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  • Build complex spatial models using LAS operators

    LiDAR data of Yosemite, RGB encoded with NAIP data.
  • Monitor ship movements using radar images

    Dynamic Radar change detection tools highlight temperal image differences
  • Find quarry changes using the Spatial Modeler

    Two dates of SGM results from ADS40 imagery were modeled
  • Feature extraction using image segmentation

    FLS image segmentation requires only a few intuitive parameters
  • Work with your geophysical data in ERDAS IMAGINE

    ERDAS IMAGINE integrates more tools for mining and oil & gas

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ERDAS Field Guide

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Image Compression in ERDAS IMAGINE

IMAGINE Data Format by Product Tier

Python Scripting with ERDAS IMAGINE Spatial Modeler
ERDAS IMAGINE Professional includes a spatial modeling environment which provides the user the opportunity to extend the core functionality by using Python scripting. This document provides some instruction and sample code for working with Python in the IMAGINE Spatial Modeler. The data and sample code used in the examples is contained in the accompanying zip file.

WorldView2 Basics and ERDAS IMAGINE

White Papers

Better Urban Change Detection Using ERDAS IMAGINE and Spatial Modeler

Identifying change in high-resolution urban imagery has always presented unique problems. Even when it is successfully performed, it is a laborious one-off process; the next time it is run, you have to start all over again from square one.

This paper describes how to use the image pre-processing of ERDAS IMAGINE AAIC and ERDAS IMAGINE Spatial Modeler to create a reusable workflow that can be accurately repeated with new datasets.

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National Mapping Agencies

In this short whitepaper you will see how places like Africa are using Hexagon Geospatial products.

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Creating Customized Spatial Models with Point Clouds WhitePaper

This white paper shows describes how to use the point cloud tools in the ribbon interface of ERDAS IMAGINE 2014, as well as operators within the spatial modeling environment.  Users can now edit point clouds through RGB encoding, classifying and rasterizing – allowing for a more streamlined and efficient workflow.  By offering the option to rasterize the point clouds, the new Spatial Modeler also provides more accurate bare earth extraction.

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Hexagon Geospatial Solutions for Distributed Common Ground Station (DCGS)

Since Desert Storm, Hexagon Geospatial has provided the tactical community with advanced imagery intelligence and GEOINT processing solutions that have made American, Commonwealth, and Coalition warfighters more effective in battle and in peace. Now, with a complete, geospatially-enabled, service oriented architecture, Hexagon Geospatial brings the tactical community, through the Distribute Common Ground Station (DCGS) program a host of next generation solutions that promise to increase situational awareness, ensure accuracy, and reduce mission latency.
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Discriminant Function Change in ERDAS IMAGINE

For ERDAS IMAGINE 2011, ERDAS Inc. has developed a new algorithm for change detection between two co-registered images acquired at different dates. This algorithm, named Discriminant Function Change (DFC), characterizes the natural distribution of spectral clusters in the data space of one image, then uses a discriminant function to measure probability of change of the pixels in the other image.
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Radar Analyst Workstation: Modern, User-friendly Radar Technology in ERDAS IMAGINE

Over the past two decades, radar software research and development has resulted in a number of algorithms that produce higher-quality versions of the information products that are required by the user community. However, the natural variation in radar images and background-to-target ratios, as well as false positives, preclude the specification of processing parameters and thresholds that are universally applicable. As a result, handling radar images has traditionally been a very hands-on process, much more so than the processing and analysis of optical imagery.

ERDAS IMAGINE’s radar technology has been specifically designed to minimize the amount of work required from a human operator. With easy-to-use wizards and intelligent default values, ERDAS IMAGINE’s radar tools provide the maximum amount of automated pre-processing and information extraction and present you with the results for final refinement.

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Volumetric Measure Using Geospatial Technology

Using high-resolution terrain information generated from stereo imagery, you can obtain volumetric measurements for monitoring and surface analysis. In this whitepaper, we detail workflows that could be used for such a task. We will prepare the project and stereo imagery, create a digital surface model (DSM), and take volumetric measurements. For our demonstration, the goal is to collect volumetric measurements that show how much earth has been removed from a quarry or mine. However, this process could also be used for a variety of applications.
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Object-Based Change Detection for a Cultural-Historical Survey of the Landscape — From Cow Trails to Walking Paths

Historical maps, aerial images and other historic documents are finding their way into the digital world. This offers new possibilities but also requires adapted technologies and interdisciplinary approaches. The ProMeRe project connects historic, architectural and planning methods with remote sensing and geoinformation technologies. It aims to analyse the development of tourism and its effects in an alpine environment over the last 150 years. We present an object-based change detection approach with IMAGINE Objective for the monitoring of country roads and walking paths networks in the project area.
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Signature of Pest-Organisms in Mato Grosso Agroecosystems Using WorldView-2 Imagery

Submitted as part of our 2012 Geospatial Challenge, this paper presents an application in which WorldView-2 images were used to detect the damage caused by nematodes in soybean (Glycine max L.), maize (Zea mays L.), sunnhemp (Crotalaria ochroleuca G. Don.) and dark sword-grass (Agrotis ipsilon) in maize (Zea mays L.).
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A Maximum Entropy and Least Cost Path Model of Bearded Capuchin Monkey Movement in Northeastern Brazil Incorporating ERDAS Subpixel Classification Analysis of WorldView-2 Imagery

This is a winner paper from our 2012 ERDAS IMAGINE-DigitalGlobe Geospatial Challenge. Howard’s work has implications for predicting daily movement patterns of all mobile animals, as high-resolution imagery and subpixel analysis in conjunction with habitat modeling represent significant improvements to previous attempts to link species occurrence or animal behavior with environmental variables.
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