{"id":14495,"date":"2014-07-09T07:05:06","date_gmt":"2014-07-09T12:05:06","guid":{"rendered":"http:\/\/gisgeography.com\/?p=14495"},"modified":"2025-04-01T19:37:26","modified_gmt":"2025-04-02T00:37:26","slug":"obia-object-based-image-analysis-geobia","status":"publish","type":"post","link":"https:\/\/gisgeography.com\/obia-object-based-image-analysis-geobia\/","title":{"rendered":"OBIA &#8211; Object-Based Image Analysis (GEOBIA)"},"content":{"rendered":"<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1057\" height=\"578\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Object-Based-Image-Analysis-Geobia.jpg\" alt=\"OBIA Object-Based Image Analysis Geobia\" class=\"wp-image-96818\" style=\"width:700px\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Object-Based-Image-Analysis-Geobia.jpg 1057w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Object-Based-Image-Analysis-Geobia-300x164.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Object-Based-Image-Analysis-Geobia-678x371.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Object-Based-Image-Analysis-Geobia-768x420.jpg 768w\" sizes=\"auto, (max-width: 1057px) 100vw, 1057px\" \/><\/figure>\n<\/div>\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\">Think objects, not pixels<\/h3>\n\n\n\n<p>How amazing would it be if you could digitize all your features in an image with just a click of a button?<\/p>\n\n\n\n<p>On top of that, you can classify each feature with another click of a button.<\/p>\n\n\n\n<p>Sounds like magic?  But these two processes are segmentation and classification performed in <strong>Object-based Image Analysis (OBIA)<\/strong>.  <\/p>\n\n\n\n<p>Let&#8217;s examine what it is and how you can use it to get your work done more efficiently and accurately.<\/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\">Segmentation is key to classification<\/h3>\n\n\n\n<p>Human visual perception almost always outperforms computer vision algorithms.<\/p>\n\n\n\n<p>For example, your eyes know a river when they see one. But a computer can&#8217;t recognize rivers from lakes.<\/p>\n\n\n\n<p>&#8230;Or can it?<\/p>\n\n\n\n<p>Traditional pixel-based <a href=\"https:\/\/gisgeography.com\/image-classification-techniques-remote-sensing\/\">image classification<\/a> assigns a land cover class per pixel.  All pixels are the same size, same shape, and don&#8217;t have any concept of their neighbors.<\/p>\n\n\n\n<p>However, OBIA segments an image grouping small pixels together into vector objects.  Instead of a per-pixel basis, segmentation automatically\u200b digitizes the image for you. <\/p>\n\n\n<div class=\"wp-block-image size-large wp-image-14776\">\n<figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1000\" height=\"550\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Segmentation.jpg\" alt=\"OBIA Segmentation\" class=\"wp-image-96797\" style=\"width:700px\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Segmentation.jpg 1000w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Segmentation-300x165.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Segmentation-678x373.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Segmentation-768x422.jpg 768w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><figcaption class=\"wp-element-caption\">Object-Based Image Analysis (OBIA) segmentation is a process that groups similar pixels into objects<\/figcaption><\/figure>\n<\/div>\n\n\n<p>What segmentation does is replicate what your eyes are doing.  <\/p>\n\n\n\n<p>But with these\u200b segmented objects, you use their spectral, geometrical, and spatial properties to classify them into land cover.<\/p>\n\n\n<div class=\"wp-block-image size-large wp-image-14778\">\n<figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1000\" height=\"435\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Classification-Final.jpg\" alt=\"OBIA Classification Final\" class=\"wp-image-96813\" style=\"width:700px\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Classification-Final.jpg 1000w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Classification-Final-300x131.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Classification-Final-678x295.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Classification-Final-768x334.jpg 768w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><figcaption class=\"wp-element-caption\">OBIA classification uses shape, size, and spectral properties of objects to classify each object<\/figcaption><\/figure>\n<\/div>\n\n\n<p>Otherwise, when you use traditional image classification techniques, you often get a salt-and-pepper look in the classification result.