{"id":5111,"date":"2016-07-19T21:12:48","date_gmt":"2016-07-20T02:12:48","guid":{"rendered":"http:\/\/gisgeography.com\/?p=5111"},"modified":"2025-03-30T19:03:05","modified_gmt":"2025-03-31T00:03:05","slug":"erdas-imagine","status":"publish","type":"post","link":"https:\/\/gisgeography.com\/erdas-imagine\/","title":{"rendered":"ERDAS Imagine &#8211; Earth Resources Data Analysis System"},"content":{"rendered":"\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"886\" height=\"529\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Imagine-Feature.jpg\" alt=\"ERDAS Imagine Feature\" class=\"wp-image-96538\" style=\"width:700px\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Imagine-Feature.jpg 886w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Imagine-Feature-300x179.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Imagine-Feature-678x405.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Imagine-Feature-768x459.jpg 768w\" sizes=\"auto, (max-width: 886px) 100vw, 886px\" \/><\/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<h3 class=\"wp-block-heading\">ERDAS Imagine Software Review<\/h3>\n\n\n\n<p>The year was 1978 when <strong>ERDAS Imagine<\/strong> (ERDAS 4 at the time) was first launched using a hard drive the size of a small washing machine. Up to the present time, ERDAS Imagine has been re-worked and is still one of the best in the business<\/p>\n\n\n\n<p>While it excels in being user-friendly, the tab interface helps keep all your tools in order. Today, we highlight why ERDAS Imagine is Hexagon Geospatial&#8217;s flagship image processing software.<\/p>\n\n\n<style>.wp-block-kadence-advancedbtn.kt-btns5111_345cf3-e6, .site .entry-content .wp-block-kadence-advancedbtn.kt-btns5111_345cf3-e6, .wp-block-kadence-advancedbtn.kb-btns5111_345cf3-e6, .site .entry-content .wp-block-kadence-advancedbtn.kb-btns5111_345cf3-e6{margin-top:0px;}.wp-block-kadence-advancedbtn.kb-btns5111_345cf3-e6{padding-bottom:var(--global-kb-spacing-lg, 3rem);gap:var(--global-kb-gap-xs, 0.5rem );justify-content:flex-start;align-items:center;}.kt-btns5111_345cf3-e6 .kt-button{font-weight:normal;font-style:normal;}@media all and (max-width: 1024px){.wp-block-kadence-advancedbtn.kb-btns5111_345cf3-e6{justify-content:center;}}@media all and (max-width: 767px){.wp-block-kadence-advancedbtn.kb-btns5111_345cf3-e6{justify-content:flex-start;}}<\/style>\n<div class=\"wp-block-kadence-advancedbtn kb-buttons-wrap kb-btns5111_345cf3-e6\"><style>ul.menu .wp-block-kadence-advancedbtn .kb-btn5111_fbbd7f-5e.kb-button{width:initial;}.wp-block-kadence-advancedbtn .kb-btn5111_fbbd7f-5e.kb-button{color:var(--global-palette9, #ffffff);background:var(--global-palette1, #3182CE);border-top-left-radius:24px;border-top-right-radius:24px;border-bottom-right-radius:24px;border-bottom-left-radius:24px;border-top-color:var(--global-palette1, #3182CE);border-top-style:solid;border-right-color:var(--global-palette1, #3182CE);border-right-style:solid;border-bottom-color:var(--global-palette1, #3182CE);border-bottom-style:solid;border-left-color:var(--global-palette1, #3182CE);border-left-style:solid;}.wp-block-kadence-advancedbtn .kb-btn5111_fbbd7f-5e.kb-button:hover, .wp-block-kadence-advancedbtn .kb-btn5111_fbbd7f-5e.kb-button:focus{color:#ffffff;background:var(--global-palette2, #2B6CB0);border-top-color:var(--global-palette2, #2B6CB0);border-top-style:solid;border-right-color:var(--global-palette2, #2B6CB0);border-right-style:solid;border-bottom-color:var(--global-palette2, #2B6CB0);border-bottom-style:solid;border-left-color:var(--global-palette2, #2B6CB0);border-left-style:solid;}@media all and (max-width: 1024px){.wp-block-kadence-advancedbtn .kb-btn5111_fbbd7f-5e.kb-button{border-top-color:var(--global-palette1, #3182CE);border-top-style:solid;border-right-color:var(--global-palette1, #3182CE);border-right-style:solid;border-bottom-color:var(--global-palette1, #3182CE);border-bottom-style:solid;border-left-color:var(--global-palette1, #3182CE);border-left-style:solid;}}@media all and (max-width: 1024px){.wp-block-kadence-advancedbtn .kb-btn5111_fbbd7f-5e.kb-button:hover, .wp-block-kadence-advancedbtn .kb-btn5111_fbbd7f-5e.kb-button:focus{border-top-color:var(--global-palette2, #2B6CB0);border-top-style:solid;border-right-color:var(--global-palette2, #2B6CB0);border-right-style:solid;border-bottom-color:var(--global-palette2, #2B6CB0);border-bottom-style:solid;border-left-color:var(--global-palette2, #2B6CB0);border-left-style:solid;}}@media all and (max-width: 767px){.wp-block-kadence-advancedbtn .kb-btn5111_fbbd7f-5e.kb-button{border-top-color:var(--global-palette1, #3182CE);border-top-style:solid;border-right-color:var(--global-palette1, #3182CE);border-right-style:solid;border-bottom-color:var(--global-palette1, #3182CE);border-bottom-style:solid;border-left-color:var(--global-palette1, #3182CE);border-left-style:solid;}.wp-block-kadence-advancedbtn .kb-btn5111_fbbd7f-5e.kb-button:hover, .wp-block-kadence-advancedbtn .kb-btn5111_fbbd7f-5e.kb-button:focus{border-top-color:var(--global-palette2, #2B6CB0);border-top-style:solid;border-right-color:var(--global-palette2, #2B6CB0);border-right-style:solid;border-bottom-color:var(--global-palette2, #2B6CB0);border-bottom-style:solid;border-left-color:var(--global-palette2, #2B6CB0);border-left-style:solid;}}<\/style><a class=\"kb-button kt-button button kb-btn5111_fbbd7f-5e kt-btn-size-standard kt-btn-width-type-auto kb-btn-global-outline  kt-btn-has-text-true kt-btn-has-svg-true  wp-block-kadence-singlebtn\" aria-label=\"Download\" href=\"https:\/\/hexagon.com\/products\/erdas-imagine\" download=\"\" target=\"_blank\" rel=\"noreferrer noopener\"><span class=\"kt-btn-inner-text\"><strong>Go To ERDAS Imagine<\/strong><\/span><span class=\"kb-svg-icon-wrap kb-svg-icon-fe_chevronRight kt-btn-icon-side-right\"><svg viewBox=\"0 0 24 24\"  fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"  aria-hidden=\"true\"><polyline points=\"9 18 15 12 9 6\"\/><\/svg><\/span><\/a><\/div>\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\">1. The Intelligent Viewer<\/h3>\n\n\n\n<p>The viewer is sharp, it&#8217;s expandable&#8230; Not to mention, it has an uncanny ability to read any format you throw at it. For example, it&#8217;s compatible with a range of satellites like <a href=\"http:\/\/gisgeography.com\/landsat-program-satellite-imagery-bands\/\" target=\"_blank\" rel=\"noopener noreferrer\">Landsat<\/a>, Worldview, <a href=\"https:\/\/gisgeography.com\/how-to-download-sentinel-satellite-data\/\" target=\"_blank\" rel=\"noopener noreferrer\">Sentinel<\/a>, <a href=\"http:\/\/gisgeography.com\/spot-satellite-pour-observation-terre\/\" target=\"_blank\" rel=\"noopener noreferrer\">SPOT<\/a>, and AVHRR.<\/p>\n\n\n\n<p>This means that it&#8217;s intelligent enough to read your image and deliver canned composites. Equally important, you can organize the spectral bands as you choose for composites of your own.<\/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=\"1000\" height=\"520\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2023\/02\/ERDAS-Imagine-Tokyo.jpg\" alt=\"ERDAS Imagine Tokyo\" class=\"has-border-color has-theme-palette-3-border-color wp-image-96535\" style=\"border-width:1px;width:700px\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2023\/02\/ERDAS-Imagine-Tokyo.jpg 1000w, https:\/\/gisgeography.com\/wp-content\/uploads\/2023\/02\/ERDAS-Imagine-Tokyo-300x156.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2023\/02\/ERDAS-Imagine-Tokyo-678x353.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2023\/02\/ERDAS-Imagine-Tokyo-768x399.jpg 768w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/figure>\n<\/div>\n\n\n<p>ERDAS Imagine will pan-sharpen your image for crisper outputs given a panchromatic band.<\/p>\n\n\n\n<p><strong>READ MORE:<\/strong> <a href=\"https:\/\/gisgeography.com\/free-satellite-imagery-data-list\/\" target=\"_blank\" rel=\"noopener noreferrer\">15 Free Satellite Imagery Data Sources<\/a><\/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\">2. LiDAR Tools<\/h3>\n\n\n\n<p><a href=\"https:\/\/gisgeography.com\/lidar-light-detection-and-ranging\/\" target=\"_blank\" rel=\"noopener noreferrer\">Light Detection and Ranging (LiDAR)<\/a> is the new authoritative data set.<\/p>\n\n\n\n<p>LiDAR actively sends light energy to the ground. It measures reflected light in time back to the sensor to get the distance. For this reason, LiDAR gives an accurate snapshot of the ground elevation and its features.