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Lab 9: Hyperspectral Remote Sensing

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Goal Te main goal of this lab is to understand processing and identifying features on hyperspectral images and selected spectral processing basics. Imaging spectrometry , hyperspectral images, and selected spectral processing basics will be performed. FLAASH will be introduced, and used to atmospherically correct a hyperspectral image. Then an introduction in determining the overall state of different vegetation types from their reflectance properties will be done. Methods Basic analysis of spectral processing The first part of this lab introduces basic analysis performed on hyperspectral images. First the image cup95_rd.int is utilized which contains 50 bands(1.99-2.48um) of JPL-calibrated AVIRIS radiance for the Cuprite Mining District in Nevada. First a color image is displayed utilizing bands 183,193,and 207 in that order into the RGB combination. The spectral means of minerals within the area were plotted (Figure 1). Then mean spectra from ROIs was compared to spectral librari...
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Lab 10: Radar Remote Sensing

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Goal The goal of this lab is to understand how to perform basic preprocessing and processing of radar images. Specifically noise reduction through speckle filtering, spectral and spatial enhancement, multi-sensor fusion, texture analysis, polarimetric processing, and slant-range to ground-range conversion. When this lab is completed, the basic knowledge of processing on any type of synthetic aperture radar image will be gained. Methods Speckle Supression In this lab radar imagery is analyzed and processed through Erdas Imagine, and ENVI. Noise reduction methods for preprocessing of radar imagery was performed through speckle filtering, edge enhancement and image enhancement. To perform speckle filtering, Radar speckle suppression tool is used. this removes common salt and pepper effect. this is done by utilizing a calculated coefficient of variation (Figure 1). Figure 1: coefficient of variation is 0.274552. This variable is then used to run the Speckle Suppression Functio...
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Lab 7: Object-based Classification

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Background The goal of this lab is to learn and utilize eCognition, a state of the art object-based processing tool. First  homogeneous spatial and spectral clusters are produced over the image or known as objects. a random forest and support vector machine classifiers will be used which require training samples. Finally an output will be produced showing the newly classified image. Methods First a project was created in eCognition, then objects need to be created on the image, this is done through the process tree utilizing multiresolution segmentation, with a scale parameter of 9. This produced the objects shown in figure 1 and 2. Finally training samples are taken to be put in the process tree. this trains the classifier. Figure 1: This shows a mess of blue over the image, but thwese are all polygons that contain similar pixels. Figure 2: This image shows a zoomed in section of figure 1, this allows the actual polygons to be seen. Next the same process was performed...
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Lab 6: Digital Change Detection

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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...
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Lab 5: classification Accuracy Assessment

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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: ...
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Lab 4: Pixel-Based Supervised Classification

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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...
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Lab 3: Unsupervised Classification

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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 ...
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