<\/p>\n\n\n\n<p>To recap, the two basic principles of OBIA are:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>SEGMENTATION<\/strong>: Break the image up into objects representing land-based features.<\/li>\n\n\n\n<li><strong>CLASSIFICATION<\/strong>: Classify those objects using their shape, size, spatial and spectral properties.<\/li>\n<\/ul>\n\n\n\n<p>Let&#8217;s dig a bit deeper into these two concepts.<\/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\">Generate meaningful objects with segmentation<\/h3>\n\n\n\n<p>When you segment an image, the process groups pixels to form objects. Suddenly, land cover features start popping out, similar to how your eyes process your surroundings.<\/p>\n\n\n\n<p>For this 50cm resolution image, the <strong>multi-resolution segmentation<\/strong> algorithm breaks up an image in <a href=\"http:\/\/www.ecognition.com\/\" target=\"_blank\" rel=\"noopener noreferrer\">eCognition Definiens Developer<\/a>.  Based on your compactness and shape settings, this is the preliminary step in OBIA.  <\/p>\n\n\n\n<p>How big do you want the objects to be? There&#8217;s a scale parameter that you can estimate to generate more meaningful objects. <\/p>\n\n\n\n<p>Also, you can configure weights for all the layers you want to segment.  This means that you don&#8217;t only have to segment by red, green, or blue, but you can also segment a DEM, DSM, NIR, or even LiDAR intensity.<\/p>\n\n\n<div class=\"wp-block-image size-large wp-image-14782\">\n<figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1078\" height=\"565\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/Ecognition-Segmentation-Tool.jpg\" alt=\"Ecognition Segmentation Tool\" class=\"wp-image-96811\" style=\"width:700px\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/Ecognition-Segmentation-Tool.jpg 1078w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/Ecognition-Segmentation-Tool-300x157.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/Ecognition-Segmentation-Tool-678x355.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/Ecognition-Segmentation-Tool-768x403.jpg 768w\" sizes=\"auto, (max-width: 1078px) 100vw, 1078px\" \/><figcaption class=\"wp-element-caption\">Trimble Ecognition multi-resolution segmentation with customized image object fusion<\/figcaption><\/figure>\n<\/div>\n\n\n<p>Similarly, the <a href=\"http:\/\/desktop.arcgis.com\/en\/arcmap\/10.3\/tools\/spatial-analyst-toolbox\/segment-mean-shift.htm\" target=\"_blank\" rel=\"noopener noreferrer\">segment mean shift<\/a> in ArcGIS is an alternative method of object-based image analysis. However, you don&#8217;t have as many options as Trimble eCognition.<\/p>\n\n\n\n<p>For example, you can&#8217;t set the weights of several layers when you run the process.  What you can do is set the spectral and spatial detail, along with the minimum size in pixels.  With a bit of trial and error, we used the raster calculator to set custom weights using an nDSM and the red band as input.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1078\" height=\"557\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/ArcGIS-Segmentation.jpg\" alt=\"ArcGIS Segmentation with Segment Mean Shift Algorithm\" class=\"wp-image-96809\" style=\"width:700px\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/ArcGIS-Segmentation.jpg 1078w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/ArcGIS-Segmentation-300x155.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/ArcGIS-Segmentation-678x350.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/ArcGIS-Segmentation-768x397.jpg 768w\" sizes=\"auto, (max-width: 1078px) 100vw, 1078px\" \/><figcaption class=\"wp-element-caption\">ArcGIS segmentation using the Segment Mean Shift algorithm<\/figcaption><\/figure>\n<\/div><\/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\">Classify land cover features<\/h3>\n\n\n\n<p>After you segment the image, it&#8217;s time to classify each object.  You are now able to classify because each object has statistics associated with it.  For example, you can classify objects based on geometry, area, color, shape, texture, adjacency, and more.  <\/p>\n\n\n\n<p>While options are limiting in ArcGIS, this is where the true power lies in Trimble eCognition.  In this example, there are seemingly endless statistics to classify buildings.  But which statistic is the correct one to use?