Wil Blouin's Advanced Remote Sensing Portfolio

Goals and Background: The goal of this lab was to gain experience working with an unsupervised classifier. Unsupervised classifiers do not take training samples or other spectral information in order to give a classified land output. With parameters and multispectral raster data provided, they run fully automatically. The unsupervised classifier used in this lab was the ISODATA algorithm. We ran this classifier from within ERDAS Imagine. The classifier was run twice with different amounts of clusters. After the processing was completed, the clusters were examined individually and identified as the class that the majority of the cluster happened to cover. Unsupervised classification can produce a less than perfect result with salt and peppering of incorrect land cover spread over sections of correct land cover. This is a reason not to use this type of classification and to instead choose a supervised classifier or an object based classifier. It is important nonetheless to have exper...

Goals and Background: The goal of this lab was to familiarize students with performing non-radiative transfer code (RTC) methods of absolute and also relative atmospheric correction on remotely sensed images. More specifically, the Empirical Line Calibration and Enhanced Image Dark Object Subtraction absolute atmospheric correction methods were performed in parts 1 and 2 of this lab, and Multidate Image Normalization was performed in part 3 of the lab. Because of the complexity of finding the in-situ data such as atmospheric moisture content, temperature, pressure, aerosol contents, vertical distribution of water vapor, and other factors, RTC methods were not employed in this lab. All work was performed in ERDAS Imagine. Atmospheric correction is a necessary step before performing many major remote sensing functions. Before measuring any biophysical elements of an image including soil properties (such as performing a ferrous mineral index), vegetation characteristics (such as perf...

Goal and Background:  The goal of this lab was to become familiarized with the concepts pertaining to thermal remote sensing data and working with thermal remote sensing data from various satellite data to find ground temperatures (assuming perfect blackbody emissivity of the surface). Part 1 required students to make various basic observations of the thermal data. Then, data covering a large area of central western Wisconsin was processed in Erdas Imagine using the Model Maker to first find at-satellite radiance from raw Digital Number (DN) data (Part 2, Section 1), and then to find the blackbody surface temperature from the at-satellite radiance data (Part 2, Section 2). While Part 2 worked with Landsat 7 ETM+ data, Part 3 worked with Landsat TM data and Part 4 worked with Landsat 8 data. These last two sections required the same processes as in Part 2 Sections 1 and 2, but in a fully integrated model form. Landsat TM includes a thermal band as band 6 with a width o f  1...