<\/p>\n\n\n\n<p>ERDAS Imagine handles LiDAR in its native form (LAS). As a matter of fact, you can drag and drop a LAS file in the viewer to see and edit the point cloud.<\/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=\"1000\" height=\"534\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-LiDAR-Tools.jpg\" alt=\"ERDAS LiDAR Tools\" class=\"has-border-color has-theme-palette-3-border-color wp-image-96543\" style=\"border-width:1px;width:700px\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-LiDAR-Tools.jpg 1000w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-LiDAR-Tools-300x160.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-LiDAR-Tools-678x362.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-LiDAR-Tools-768x410.jpg 768w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/figure>\n<\/div>\n\n\n<p>It can render point clouds in 3D. Also, you can view the intensity, classification, elevation, and various returns.<\/p>\n\n\n\n<p>The LiDAR profile side view is slick. You also get a front view which is also neat.<\/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=\"1000\" height=\"534\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-LiDAR-Sideview.jpg\" alt=\"ERDAS LiDAR Sideview\" class=\"has-border-color has-theme-palette-3-border-color wp-image-96545\" style=\"border-width:1px;width:700px\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-LiDAR-Sideview.jpg 1000w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-LiDAR-Sideview-300x160.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-LiDAR-Sideview-678x362.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-LiDAR-Sideview-768x410.jpg 768w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/figure>\n<\/div>\n\n\n<p>As LiDAR expands in usage, ERDAS Imagine adjusts to its growing demand<\/p>\n\n\n\n<p><strong>READ MORE:<\/strong> <a href=\"https:\/\/gisgeography.com\/top-6-free-lidar-data-sources\/\" target=\"_blank\" rel=\"noopener noreferrer\">Top 6 Free LiDAR Data Sources<\/a><\/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\">3. Terrain Tools<\/h3>\n\n\n\n<p>Whether you have a photogrammetric, LiDAR-based, or InSAR <a href=\"http:\/\/gisgeography.com\/dem-dsm-dtm-differences\/\" target=\"_blank\" rel=\"noopener noreferrer\">digital elevation model<\/a>, ERDAS Imagine can perform most types of terrain analysis.<\/p>\n\n\n\n<p>The painted relief raster splits elevation into color ranges. It paints each range based on its elevation as in the example below. We&#8217;ve also included a side-view profile in this example.<\/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=\"1000\" height=\"520\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-DEM-Profile.jpg\" alt=\"ERDAS DEM Profile\" class=\"has-border-color has-theme-palette-3-border-color wp-image-96546\" style=\"border-width:1px;width:700px\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-DEM-Profile.jpg 1000w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-DEM-Profile-300x156.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-DEM-Profile-678x353.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-DEM-Profile-768x399.jpg 768w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/figure>\n<\/div>\n\n\n<p>ERDAS Imagine can generate your basic slope, <a href=\"http:\/\/gisgeography.com\/slope-aspect-microclimate-south-facing\/\">aspect<\/a>, <a href=\"https:\/\/gisgeography.com\/contour-lines-topographic-map\/\">contours<\/a>, and <a href=\"https:\/\/gisgeography.com\/line-of-sight-viewshed-visibility-analysis\/\">viewshed types of maps<\/a>. Additionally, it has geoscientific tools to remove void areas, extract valleys and ridges, and drape images in a virtual GIS viewer.<\/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=\"1000\" height=\"534\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Aspect.jpg\" alt=\"ERDAS Aspect\" class=\"has-border-color has-theme-palette-3-border-color wp-image-96547\" style=\"border-width:1px;width:700px\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Aspect.jpg 1000w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Aspect-300x160.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Aspect-678x362.