<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized has-custom-border\"><img loading=\"lazy\" decoding=\"async\" width=\"1096\" height=\"573\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Image-Object-Information.jpg\" alt=\"OBIA Image Object Information\" class=\"has-border-color has-theme-palette-6-border-color wp-image-96826\" style=\"border-width:1px;width:700px\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Image-Object-Information.jpg 1096w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Image-Object-Information-300x157.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Image-Object-Information-678x354.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Image-Object-Information-768x402.jpg 768w\" sizes=\"auto, (max-width: 1096px) 100vw, 1096px\" \/><figcaption class=\"wp-element-caption\">Each object in OBIA has statistics associated with it. On the right, is image object information for the selected building regarding its shape, size and spectral characteristics<\/figcaption><\/figure>\n<\/div>\n\n\n<p>Admittedly, there is no <strong><em>best<\/em><\/strong> way to classify land cover features using OBIA. However, analysts frequently use these statistics to classify land cover using OBIA:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>WATER<\/strong> is flat (low nDSM), it accumulates into depressions (high TWI or low TPI), it has a low temperature (thermal infrared &#8211; TIRS) and it has high near-infrared absorption (negative <a href=\"https:\/\/gisgeography.com\/how-to-ndvi-maps-arcgis\/\">NDVI<\/a>)<\/li>\n\n\n\n<li><strong>TREES<\/strong> have varying heights (high nDSM standard deviation) and have high near-infrared reflectance (high NDVI).<\/li>\n\n\n\n<li><strong>BUILDINGS<\/strong> are often rectangular (high rectangular fit), are tall (high nDSM), and have high slopes.<\/li>\n\n\n\n<li><strong>GRASS<\/strong> is short (low nDSM), it&#8217;s flat (low nDSM standard deviation), and has moderate near-infrared reflectance (moderate NDVI).<\/li>\n\n\n\n<li><strong>ROADS<\/strong> reflect a lot of light (high RGB), they are flat (low nDSM), have a high light intensity, and have a low or negative NDVI.<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1000\" height=\"523\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/Ecognition-Segmentation-Classification.jpg\" alt=\"Ecognition Segmentation Classification\" class=\"wp-image-96820\" style=\"width:700px\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/Ecognition-Segmentation-Classification.jpg 1000w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/Ecognition-Segmentation-Classification-300x157.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/Ecognition-Segmentation-Classification-678x355.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/Ecognition-Segmentation-Classification-768x402.jpg 768w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/figure>\n<\/div>\n\n\n<p>You can set up rulesets, which are a set of pre-defined steps to segment and classify objects.  Similar to <a href=\"https:\/\/gisgeography.com\/arcgis-model-builder-custom-toolbox-python\/\">ModelBuilder in ArcGIS<\/a>, it steps through each process until it finishes.<\/p>\n\n\n\n<p>Alternatively, Trimble ECognition has a <a href=\"http:\/\/gisgeography.com\/nearest-neighbor-classification-guide-ecognition\/\">nearest neighbor classification<\/a> where you add and classify based on defined samples.<\/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\">Sharper images = More advanced image classification<\/h3>\n\n\n\n<p>In 1972, Landsat-1 sparked a revolution in how we monitor our Earth. With the US government relaxing regulations on high-resolution <a href=\"https:\/\/gisgeography.com\/free-satellite-imagery-data-list\/\">satellite data<\/a>, the uptrend in sharper imagery is simply remarkable.<\/p>\n\n\n\n<p>It\u2019s not only satellites like Worldview or <a href=\"https:\/\/gisgeography.com\/planet-labs-imagery\/\">Planet Labs<\/a> but the usage of <a href=\"https:\/\/gisgeography.com\/lidar-light-detection-and-ranging\/\">LiDAR<\/a> and drones like DJI are seeing a healthy uptick. And the way we classify images has progressed from unsupervised to more sophisticated <strong>object-based image classification<\/strong>.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"116\" src=\"http:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/image-classification-timeline3.png\" alt=\"Image Classification Timeline\" class=\"wp-image-859\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/image-classification-timeline3.png 500w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/image-classification-timeline3-300x70.