support Vector Machine (SVM). GENERAL BACKGROUND OF THE CLASSIFICATION ALGORITHMS ISODATA Classification The initial cluster means are distributed along a vector between the point at the following spectral coordinates, Eq. 1 (ERDAS, 1999):
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