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UNC-Chapel Hill GEOG 370 - Remote Sensing Part 4 - Classification & Vegetation Indices

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Remote Sensing Part 4Classification IntroductionNon-Remote Sensing Classification ExampleSlide 4Scanning the FishSlide 6Slide 7Slide 8Slide 9Slide 10Slide 11How does this relate to remote sensing?Slide 13Slide 14Imagery ClassificationNotesUnsupervised ClassificationA Computer Algorithm Finds ClustersSlide 19Problems with Unsupervised ClassificationSupervised ClassficationSupervised ClassificationSlide 23Problems with Supervised ClassificationWhat is the computer actually doing?Example: Remote Sensing of CloudsSupervised Classification: Training SamplesSlide 28Slide 29Example Classification Results (Bangkok, Thailand)Accuracy AssessmentsHow are accuracy assessments done?Classification ChallengesIkonos Imagery: Glacier National ParkClassification ResultsAccuracy Assessment TableVegetation IndicesSlide 38Slide 39Slide 40Slide 41Slide 42Remote SensingPart 4Classification & Vegetation IndicesClassification Introduction•Humans are classifiers by nature - we’re always putting things into categories•To classify things, we use sets of criteria •Examples: –Classifying people by age, gender, race, job/career, etc.–Criteria might include appearance, style of dress, pitch of voice, build, hair style, language/lexicon, etc.–Ambiguity comes from:•1) our classification system (i.e., what classes we choose) •2) our criteria (some criteria don’t differentiate people with complete accuracy)•3) our data (i.e., people who fit multiple categories and people who fit no categories)Non-Remote Sensing Classification Example•“Sorting incoming Fish on a conveyor according to species using optical sensing” Sea bassSpecies Salmon** The following data are just hypotheticalMethods–Set up a camera and take some sample images to extract features•Length•Lightness•Width•Number and shape of fins•Position of the mouth, etc…Scanning the Fish•Classification #1–Use the length of the fish as a possible feature for discrimination•Fish length alone is a poor feature for classifying fish type–Using only length we would be correct 50-60% of the time–That’s not great because random guessing (i.e., flipping a coin) would be right ~50% of the time if there are an equal number of each fish type•Classification #2–Use the lightness (i.e., color) of the fish as a possible feature for discrimination•Fish lightness alone is a pretty good feature for classifying fish by type–Using only lightness we would be correct ~ 80% of the time•Classification #3–Use the width & lightness (i.e., color) of the fish as possible features for discrimination•Fish lightness AND fish width do a very good job of classifying fish by type–Using lightness AND width we would be correct ~90% of the timeHow does this relate to remote sensing?•Instead of fish types, we are typically interested in land cover–For example: forests, crops, urban areas•Instead of fish characteristics we have reflectance in the spectral bands collected by the sensor–For example: Landsat TM bands 1-6 instead of fish length, width, lightness, etc.Imagery Classification•Two main types of classification–Unsupervised•Classes based on statistics inherent in the remotely sensed data itself•Classes do not necessarily correspond to real world land cover types–Supervised•A classification algorithm is “trained” using ground truth data•Classes correspond to real world land cover types determined by the userNotes•For ease of display the following examples show just 2 bands: –one band on the X-axis–one band on the Y-axis•In reality computers use all bands when doing classifications•These types of graphs are often called feature space•The points displayed on the graphs relate to pixels from an image•The term cloud sometimes refers to the amorphous blob(s) of pixels in the feature spaceUnsupervised Classification•Classes are created based on the locations of the pixel data in feature space Red BV’sInfrared BV’s00 255255vA Computer Algorithm Finds ClustersRed BV’sInfrared BV’s00 255255vUnsupervised ClassificationUnsupervised Classification•Attribution phase – performed by humanwaterSoilagricultureforestRed BV’sInfrared BV’s00 255255Problems with Unsupervised ClassificationRed BV’sInfrared BV’s00 255255vThe computer may consider these 2 clusters (forest and agriculture) as one clusterThe computer may consider this cluster (soil) to be 2 clustersSupervised Classfication•We “train” the computer program using ground truth data•I.e., we tell the computer what our classes (e.g., trees, soil, agriculture, etc.) “look like”Coniferous treesDeciduous treesSupervised ClassificationRed BV’sInfrared BV’s00 255255vSample pixelsOther pixelsSupervised Classification•No attribution phase necessary because we define the classes before-handwaterSoilagricultureforestRed BV’sInfrared BV’s00 255255Problems with Supervised ClassificationRed BV’sInfrared BV’s00 255255vforestagriwaterSoilWhat’s this?vWhat is the computer actually doing?•This classification generates statistics for the center, the size, and the shape of the sample pixel clouds•The computer will then classify all the rest of the pixels in the image using these statistical valuesExample: Remote Sensing of CloudsSupervised Classification: Training Samples•Users survey (using GPS) areas of “pure” land cover for all possible land cover types in an image•OR•Users “heads-up” digitize “pure” areas using expert knowledge and/or higher spatial resolution imagery•The rest of the image is classified based on the spectral characteristics of the training sitesClassification of Nang Rong imagery(a) Nov 1979 (c) Nov 2001Shown are Landsat MSS,TM,and ETM Image Classification Results(a) Nov 1992Upland AgForestRiceWaterBuilt-upLand Use/cover Change in Nang Rong, Thailand1954 1994Example Classification Results (Bangkok, Thailand)Accuracy Assessments•After classifying an image we want to know how well the classification worked•To find out we must conduct an accuracy assessmentHow are accuracy assessments done?•Basically we need to compare the classification results with real land cover•As with training data, the real land cover data can be field data (best) or samples from higher spatial resolution imagery (easier)•What points should we use for the accuracy assessment?–Possible options (there are others)•Random points•Stratified random points (each class represented with an equal


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UNC-Chapel Hill GEOG 370 - Remote Sensing Part 4 - Classification & Vegetation Indices

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