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/image-classification-timeline3-50x12.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/image-classification-timeline3-200x46.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/image-classification-timeline3-425x99.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/image-classification-timeline3-115x27.png 115w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><\/figure>\n<\/div>\n\n\n<p>When a single pixel contained several buildings in a <a href=\"http:\/\/gisgeography.com\/landsat-program-satellite-imagery-bands\/\">Landsat-1<\/a> scene, there wasn\u2019t a need to do object-based image analysis. However, the new breed of high-resolution data requires object-based image analysis.<\/p>\n\n\n\n<p>For example, a Landsat-1 scene couldn&#8217;t decipher between buildings from parks.  In this case, <a href=\"http:\/\/gisgeography.com\/supervised-unsupervised-classification-arcgis\/\">unsupervised and supervised classification<\/a> was enough.  But now, you segment and classify high-resolution data using OBIA for more meaningful land cover. This is the trend in the remote sensing community.<\/p>\n\n\n\n<p>Otherwise, traditional image classification techniques give unwanted salt and pepper classification.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"576\" height=\"297\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/Unsupervised-Classification-Salt-Pepper.jpg\" alt=\"Unsupervised classification with unwanted speckle classification\" class=\"wp-image-96848\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/Unsupervised-Classification-Salt-Pepper.jpg 576w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/Unsupervised-Classification-Salt-Pepper-300x155.jpg 300w\" sizes=\"auto, (max-width: 576px) 100vw, 576px\" \/><figcaption class=\"wp-element-caption\">Unsupervised classification with unwanted speckle classification<\/figcaption><\/figure>\n<\/div><\/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\">OBIA &#8211; Object-based Image Analysis<\/h3>\n\n\n\n<p>OBIA started with cellular biologists dissecting image scans. <strong>GEOBIA (Geographic Object-Based Image Analysis)<\/strong> distinguishes it from its medical origin.<\/p>\n\n\n\n<p>Crisper images, more spectral bands, and an explosion of data acquisitions can help solve today\u2019s problems.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"116\" src=\"http:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/remote-sensing-trends.png\" alt=\"Remote Sensing Trends\" class=\"wp-image-855\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/remote-sensing-trends.png 500w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/remote-sensing-trends-300x70.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/remote-sensing-trends-50x12.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/remote-sensing-trends-200x46.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/remote-sensing-trends-425x99.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/remote-sensing-trends-115x27.png 115w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><\/figure>\n<\/div>\n\n\n<p>To make sense of all this information, we need OBIA or object-based image analysis to automate some of the work for us.<\/p>\n\n\n\n<p>As each day passes by, satellites collect ridiculous volumes of data silently in orbit\u2026 But what good is satellite data if you don\u2019t know how to use it?<\/p>\n\n\n\n<p>OBIA is about mass production.  You create a ruleset, run it, and edit your classification as necessary.<\/p>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Object-based Image Analysis (OBIA) segments an image by grouping pixels together into vector objects. Using these objects, you classify as land cover types.<\/p>\n","protected":false},"author":2,"featured_media":96818,"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-14495","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>OBIA - Object-Based Image Analysis (GEOBIA) - GIS Geography<\/title>\n<meta name=\"description\" content=\"Object-based Image Analysis (OBIA) segments an image by grouping pixels together into vector objects. Using these objects, you classify as land cover types.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/gisgeography.com\/obia-object-based-image-analysis-geobia\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"OBIA - Object-Based Image Analysis (GEOBIA) - GIS Geography\" \/>\n<meta property=\"og:description\" content=\"Object-based Image Analysis (OBIA) segments an image by grouping pixels together into vector objects. Using these objects, you classify as land cover types.