Module 5: Unsupervised & Supervised Classification

This lab step through using ERDAS IMAGINE to classify imagery using unsupervised and supervised classification. 

To start the lab provided a high-resolution aerial image of University of West Florida, which was classified into 50 classes. Then it was necessary to accurately label the classes and group them into five categories. Once labeled a Reorder tool was used to save the images with only those five classes. See snippet below,

The lab then step through the process of supervised classification by annotating an image's spectral signatures. The ERDAS IMAGINE used this signature file to process and classify the images. Optionally, you can create a distance image, to visual evaluate how well the imaged had been processed. See the map below.

Module 4: Spatial Enhancement, Multispectral Data, and Band Indices

 This lab cover a lot of material as it walk through spatial enhancement, multi-spectral data, and bands. After providing a step-by-step guide on downloading and importing satellite imagery from Glovis. 

The lab used both ERDAS and ArcGIS Pro, where manipulation of the multi spectral data was performed in ERDAS and creating a map demonstrating the intended concept. Below are the three maps created during this lab assignment.

True Color used to identify turbid water

False Color IR used to identify bodies of water or green vegetation.

False Natural Color used to enhance the glacier or packed snow features.

Module 3 - Intro to ERDAS Imagine and Digital Data

 This week focused on using ERDAS Imagine and digital data. The instructions provided a brief overview of the application, setting and options, and content pane. Similar to ArcGIS Pro the application contains a ribbon with tabs and each tab contains a group of related tools. 

After navigating around adding data to the application, using the inquire tool to select an area of a LANDSAT Thematic Mapper image image of an area in Washington State. The area, found in the map below, is rather small but accomplished the goal. Another requirement was updating the images attribute with the a column that shows the area of each classification.