{"id":330,"date":"2016-06-30T00:47:09","date_gmt":"2016-06-30T05:47:09","guid":{"rendered":"http:\/\/gisgeography.com\/?p=330"},"modified":"2024-03-10T07:13:50","modified_gmt":"2024-03-10T12:13:50","slug":"supervised-unsupervised-classification-arcgis","status":"publish","type":"post","link":"https:\/\/gisgeography.com\/supervised-unsupervised-classification-arcgis\/","title":{"rendered":"Supervised and Unsupervised Classification in Remote Sensing"},"content":{"rendered":"<style>.kb-image330_593033-19 .kb-image-has-overlay:after{opacity:0.3;}<\/style>\n<figure class=\"wp-block-kadence-image kb-image330_593033-19 size-medium_large\"><img loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"421\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/unuspervised-landcover-example-768x421.gif\" alt=\"Unsupervised Classification Example\" class=\"kb-img wp-image-2773\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/unuspervised-landcover-example-768x421.gif 768w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/unuspervised-landcover-example-300x164.gif 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/unuspervised-landcover-example-678x372.gif 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/unuspervised-landcover-example-1536x842.gif 1536w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/unuspervised-landcover-example-50x27.gif 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/unuspervised-landcover-example-200x110.gif 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/unuspervised-landcover-example-425x233.gif 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/unuspervised-landcover-example-550x301.gif 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/unuspervised-landcover-example-115x63.gif 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/unuspervised-landcover-example-1265x693.gif 1265w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/unuspervised-landcover-example-850x466.gif 850w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \/><\/figure>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading\">Unsupervised vs Supervised Classification in Remote Sensing<\/h2>\n\n\n\n<p>The 3 most common <strong>remote sensing classification<\/strong> methods are:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Unsupervised classification<\/li>\n\n\n\n<li>Supervised classification<\/li>\n\n\n\n<li>Object-based image analysis<\/li>\n<\/ol>\n\n\n\n<p>What are the main differences between <strong>supervised<\/strong> and <strong>unsupervised classification<\/strong>? You can follow along as we classify in ArcGIS.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Aspect<\/th><th>Supervised Classification<\/th><th>Unsupervised Classification<\/th><\/tr><\/thead><tbody><tr><td>Definition<\/td><td>Classification guided by known training samples<\/td><td>Classification without predefined training data<\/td><\/tr><tr><td>Training Data<\/td><td>Requires labeled training data for each class<\/td><td>No labeled training data required<\/td><\/tr><tr><td>Process<\/td><td>Involves training the classifier using labeled samples<\/td><td>Uses clustering algorithms to group pixels<\/td><\/tr><tr><td>User Involvement<\/td><td>Requires user to select training samples<\/td><td>Minimal user intervention during classification<\/td><\/tr><tr><td>Class Information<\/td><td>Prior knowledge of class identities is needed<\/td><td>Classes are discovered from data patterns<\/td><\/tr><tr><td>Accuracy Assessment<\/td><td>Often results in higher accuracy due to training<\/td><td>May have lower accuracy due to lack of training<\/td><\/tr><tr><td>Applicability<\/td><td>Effective for identifying specific classes<\/td><td>Suitable for exploratory data analysis<\/td><\/tr><tr><td>Flexibility<\/td><td>Less flexible, as predefined classes are used<\/td><td>More flexible, as classes are generated dynamically<\/td><\/tr><tr><td>Complexity<\/td><td>Potentially more complex due to training process<\/td><td>Generally simpler as it relies on clustering<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\">The table above highlights the differences between unsupervised and supervised classification<\/figcaption><\/figure>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading\">Supervised Classification in Remote Sensing<\/h2>\n\n\n\n<p>In supervised classification, you select training samples and classify your image based on your chosen samples. Your training samples are key because they will determine which class each pixel inherits in your overall image.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"417\" height=\"209\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/supervised-diagram.png\" alt=\"Supervised Classification Diagram\" class=\"wp-image-842\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/supervised-diagram.png 417w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/supervised-diagram-300x150.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/supervised-diagram-50x25.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/supervised-diagram-200x100.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/supervised-diagram-115x58.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/supervised-diagram-309x155.png 309w\" sizes=\"auto, (max-width: 417px) 100vw, 417px\" \/><\/figure>\n<\/div>\n\n\n<p>When you run a supervised classification, you perform the following 3 steps:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Select training areas<\/li>\n\n\n\n<li>Generate signature file<\/li>\n\n\n\n<li>Classify<\/li>\n<\/ol>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">Step 1. Select training areas<\/h3>\n\n\n\n<p>In this step, you find training samples for each land cover class you want to create. For example, draw a polygon for an urban area such as a road or parking lot. Then, continue drawing urban areas representative of the entire image. Make sure it&#8217;s not just a single area.