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/gisgeography.com\/obia-object-based-image-analysis-geobia\/\" \/>\n<meta property=\"og:site_name\" content=\"GIS Geography\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/gisgeography\" \/>\n<meta property=\"article:author\" content=\"https:\/\/www.facebook.com\/gisgeography\" \/>\n<meta property=\"article:published_time\" content=\"2014-07-09T12:05:06+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-04-02T00:37:26+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Object-Based-Image-Analysis-Geobia.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1057\" \/>\n\t<meta property=\"og:image:height\" content=\"578\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"GISGeography\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@https:\/\/twitter.com\/GisGeography\" \/>\n<meta name=\"twitter:site\" content=\"@GisGeography\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"GISGeography\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/gisgeography.com\\\/obia-object-based-image-analysis-geobia\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/gisgeography.com\\\/obia-object-based-image-analysis-geobia\\\/\"},\"author\":{\"name\":\"GISGeography\",\"@id\":\"https:\\\/\\\/gisgeography.com\\\/#\\\/schema\\\/person\\\/9e7385da3acea92bc45d45be5dfe191e\"},\"headline\":\"OBIA &#8211; Object-Based Image Analysis (GEOBIA)\",\"datePublished\":\"2014-07-09T12:05:06+00:00\",\"dateModified\":\"2025-04-02T00:37:26+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/gisgeography.com\\\/obia-object-based-image-analysis-geobia\\\/\"},\"wordCount\":1072,\"commentCount\":11,\"publisher\":{\"@id\":\"https:\\\/\\\/gisgeography.com\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/gisgeography.com\\\/obia-object-based-image-analysis-geobia\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/gisgeography.com\\\/wp-content\\\/uploads\\\/2014\\\/07\\\/OBIA-Object-Based-Image-Analysis-Geobia.jpg\",\"keywords\":[\"Image Classification\"],\"articleSection\":[\"Remote Sensing\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/gisgeography.com\\\/obia-object-based-image-analysis-geobia\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/gisgeography.com\\\/obia-object-based-image-analysis-geobia\\\/\",\"url\":\"https:\\\/\\\/gisgeography.com\\\/obia-object-based-image-analysis-geobia\\\/\",\"name\":\"OBIA - Object-Based Image Analysis (GEOBIA) - GIS Geography\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/gisgeography.com\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/gisgeography.com\\\/obia-object-based-image-analysis-geobia\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/gisgeography.com\\\/obia-object-based-image-analysis-geobia\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/gisgeography.com\\\/wp-content\\\/uploads\\\/2014\\\/07\\\/OBIA-Object-Based-Image-Analysis-Geobia.jpg\",\"datePublished\":\"2014-07-09T12:05:06+00:00\",\"dateModified\":\"2025-04-02T00:37:26+00:00\",\"description\":\"Object-based Image Analysis (OBIA) segments an image by grouping pixels together into vector objects. Using these objects, you classify as land cover types.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/gisgeography.com\\\/obia-object-based-image-analysis-geobia\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/gisgeography.com\\\/obia-object-based-image-analysis-geobia\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/gisgeography.com\\\/obia-object-based-image-analysis-geobia\\\/#primaryimage\",\"url\":\"https:\\\/\\\/gisgeography.com\\\/wp-content\\\/uploads\\\/2014\\\/07\\\/OBIA-Object-Based-Image-Analysis-Geobia.jpg\",\"contentUrl\":\"https:\\\/\\\/gisgeography.com\\\/wp-content\\\/uploads\\\/2014\\\/07\\\/OBIA-Object-Based-Image-Analysis-Geobia.jpg\",\"width\":1057,\"height\":578,\"caption\":\"OBIA Object-Based Image Analysis Geobia\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/gisgeography.com\\\/obia-object-based-image-analysis-geobia\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/gisgeography.com\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Remote Sensing\",\"item\":\"https:\\\/\\\/gisgeography.com\\\/category\\\/remote-sensing\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"OBIA &#8211; Object-Based Image Analysis (GEOBIA)\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/gisgeography.com\\\/#website\",\"url\":\"https:\\\/\\\/gisgeography.com\\\/\",\"name\":\"GIS