Wil Blouin's Advanced Remote Sensing Portfolio

Goals and Background: The goal of this lab was to gain experience in correcting classification with the expert system/decision tree method leveraging ancillary data, and performing a neural network classification. The expert system/decision tree method for classification allows the user to create shapes that designate an area where the user would like to change specific classes to other classes in an existing classified image by using whatever ancillary data is available to the user to consult. This is performed in ERDAS Imagine. The neural network classifier simulates the structure of the human brain in performing its black box classification based on a users input parameters and data, and can be performed in ENVI. The Methods: Part 1: Classification using the expert system/decision tree method This section was performed using the Knowledge Engineer tool in ERDAS Imagine. In examining the provided classified image of the cities of Eau Claire and Chippewa Falls it was evident

Goals and Background: The objective of this lab was to become familiarized with object-based land use/land cover (LULC) classification performed in Trimble eCognition. This process is made up of a few different processes: first segmentation of images into objects (clusters of pixels grouped based on spatial and spectral homogeneity vectorized), then training and setup of random forest and then support vector machine classifiers, and finally refining of the classification output from the classifiers. Methods: Part 1: Creating the eCognition project A new project was created in eCognition using Landsat 7 ETM+ imagery supplied from June 9, 2000. The data brought in included the 6 bands from the sensor, however which bands these were was unspecified. The resolution of the imagery was set at 30 meters as this was the spatial resolution of all of the layers, and a global value of zero was set for cases of no data. The layer mixing button was now clicked in the top bezel   , and the

Goals and Background: The goal of this lab was to practice various functions to detect change in images over time. Quick qualitative change detection was first practiced with the Write Function Memory Insertion method, which is the assigning of different monitor color guns to bands of multi-date images. This practice only gives a quick visual overview of the  Then, the changes in total land cover of each specific land use/land cover (LULC) class in a pair of images (one from 2001 and another from 2011) were calculated along with the percent change. Finally, amounts of LULC change of for specific pairs of LULC classes that a government agency would be interested in were calculated. These included Agriculture to Urban/Built-up and Wetland to Agriculture, among others. This was performed via the  Wilson-Lula algorithm. All functions were performed in ERDAS Imagine (including the use of the Model Builder) and Microsoft Excel.  Methods: Part 1: Change detection using Write  Function