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Aspect-768x410.jpg 768w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/figure>\n<\/div>\n\n\n<p>You will need an additional license for a stereo analyst. With the stereo analyst extension, you will be able to collect and measure 3D features using photogrammetric principles from stereo pairs of imagery.<\/p>\n\n\n<style>.kb-image5111_1896ee-bd .kb-image-has-overlay:after{opacity:0.3;}.kb-image5111_1896ee-bd img.kb-img, .kb-image5111_1896ee-bd .kb-img img{border-top:1px solid #c3c3c3;border-right:1px solid #c3c3c3;border-bottom:1px solid #c3c3c3;border-left:1px solid #c3c3c3;}@media all and (max-width: 1024px){.kb-image5111_1896ee-bd img.kb-img, .kb-image5111_1896ee-bd .kb-img img{border-top:1px solid #c3c3c3;border-right:1px solid #c3c3c3;border-bottom:1px solid #c3c3c3;border-left:1px solid #c3c3c3;}}@media all and (max-width: 767px){.kb-image5111_1896ee-bd img.kb-img, .kb-image5111_1896ee-bd .kb-img img{border-top:1px solid #c3c3c3;border-right:1px solid #c3c3c3;border-bottom:1px solid #c3c3c3;border-left:1px solid #c3c3c3;}}<\/style>\n<div class=\"wp-block-kadence-image kb-image5111_1896ee-bd\"><figure class=\"aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"425\" height=\"321\" src=\"http:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/Surface-Profile-425x321.png\" alt=\"Surface-Profile\" class=\"kb-img wp-image-11596\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/Surface-Profile-425x321.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/Surface-Profile-300x227.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/Surface-Profile-50x38.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/Surface-Profile-200x151.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/Surface-Profile-550x416.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/Surface-Profile-115x87.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/Surface-Profile-205x155.png 205w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/Surface-Profile.png 554w\" sizes=\"auto, (max-width: 425px) 100vw, 425px\" \/><\/figure><\/div>\n\n\n\n<p>ERDAS Imagine has a tool that slices your elevation data into a user-defined number of bins, each containing the same amount. It also has a tool to visualize the z-values (or reflectance spectrum) of a rectangular area such as the surface profile below:<\/p>\n\n\n\n<p><strong>READ MORE:<\/strong> <a href=\"https:\/\/gisgeography.com\/free-global-dem-data-sources\/\" target=\"_blank\" rel=\"noopener noreferrer\">Free Global DEM Data Sources<\/a><\/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\">4. Spectral Tools<\/h3>\n\n\n\n<p>Every feature on Earth has its own unique composition. It reflects and absorbs red, green, blue, near-infrared, and different types of energy. This means that each feature has its own <a href=\"https:\/\/gisgeography.com\/spectral-signature\/\">spectral signature<\/a>.<\/p>\n\n\n\n<p>With this idea in mind, researchers are building spectral libraries cataloging the unique characteristics and compositions of minerals, vegetation, and more. ERDAS Imagine lets you dig deeper into this subject with a spectral profile library of its own &#8211; similar to the <a href=\"https:\/\/landsat.usgs.gov\/spectral-characteristics-viewer\" target=\"_blank\" rel=\"noopener noreferrer\">USGS Spectral Characteristics Viewer<\/a>.<\/p>\n\n\n<style>.kb-image5111_675a82-fd .kb-image-has-overlay:after{opacity:0.3;}.kb-image5111_675a82-fd img.kb-img, .kb-image5111_675a82-fd .kb-img img{border-top:1px solid #bbbbbb;border-right:1px solid #bbbbbb;border-bottom:1px solid #bbbbbb;border-left:1px solid #bbbbbb;}@media all and (max-width: 1024px){.kb-image5111_675a82-fd img.kb-img, .kb-image5111_675a82-fd .kb-img img{border-top:1px solid #bbbbbb;border-right:1px solid #bbbbbb;border-bottom:1px solid #bbbbbb;border-left:1px solid #bbbbbb;}}@media all and (max-width: 767px){.kb-image5111_675a82-fd img.kb-img, .kb-image5111_675a82-fd .kb-img img{border-top:1px solid #bbbbbb;border-right:1px solid #bbbbbb;border-bottom:1px solid #bbbbbb;border-left:1px solid #bbbbbb;}}<\/style>\n<div class=\"wp-block-kadence-image kb-image5111_675a82-fd\"><figure class=\"aligncenter size-medium-large\"><img loading=\"lazy\" decoding=\"async\" width=\"425\" height=\"234\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/Spectral-Profile-Viewer-425x234.png\" alt=\"Spectral Profile Viewer\" class=\"kb-img wp-image-11543\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/Spectral-Profile-Viewer-425x234.