Geography\",\"description\":\"Geographic Information Systems\",\"publisher\":{\"@id\":\"https:\\\/\\\/gisgeography.com\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/gisgeography.com\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/gisgeography.com\\\/#organization\",\"name\":\"GIS Geography\",\"url\":\"https:\\\/\\\/gisgeography.com\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/gisgeography.com\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/gisgeography.com\\\/wp-content\\\/uploads\\\/2015\\\/11\\\/cropped-GIS-Geography-Logo0.png\",\"contentUrl\":\"https:\\\/\\\/gisgeography.com\\\/wp-content\\\/uploads\\\/2015\\\/11\\\/cropped-GIS-Geography-Logo0.png\",\"width\":500,\"height\":100,\"caption\":\"GIS Geography\"},\"image\":{\"@id\":\"https:\\\/\\\/gisgeography.com\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/www.facebook.com\\\/gisgeography\",\"https:\\\/\\\/x.com\\\/GisGeography\"]},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/gisgeography.com\\\/#\\\/schema\\\/person\\\/9e7385da3acea92bc45d45be5dfe191e\",\"name\":\"GISGeography\",\"description\":\"We help you learn Geographic Information Systems and remote sensing\",\"sameAs\":[\"https:\\\/\\\/www.facebook.com\\\/gisgeography\",\"https:\\\/\\\/x.com\\\/https:\\\/\\\/twitter.com\\\/GisGeography\"],\"url\":\"https:\\\/\\\/gisgeography.com\\\/author\\\/gisgeo\\\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"OBIA - Object-Based Image Analysis (GEOBIA) - GIS Geography","description":"Object-based Image Analysis (OBIA) segments an image by grouping pixels together into vector objects. Using these objects, you classify as land cover types.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/gisgeography.com\/obia-object-based-image-analysis-geobia\/","og_locale":"en_US","og_type":"article","og_title":"OBIA - Object-Based Image Analysis (GEOBIA) - GIS Geography","og_description":"Object-based Image Analysis (OBIA) segments an image by grouping pixels together into vector objects. Using these objects, you classify as land cover types.","og_url":"https:\/\/gisgeography.com\/obia-object-based-image-analysis-geobia\/","og_site_name":"GIS Geography","article_publisher":"https:\/\/www.facebook.com\/gisgeography","article_author":"https:\/\/www.facebook.com\/gisgeography","article_published_time":"2014-07-09T12:05:06+00:00","article_modified_time":"2025-04-02T00:37:26+00:00","og_image":[{"width":1057,"height":578,"url":"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Object-Based-Image-Analysis-Geobia.jpg","type":"image\/jpeg"}],"author":"GISGeography","twitter_card":"summary_large_image","twitter_creator":"@https:\/\/twitter.com\/GisGeography","twitter_site":"@GisGeography","twitter_misc":{"Written by":"GISGeography","Est. reading time":"7 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/gisgeography.com\/obia-object-based-image-analysis-geobia\/#article","isPartOf":{"@id":"https:\/\/gisgeography.com\/obia-object-based-image-analysis-geobia\/"},"author":{"name":"GISGeography","@id":"https:\/\/gisgeography.com\/#\/schema\/person\/9e7385da3acea92bc45d45be5dfe191e"},"headline":"OBIA &#8211; Object-Based Image Analysis (GEOBIA)","datePublished":"2014-07-09T12:05:06+00:00","dateModified":"2025-04-02T00:37:26+00:00","mainEntityOfPage":{"@id":"https:\/\/gisgeography.com\/obia-object-based-image-analysis-geobia\/"},"wordCount":1072,"commentCount":11,"publisher":{"@id":"https:\/\/gisgeography.com\/#organization"},"image":{"@id":"https:\/\/gisgeography.com\/obia-object-based-image-analysis-geobia\/#primaryimage"},"thumbnailUrl":"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Object-Based-Image-Analysis-Geobia.jpg","keywords":["Image Classification"],"articleSection":["Remote Sensing"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/gisgeography.com\/obia-object-based-image-analysis-geobia\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/gisgeography.com\/obia-object-based-image-analysis-geobia\/","url":"https:\/\/gisgeography.com\/obia-object-based-image-analysis-geobia\/","name":"OBIA - Object-Based Image Analysis (GEOBIA) - GIS Geography","isPartOf":{"@id":"https:\/\/gisgeography.com\/#website"},"primaryImageOfPage":{"@id":"https:\/\/gisgeography.com\/obia-object-based-image-analysis-geobia\/#primaryimage"},"image":{"@id":"https:\/\/gisgeography.com\/obia-object-based-image-analysis-geobia\/#primaryimage"},"thumbnailUrl":"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Object-Based-Image-Analysis-Geobia.jpg","datePublished":"2014-07-09T12:05:06+00:00","dateModified":"2025-04-02T00:37:26+00:00","description":"Object-based