GEOG 438: Advanced Remote Sensing

Background The goal of this lab is to develop an image that shows change detection of land surface features over time. First a means of performing a qualitative change detection will be performed, then we will quantify post-classification change detection, and a model will be developed that will map detailed change of land use land cover images over time. Methods Part 1: Change detection using Write Function Memory Insertion The first part of the lab, a Write Function Memory Insertion is utilized. This processes utilizes near infrared bands, when the two images are brought into the Write Function, an output image is produced, and the image has areas highlighted in pinkish red, these are areas that have experienced change. Specifically in this lab, ec_envs1991.img and ec_envs_2011.img were used to perform this change detection. Part 2: Post-classification comparison change detection   In this part of the lab a From-to change detection using classified images of the Milw...

Goal The goal of this lab is to understand how to evaluate the accuracy of classification results. Accuracy assessments are mandatory after performing an image classification, this is a portion of the post-processing stage of remotely sensed data. Methods An accuracy assessment is performed on a supervised and an unsupervised classification from labs 3 and 4. This is done by utilizing the accuracy assessment tool in Erdas. A high resolution reference image of the same study area is used to perform the assessment. This is done by generating  random points on the reference image, 125 random points in this lab,  and classifying those points based on the high resolution image utilizing the same classification codes for water, forest, agriculture, etc. (Table 1). By identifying these points, a comparison report is then produced (Figure 1). This allows for an error matrix to be created from the results of comparing the random points to the classified images. Table 1: ...

Pixel-based Supervised Classification Goal The goal of this lab is to continue in learning the art of extracting biophysical and sociocultural information from remotely sensed images through pixel-based supervised classification. This is one of the most important skills in remote sensing. In the first portion of the lab, skills will be developed in selecting training samples to train a supervised classifier. Next the quality of the signatures will be evaluated. Then meaningful Land use/land cover classes will be produced in the final output. A Landsat 7 (ETM+) image captured on June 9, 2000 will be utilized with an Area of interest covering Eau Claire and Chippewa counties this image is ec_cpw2000.img.   Methods Part 1: Collection of training samples for supervised classification In this first part of the lab, training samples (spectral signatures) are collected to train a maximum likelihood classifier which will be used to classify the ec_cpw2000.img image. This...