<\/p>\n\n\n\n<p>Once you have enough samples for urban areas, you can start adding training samples for another land cover class. For example, you can add polygons over treed areas for the &#8220;forest&#8221; class.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"678\" height=\"373\" src=\"http:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/training-sample-manager-2-678x373.gif\" alt=\"Training Sample Manager\" class=\"wp-image-2780\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/training-sample-manager-2-678x373.gif 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/training-sample-manager-2-300x165.gif 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/training-sample-manager-2-50x28.gif 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/training-sample-manager-2-70x40.gif 70w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/training-sample-manager-2-200x110.gif 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/training-sample-manager-2-425x234.gif 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/training-sample-manager-2-550x303.gif 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/training-sample-manager-2-115x63.gif 115w\" sizes=\"auto, (max-width: 678px) 100vw, 678px\" \/><\/figure>\n\n\n\n<p>If you&#8217;re using ArcGIS, the steps are:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Beforehand, you must enable the Image Analysis Toolbar (Windows \u2023 Image Analysis).<\/li>\n\n\n\n<li>Add the training sample manager. Then, click the <em>&#8220;Draw Polygon&#8221;<\/em> icon to add training samples.<\/li>\n\n\n\n<li>For each land cover class, draw polygons. Then, merge them into a single class.<\/li>\n<\/ul>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">Step 2. Generate signature file<\/h3>\n\n\n\n<p>At this point, you should have training samples for each class. The signature file is what holds all the training sample data that you&#8217;ve collected up to this point. It&#8217;s a way to save your samples for you to work on at a later time.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"374\" height=\"95\" src=\"http:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/create-sig.gif\" alt=\"Create Signature File\" class=\"wp-image-2782\"\/><\/figure>\n\n\n\n<p>The steps in ArcGIS are:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Create a signature file by clicking the &#8220;create a signature file&#8221; icon.<\/li>\n<\/ul>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">Step 3. Classify<\/h3>\n\n\n\n<p>The most common supervised classification methods include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Maximum likelihood<\/li>\n\n\n\n<li>Iso cluster<\/li>\n\n\n\n<li>Class probability<\/li>\n\n\n\n<li>Principal components<\/li>\n\n\n\n<li>Support vector machine (SVM)<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright is-resized\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/lcv-4-425x262.png\" alt=\"Supervised Classification Example\" class=\"wp-image-2775\" style=\"width:319px;height:197px\" width=\"319\" height=\"197\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/lcv-4-425x262.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/lcv-4-300x185.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/lcv-4-50x31.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/lcv-4-200x123.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/lcv-4-115x71.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/lcv-4.png 520w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/lcv-4-252x155.png 252w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/lcv-4-500x308.png 500w\" sizes=\"auto, (max-width: 319px) 100vw, 319px\" \/><\/figure>\n<\/div>\n\n\n<p>As shown in previous studies, <a href=\"https:\/\/www.tandfonline.com\/doi\/abs\/10.1080\/01431160512331314083\" target=\"_blank\" rel=\"noopener noreferrer\">SVM achieves one of the highest levels of accuracy<\/a> for prediction. But each option has its own advantages. Overall, it&#8217;s best to test each one for yourself.<\/p>\n\n\n\n<p>In this step, the input is your signature file which has the training samples. If you run it and don&#8217;t like the result, then you may have to verify your training samples. Ultimately, this is the best way to know where your classification errors exist.<\/p>\n\n\n\n<p>The steps in ArcGIS are:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Run the &#8220;classify&#8221; tool. Next, your input will be the signature file.<\/li>\n<\/ul>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading\">Unsupervised Classification in Remote Sensing<\/h2>\n\n\n\n<p>[Unsupervised classification generates clusters based on similar spectral characteristics inherent in the image. Then, you classify each cluster without providing training samples of your own.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"213\" height=\"215\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/unsupervised-diagram.png\" alt=\"Unsupervised Classification Diagram\" class=\"wp-image-834\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/unsupervised-diagram.png 213w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/unsupervised-diagram-150x150.png 150w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/unsupervised-diagram-50x50.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/unsupervised-diagram-198x200.png 198w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/unsupervised-diagram-115x116.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/unsupervised-diagram-154x155.png 154w\" sizes=\"auto, (max-width: 213px) 100vw, 213px\" \/><\/figure>\n<\/div>\n\n\n<p>The steps for running an unsupervised classification are:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Generate clusters<\/li>\n\n\n\n<li>Assign classes<\/li>\n<\/ol>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">Step 1. Generate clusters<\/h3>\n\n\n\n<p>In this step, the software clusters pixels into a set number of classes. So, the first step is to assign the number of classes you want to generate. Also, you have to identify which bands you want to use.