GEOG 438: Advanced Remote Sensing

Goal The main goal of this lab is to understand the art of extracting biophysical and sociocultural information from remotely sensed images, utilizing unsupervised classification algorithm. First in the lab we will become familiar with understanding the input configuration requirements and execution of an unsupervised classifier. Then the art of recoding multiple spectral clusters generated from unsupervised classifiers into thematic informational land use and land cover classes that meet a classification scheme. Methods Part 1: Experimenting with unsupervised ISODATA classification algorithm   Section 1: Setting up an unsupervised classification algorithm     In part one of the lab, we will  be experimenting with unsupervised ISODATA classification algorithm. First off, an Eau claire and Chippewa Image was formated to an unsupervised classification, utilizing an ISODATA algorithm. Figure 1: Classification scheme utilized when performing the ISODATA ...

Goal The main goal of this lab is to gain practical experience on correcting remotely sensed images for atmospheric correction. This lab will provide the knowledge to perform multiple methods for absolute atmospheric correction, and additionally relative atmospheric corrections will be performed. The two absolute atmospheric correcting methods that were used are Empirical Line Calibration (ELC) and Dark Object Subtraction (DOS). Additionally the Relative Atmospheric correction process requires using a multidate image normalization process. Methods Part 1: Absolute atmospheric correcting using empirical line calibration  In the first part of this lab atmospheric correction was performed on an Eau_Claire2011.img. This is a Landsat 5 TM image, that was collected on August 3rd 2011 at 10:41 am CST. In order to remove the atmospheric interference from this imaghe, spectral libraries are necessary for providing in situ data to help understand spectral profiles of surfaace features...

Goal and Background The goal of this lab is to understand and utilize the skills for extracting land surface temperatures from thermal bands from satellite images, and additionally account for these variations in land surface temperature over different areas. This lab will allow us to visually identify variations in relative land surface temperature. Model building will be used to quantitatively estimate surface temperatures from thermal bands, then more complex models will be utilized to accomplish the goal in one model rather than using multiple models or steps. Methods Part 1: Visual identification of relative variations in land surface temperature To begin the first part of this lab,  Landsat ETM+ images from band 61 and 62 are utilized. In this portion of the lab the tonal quality of each image is analyzed to determine temperature differences. Then the spectral range, and spatial resolution were determined.this portion of the lab helped in the process of understanding the...