Image Analysis (OBIA) segments an image by grouping pixels together into vector objects. Using these objects, you classify as land cover types.","breadcrumb":{"@id":"https:\/\/gisgeography.com\/obia-object-based-image-analysis-geobia\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/gisgeography.com\/obia-object-based-image-analysis-geobia\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/gisgeography.com\/obia-object-based-image-analysis-geobia\/#primaryimage","url":"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Object-Based-Image-Analysis-Geobia.jpg","contentUrl":"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Object-Based-Image-Analysis-Geobia.jpg","width":1057,"height":578,"caption":"OBIA Object-Based Image Analysis Geobia"},{"@type":"BreadcrumbList","@id":"https:\/\/gisgeography.com\/obia-object-based-image-analysis-geobia\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/gisgeography.com\/"},{"@type":"ListItem","position":2,"name":"Remote Sensing","item":"https:\/\/gisgeography.com\/category\/remote-sensing\/"},{"@type":"ListItem","position":3,"name":"OBIA &#8211; Object-Based Image Analysis (GEOBIA)"}]},{"@type":"WebSite","@id":"https:\/\/gisgeography.com\/#website","url":"https:\/\/gisgeography.com\/","name":"GIS Geography","description":"Geographic Information Systems","publisher":{"@id":"https:\/\/gisgeography.com\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/gisgeography.com\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/gisgeography.com\/#organization","name":"GIS Geography","url":"https:\/\/gisgeography.com\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/gisgeography.com\/#\/schema\/logo\/image\/","url":"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/cropped-GIS-Geography-Logo0.png","contentUrl":"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/cropped-GIS-Geography-Logo0.png","width":500,"height":100,"caption":"GIS Geography"},"image":{"@id":"https:\/\/gisgeography.com\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/gisgeography","https:\/\/x.com\/GisGeography"]},{"@type":"Person","@id":"https:\/\/gisgeography.com\/#\/schema\/person\/9e7385da3acea92bc45d45be5dfe191e","name":"GISGeography","description":"We help you learn Geographic Information Systems and remote sensing","sameAs":["https:\/\/www.facebook.com\/gisgeography","https:\/\/x.com\/https:\/\/twitter.com\/GisGeography"],"url":"https:\/\/gisgeography.com\/author\/gisgeo\/"}]}},"taxonomy_info":{"category":[{"value":92,"label":"Remote Sensing"}],"post_tag":[{"value":67,"label":"Image Classification"}]},"featured_image_src_large":["https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/OBIA-Object-Based-Image-Analysis-Geobia-678x371.jpg",678,371,true],"author_info":{"display_name":"GISGeography","author_link":"https:\/\/gisgeography.com\/author\/gisgeo\/"},"comment_info":38,"category_info":[{"term_id":92,"name":"Remote Sensing","slug":"remote-sensing","term_group":0,"term_taxonomy_id":93,"taxonomy":"category","description":"","parent":0,"count":62,"filter":"raw","cat_ID":92,"category_count":62,"category_description":"","cat_name":"Remote Sensing","category_nicename":"remote-sensing","category_parent":0}],"tag_info":[{"term_id":67,"name":"Image Classification","slug":"image-classification","term_group":0,"term_taxonomy_id":68,"taxonomy":"post_tag","description":"","parent":0,"count":6,"filter":"raw"}],"_links":{"self":[{"href":"https:\/\/gisgeography.com\/wp-json\/wp\/v2\/posts\/14495","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/gisgeography.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/gisgeography.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/gisgeography.com\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/gisgeography.com\/wp-json\/wp\/v2\/comments?post=14495"}],"version-history":[{"count":24,"href":"https:\/\/gisgeography.com\/wp-json\/wp\/v2\/posts\/14495\/revisions"}],"predecessor-version":[{"id":96849,"href":"https:\/\/gisgeography.com\/wp-json\/wp\/v2\/posts\/14495\/revisions\/96849"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/gisgeography.com\/wp-json\/wp\/v2\/media\/96818"}],"wp:attachment":[{"href":"https:\/\/gisgeography.com\/wp-json\/wp\/v2\/media?parent=14495"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/gisgeography.com\/wp-json\/wp\/v2\/categories?post=14495"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/gisgeography.com\/wp-json\/wp\/v2\/tags?post=14495"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}