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https://hdl.handle.net/11681/27344Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Waldrop, Lauren E. | - |
| dc.contributor.author | Hart, Carl R. | - |
| dc.contributor.author | Parker, Nancy E. | - |
| dc.contributor.author | Pettit, Chris L. | - |
| dc.contributor.author | McIntosh, Scotlund. | - |
| dc.date.accessioned | 2018-06-18T19:47:10Z | - |
| dc.date.available | 2018-06-18T19:47:10Z | - |
| dc.date.issued | 2018-06 | - |
| dc.identifier.govdoc | ERDC/CRREL TR-18-7 | - |
| dc.identifier.uri | http://hdl.handle.net/11681/27344 | - |
| dc.identifier.uri | http://dx.doi.org/10.21079/11681/27344 | - |
| dc.description | Technical Report | - |
| dc.description.abstract | In support of the Terrain Characterization for Rendering and Field Evaluation effort, the U.S. Army Corps of Engineers, Engineer Research and Development Center (ERDC), Cold Regions Research and Engineering Laboratory (CRREL), assisted the Natick Soldier Research, Development, and Engineering Center (NSRDEC) in evaluating machine learning algorithms to automatically classify three vegetation types (tree, shrub, and herbaceous), and a non-vegetated type in terrestrial images. In a previous partnership between CRREL and NSRDEC, researchers developed the Global Natural Background Image Database (GNBID), a collection of natural background images classified by vegetation attributes to include vegetation type and height, leaf shape, leaf color, and many others. Following deployment, the GNBID successfully improved on-the-ground understanding of natural background environments and quickly revealed the need for a larger database. Manual classification methods proved time intensive and variable, thus CRREL explored the feasibility of automatically identifying features using machine learning algorithms. In this scope-of-work study, we explore a multitude of computer vision techniques, settling on a supervised deep-learning technique. Here we present the advantages and disadvantages of various techniques, classification results from a subset of images, and recommendations for future research in this area. | en_US |
| dc.description.sponsorship | Terrain Characterization for Rendering and Field Evaluation Program (U.S.) | en_US |
| dc.description.sponsorship | U.S. Army Natick Research, Development, and Engineering Center. | - |
| dc.format.extent | 69 pages/3.920 Mb | - |
| dc.format.medium | PDF/A | - |
| dc.language.iso | en | en_US |
| dc.publisher | Cold Regions Research and Engineering Laboratory (U.S.) | en_US |
| dc.publisher | Engineer Research and Development Center (U.S.) | en_US |
| dc.relation.ispartofseries | Technical Report (Engineer Research and Development Center (U.S.) ) ; no. ERDC/CRREL TR-18-7 | - |
| dc.rights | Approved for Public Release; Distribution is Unlimited | - |
| dc.source | This Digital Resource was created in Microsoft Word and Adobe Acrobat | - |
| dc.subject | Camouflage (Military science) | en_US |
| dc.subject | Computer algorithms | en_US |
| dc.subject | Computer vision | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Remote sensing | en_US |
| dc.subject | Terrain characterization | en_US |
| dc.subject | Vegetation classification | en_US |
| dc.title | Utility of machine learning algorithms for natural background photo classification | en_US |
| dc.type | Report | en_US |
| Appears in Collections: | Technical Report | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ERDC-CRREL TR-18-7.pdf | 4.01 MB | Adobe PDF |  View/Open |