<\/p>\n\n\n\n<p>If you&#8217;re using Landsat, here is a list of <a href=\"https:\/\/gisgeography.com\/landsat-8-bands-combinations\/\">Landsat bands<\/a>. For Sentinel, here are <a href=\"https:\/\/gisgeography.com\/sentinel-2-bands-combinations\/\">Sentinel-2 bands<\/a>. We also have a <a href=\"https:\/\/gisgeography.com\/spectral-signature\/\">handy guide on spectral signatures<\/a> which explains which spectral bands are useful for classifying different classes.<\/p>\n\n\n\n<p>In ArcGIS, the steps for generating clusters are:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>First, you have to activate the spatial analyst extension (Customize \u2023 Extensions \u2023 Spatial Analyst).<\/li>\n\n\n\n<li>In this unsupervised classification example, we use Iso-clusters (Spatial Analysis Tools \u2023 Multivariate \u2023 Iso clusters).<\/li>\n<\/ul>\n\n\n\n<p><strong>INPUT<\/strong>: The image you want to classify.<br><strong>NUMBER OF CLASSES<\/strong>: The number of classes you want to generate during the unsupervised classification. For example, if you are working with <a href=\"https:\/\/gisgeography.com\/multispectral-vs-hyperspectral-imagery-explained\/\">multispectral imagery<\/a> (red, green, blue, and NIR bands), then the number here will be 40 (4 classes x 10).<br><strong>MINIMUM CLASS SIZE<\/strong>: This is the number of pixels to make a unique class.<\/p>\n\n\n\n<p>When you click OK, it creates clusters based on your input parameters. But you still need to identify which land cover classes each cluster belongs to.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">Step 2. Assign classes<\/h3>\n\n\n\n<p>Now that you have clusters, the last step is to identify each class from the iso-clusters output. Here are some tips to make this step easier:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>In general, it helps to select colors for each class. For example, set water as blue for each class.<\/li>\n\n\n\n<li>After setting each one of your classes, we can merge the classes by using the reclassify tool.<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"425\" height=\"262\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/lcv-2-425x262.png\" alt=\"Unsupervised Classification Example\" class=\"wp-image-2774\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/lcv-2-425x262.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/lcv-2-300x185.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/lcv-2-50x31.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/lcv-2-200x123.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/lcv-2-115x71.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/lcv-2.png 520w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/lcv-2-252x155.png 252w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/05\/lcv-2-500x308.png 500w\" sizes=\"auto, (max-width: 425px) 100vw, 425px\" \/><\/figure>\n<\/div>\n\n\n<p>If land cover appears in 2 classes, you will need to make some manual edits. For example, if vegetation was mistakenly classified as water (perhaps algae in the water), you will have to manually edit the polygon.<\/p>\n\n\n\n<p>In most cases, it helps to convert the raster to vector and use the editing toolbar. You can split polygons to help properly identify them.<\/p>\n\n\n\n<p><strong>READ MORE:<\/strong><a href=\"https:\/\/gisgeography.com\/free-global-land-cover-land-use-data\/\" target=\"_blank\" rel=\"noopener noreferrer\"> 9 Free Global Land Cover \/ Land Use Data Sets<\/a><\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Unsupervised Image Classification in Remote Sensing\" width=\"720\" height=\"405\" src=\"https:\/\/www.youtube.com\/embed\/fZwdsSNr7vk?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading\">Land Cover Classification with Supervised and Unsupervised Methods<\/h2>\n\n\n\n<p>Today, you&#8217;ve learned how to create a land cover using supervised and unsupervised classification.<\/p>\n\n\n\n<p>But the next step forward is to use <a href=\"https:\/\/gisgeography.com\/obia-object-based-image-analysis-geobia\/\">object-based image analysis<\/a>. This is the most modern technique in <a href=\"https:\/\/gisgeography.com\/image-classification-techniques-remote-sensing\/\">image classification<\/a>.<\/p>\n\n\n\n<p>Don&#8217;t stop here. Read some more of our comprehensive articles on remote sensing:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/gisgeography.com\/remote-sensing-applications\/\">100 Remote Sensing Applications &amp; Uses<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/gisgeography.com\/remote-sensing-earth-observation-guide\/\">What is Remote Sensing? A Guide to Earth Observation<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/gisgeography.com\/passive-active-sensors-remote-sensing\/\">Passive vs Active Sensors in Remote Sensing<\/a><\/li>\n<\/ul>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Supervised classification creates training areas, signature file and classifies. Unsupervised classification generate clusters and assigns classes.<\/p>\n","protected":false},"author":2,"featured_media":2773,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","_kad_post_transparent":"default","_kad_post_title":"default","_kad_post_layout":"default","_kad_post_sidebar_id":"","_kad_post_content_style":"default","_kad_post_vertical_padding":"default","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","footnotes":""},"categories":[92],"tags":[67],"class_list":["post-330","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-remote-sensing","tag-image-classification"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Supervised and Unsupervised Classification in Remote Sensing - GIS Geography<\/title>\n<meta name=\"description\" content=\"Supervised classification creates training areas, signature file and classifies. 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