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/Spectral-Profile-Viewer-300x165.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/Spectral-Profile-Viewer-50x28.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/Spectral-Profile-Viewer-70x40.png 70w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/Spectral-Profile-Viewer-200x110.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/Spectral-Profile-Viewer-550x303.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/Spectral-Profile-Viewer-115x63.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/Spectral-Profile-Viewer-281x155.png 281w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/Spectral-Profile-Viewer.png 622w\" sizes=\"auto, (max-width: 425px) 100vw, 425px\" \/><\/figure><\/div>\n\n\n\n<p>With more spectral bands (such as with <a href=\"https:\/\/gisgeography.com\/multispectral-vs-hyperspectral-imagery-explained\/\" target=\"_blank\" rel=\"noopener noreferrer\">hyperspectral data<\/a>), you have a greater opportunity to classify more features with greater accuracy. Although <a href=\"https:\/\/gisgeography.com\/hyperspectral-imaging\/\">hyperspectral data<\/a> is hard to come by, ERDAS Imagine provides wizards for hyperspectral analysis.<\/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=\"1000\" height=\"520\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Hyperspectral.jpg\" alt=\"ERDAS Hyperspectral\" class=\"has-border-color has-theme-palette-3-border-color wp-image-96548\" style=\"border-width:1px;width:700px\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Hyperspectral.jpg 1000w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Hyperspectral-300x156.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Hyperspectral-678x353.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Hyperspectral-768x399.jpg 768w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/figure>\n<\/div>\n\n\n<p>There are tools for <a href=\"https:\/\/gisgeography.com\/image-classification-techniques-remote-sensing\/\" target=\"_blank\" rel=\"noopener noreferrer\">supervised and unsupervised classification<\/a>. Despite its impressive image classification tools, you won&#8217;t be able to perform object-based image analysis like you can using <a href=\"https:\/\/geospatial.trimble.com\/en\/products\/software\/trimble-ecognition\" target=\"_blank\" rel=\"noreferrer noopener\">Trimble ECogntion Definiens Developer<\/a>.<\/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=\"1000\" height=\"534\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Supervised-Classification.jpg\" alt=\"ERDAS Supervised Classification\" class=\"has-border-color has-theme-palette-3-border-color wp-image-96549\" style=\"border-width:1px;width:700px\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Supervised-Classification.jpg 1000w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Supervised-Classification-300x160.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Supervised-Classification-678x362.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Supervised-Classification-768x410.jpg 768w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/figure>\n<\/div>\n\n\n<p>There are no shortages of indices in remote sensing &#8211; NDVI, SAVI, GNDVI, RVI, MSAVI, DVI&#8230; Even for different materials like vegetation, sulfates, water, snow, and ice.<\/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=\"1000\" height=\"520\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-WV2-False-IR-NDVI.jpg\" alt=\"ERDAS WV2 False IR NDVI\" class=\"has-border-color has-theme-palette-3-border-color wp-image-96550\" style=\"border-width:1px;width:700px\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-WV2-False-IR-NDVI.jpg 1000w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-WV2-False-IR-NDVI-300x156.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-WV2-False-IR-NDVI-678x353.