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Erdas Imagine Unsupervised Classification

Erdas Imagine Unsupervised Classification

This tutorial demonstrates how to perform Unsupervised Classification of a Landsat 8 Image using Erdas Imagine software. Unsupervised classification is a classification method which examines a large number of unknown pixels and divides into a number of clusters based on natural groupings present in the image values. unlike supervised classification, unsupervised classification does not require analyst-specified training data. The user ...

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Using Deep Learning to Extract Feature Data from Imagery

Using Deep Learning to Extract Feature Data from Imagery

Vector data collection is the most tedious task in a GIS workflow. Digitizing features from imagery or scanned maps is a manual process that is costly, requiring significant human resources to accomplish. Building footprint extraction from imagery provides an even more complex challenge due to shadows, tree overhang, and the complexity of roofs. Often times, this feature extraction work is ...

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Erdas Imagine MNDWI (Water) using Model Maker or Spatial Modeller for Landsat 8

Erdas Imagine MNDWI (Water) using Model Maker or Spatial Modeller for Landsat 8

This tutorial demonstrates how to calculate MNDWI using ERDAS Imagine Model Maker or Spatial Modeller The Modified Normalized Difference Water Index (MNDWI) is is an index of open water features and produce much better result in comparison to NDWI Indices. MNDWI = (GREEN— SWIR)/(GREEN + SWIR) This index maximizes reflectance of water by using green light wavelengths and minimizes low ...

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Erdas Imagine Satellite Image Layer Stacking

Erdas Imagine Satellite Image Layer Stacking

This tutorial demonstrates how to layer stack or create a multi-band image from individual satellite images (band) using Erdas Imagine software and Landsat 8 Image. Downloaded Remote sensing satellite image is distributed in the form of band. In order to use the data in the typical manner in most geospatial software packages we need to combine the individual bands in ...

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