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-WV2-False-IR-NDVI-768x399.jpg 768w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/figure>\n<\/div>\n\n\n<p>As long as you have the needed bands, you can extract this data from your multispectral image. For example, here is a quick NDVI image:<\/p>\n\n\n\n<p><strong>READ MORE:<\/strong> <a href=\"https:\/\/gisgeography.com\/ndvi-normalized-difference-vegetation-index\/\">What is NDVI (Normalized Difference Vegetation Index)?<\/a><\/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\">5. Radar Tools<\/h3>\n\n\n\n<p>A whole other discipline in remote sensing is <a href=\"https:\/\/gisgeography.com\/passive-active-sensors-remote-sensing\/\" target=\"_blank\" rel=\"noopener noreferrer\">active sensors<\/a> like synthetic aperture radar. For example, Sentinel-1, Radarsat, and TerraSAR satellites can be imported in their native form.<\/p>\n\n\n\n<p>Quickly view data by simply dragging the product.xml into your workspace<\/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=\"1000\" height=\"534\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Radar-Magnitude.jpg\" alt=\"ERDAS Radar Magnitude\" class=\"has-border-color has-theme-palette-3-border-color wp-image-96551\" style=\"border-width:1px;width:700px\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Radar-Magnitude.jpg 1000w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Radar-Magnitude-300x160.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Radar-Magnitude-678x362.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Radar-Magnitude-768x410.jpg 768w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/figure>\n<\/div>\n\n\n<p>ERDAS Imagine has the Radar Toolbox to satisfy the needs of <a href=\"http:\/\/gisgeography.com\/synthetic-aperture-radar-examples\/\">synthetic aperture radar<\/a> users. Despite its complexity, ERDAS Imagine can perform interferometry for DEM extraction, texture analysis, and polarimetric classification.<\/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=\"1000\" height=\"520\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Radar.jpg\" alt=\"ERDAS Radar\" class=\"has-border-color has-theme-palette-3-border-color wp-image-96552\" style=\"border-width:1px;width:700px\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Radar.jpg 1000w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Radar-300x156.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Radar-678x353.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Radar-768x399.jpg 768w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/figure>\n<\/div>\n\n\n<p>Overall, this software makes it easy for the user to manipulate radar data<\/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\">6. Spatial Model Editor<\/h3>\n\n\n\n<p>The Spatial Model Editor is where you string a set of tools together. When you run them all together, you can automate your work. Similar to <a href=\"http:\/\/gisgeography.com\/arcgis-model-builder-custom-toolbox-python\/\">ArcGIS Model Builder<\/a> or an <a href=\"http:\/\/gisgeography.com\/fme-software\/\">FME workbench<\/a>, Spatial Model Editor lets you streamline and customize a repeatable way of getting the answer<\/p>\n\n\n\n<p>For example, here is a LAS to raster conversion. In case you receive another LAS file, you can recycle your model and produce identical results with a different file.<\/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=\"970\" height=\"517\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Spatial-Model-Editor.jpg\" alt=\"ERDAS Spatial Model Editor\" class=\"has-border-color has-theme-palette-3-border-color wp-image-96553\" style=\"border-width:1px;width:700px\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Spatial-Model-Editor.jpg 970w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Spatial-Model-Editor-300x160.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Spatial-Model-Editor-678x361.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Spatial-Model-Editor-768x409.jpg 768w\" sizes=\"auto, (max-width: 970px) 100vw, 970px\" \/><\/figure>\n<\/div>\n\n\n<p>The Spatial Model Editor is versatile and intuitive. Best of all, you can pick it up and learn effortlessly.<\/p>\n\n\n\n<p>Although part of Hexagon Geospatial GeoMedia software, the Spatial Model Editor can manipulate and manage satellite and aerial imagery as well.<\/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\">ERDAS Imagine History<\/h3>\n\n\n\n<p>Here are some of its key dates of release:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>First, its initial release came in 1978 with <strong>ERDAS 4<\/strong>. In addition, it was run on Cromemco microcomputers with options for large digitizing tablets and 80-megabyte hard drives.<\/li>\n\n\n\n<li>Following its previous release, <strong>ERDAS 400<\/strong> was introduced in 1980. At the time, computers were expensive and were being used by organizations such as NASA, the US Forest Service, and the US Environmental Protection Agency.<\/li>\n\n\n\n<li>In 1982, <strong>ERDAS 7<\/strong> was launched which sparked a collaboration between ESRI ARC\/INFO. Users were able to connect remote sensing with the power of GIS mapping.<\/li>\n\n\n\n<li>Finally, the flagship <strong>ERDAS Imagine<\/strong> software product was introduced which it is still named today. Another key point is how the graphical user interface allowed users to visualize data, create maps and perform image processing.<\/li>\n<\/ul>\n\n\n\n<p>It&#8217;s now part of Hexagon&#8217;s product suites which include <a href=\"https:\/\/gisgeography.com\/geomedia-hexagon-geospatial\/\">Hexagon GeoMedia<\/a>, Leica Geosystems, and M.App Enterprise.<\/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\">Final Thoughts: ERDAS Imagine<\/h3>\n\n\n\n<p>ERDAS Imagine is one of the first remote sensing software programs.<\/p>\n\n\n\n<p>&#8230;And it&#8217;s still one of the best.<\/p>\n\n\n\n<p>This robust raster-based GIS suite provides a wide array of tools for <a href=\"https:\/\/gisgeography.com\/spatial-analysis\/\">geospatial analysis<\/a>: import\/export, map composition, image enhancement, <a href=\"https:\/\/gisgeography.com\/image-classification-techniques-remote-sensing\/\">image classification<\/a>, raster GIS modeling, and <a href=\"https:\/\/gisgeography.com\/what-is-photogrammetry\/\">photogrammetry<\/a>.<\/p>\n\n\n\n<p>What do you think of ERDAS Imagine? Let us know with a comment below.<\/p>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>From passive to active remote sensing &#8211; photogrammetry to LiDAR, ERDAS Imagine loads you with all the necessary tools for more robust image analysis.<\/p>\n","protected":false},"author":2,"featured_media":96538,"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":[173],"tags":[527,491],"class_list":["post-5111","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-software","tag-remote-sensing-software","tag-specialized-software"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>ERDAS Imagine - Earth Resources Data Analysis System - GIS Geography<\/title>\n<meta name=\"description\" content=\"From passive to active remote sensing - photogrammetry to LiDAR, ERDAS Imagine loads you with all the necessary tools for more robust image analysis.\" \/>\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\/erdas-imagine\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"ERDAS Imagine - Earth Resources Data Analysis System - GIS Geography\" \/>\n<meta property=\"og:description\" content=\"From passive to active remote sensing - photogrammetry to LiDAR, ERDAS Imagine loads you with all the necessary tools for more robust image analysis.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/gisgeography.com\/erdas-imagine\/\" \/>\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=\"2016-07-20T02:12:48+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-03-31T00:03:05+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/07\/ERDAS-Imagine-Feature.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"886\" \/>\n\t<meta property=\"og:image:height\" content=\"529\" \/>\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=\"8 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/gisgeography.com\\\/erdas-imagine\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/gisgeography.com\\\/erdas-imagine\\\/\"},\"author\":{\"name\":\"GISGeography\",\"@id\":\"https:\\\/\\\/gisgeography.com\\\/#\\\/schema\\\/person\\\/9e7385da3acea92bc45d45be5dfe191e\"},\"headline\":\"ERDAS Imagine &#8211; 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