{"_id":"57dc3c57702ffe0e00157c64","project":"55faeacad0e22017005b8265","user":"55fae9d4825d5f19001fa379","category":{"_id":"573b4f62ef164e2900a2b881","__v":0,"project":"55faeacad0e22017005b8265","version":"55faeacad0e22017005b8268","sync":{"url":"","isSync":false},"reference":false,"createdAt":"2016-05-17T17:05:38.443Z","from_sync":false,"order":8,"slug":"algorithm-guide","title":"Algorithms"},"parentDoc":null,"__v":1,"version":{"_id":"55faeacad0e22017005b8268","project":"55faeacad0e22017005b8265","__v":35,"createdAt":"2015-09-17T16:31:06.800Z","releaseDate":"2015-09-17T16:31:06.800Z","categories":["55faeacbd0e22017005b8269","55faf550764f50210095078e","55faf5b5626c341700fd9e96","55faf8a7825d5f19001fa386","560052f91503430d007cc88f","560054f73aa0520d00da0b1a","56005aaf6932a00d00ba7c62","56005c273aa0520d00da0b3f","5601ae7681a9670d006d164d","5601ae926811d00d00ceb487","5601aeb064866b1900f4768d","5601aee850ee460d0002224c","5601afa02499c119000faf19","5601afd381a9670d006d1652","561d4c78281aec0d00eb27b6","561d588d8ca8b90d00210219","563a5f934cc3621900ac278c","5665c5763889610d0008a29e","566710a36819320d000c2e93","56ddf6df8a5ae10e008e3926","56e1c96b2506700e00de6e83","56e1ccc4e416450e00b9e48c","56e1ccdfe63f910e00e59870","56e1cd10bc46be0e002af26a","56e1cd21e416450e00b9e48e","56e3139a51857d0e008e77be","573b4f62ef164e2900a2b881","57c9d1335fd8ca0e006308ed","57e2bd9d1e7b7220000d7fa5","57f2b992ac30911900c7c2b6","58adb5c275df0f1b001ed59b","58c81b5c6dc7140f003c3c46","595412446ed4d9001b3e7b37","59e76ce41938310028037295","5a009de510890d001c2aabfe"],"is_deprecated":false,"is_hidden":false,"is_beta":false,"is_stable":true,"codename":"v1","version_clean":"1.0.0","version":"1"},"updates":[],"next":{"pages":[],"description":""},"createdAt":"2016-09-16T18:39:19.537Z","link_external":false,"link_url":"","githubsync":"","sync_unique":"","hidden":false,"api":{"settings":"","results":{"codes":[]},"auth":"required","params":[],"url":""},"isReference":false,"order":26,"body":"## Table of Contents\n\nSection | Description\n--- | ---\n[Imagery Examples](#Imagery Examples) | Before and after examples\n[Quickstart](#Quickstart) | Get started with a Python-based quickstart tutorial\n[Task Runtime](#Task Runtime) | Benchmark runtimes for the algorithm\n[Input Options](#Input Options) | Required and optional task inputs\n[Outputs](#Outputs) | Task outputs and example contents\n[Advanced Options](#Advanced Options) | Additional information for advanced users\n[Known Issues](#Known Issues) | Issues users should be aware of\n\n\n## <a name=\"Imagery Examples\"></a>Imagery Examples\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/eb49ea4-ENVI_SpectralIndex_before.jpg\",\n        \"ENVI_SpectralIndex_before.jpg\",\n        800,\n        320,\n        \"#4b4239\"\n      ],\n      \"caption\": \"Before: Input image before creating a spectral index\"\n    }\n  ]\n}\n[/block]\n\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/5bb858a-ENVI_SpectralIndex.jpg\",\n        \"ENVI_SpectralIndex.jpg\",\n        800,\n        320,\n        \"#edf0ad\"\n      ],\n      \"caption\": \"After: Output image with spectral index created\"\n    }\n  ]\n}\n[/block]\n## <a name=\"Quickstart\"></a>Quickstart Tutorial\n\nExample Script: Run in a python environment (i.e. - IPython) using the gbdxtools interface.\n[block:code]\n{\n  \"codes\": [\n    {\n      \"code\": \"from gbdxtools import Interface\\ngbdx = Interface()\\n\\n# Edit the following path to reflect a specific path to an image\\nimage = 's3://gbd-customer-data/CustomerAccount#/PathToImage/'\\n\\nenvi_ndvi = gbdx.Task(\\\"ENVI_SpectralIndex\\\")\\nenvi_ndvi.inputs.input_raster = image\\nenvi_ndvi.inputs.index = \\\"Normalized Difference Vegetation Index\\\"\\n\\nworkflow = gbdx.Workflow([envi_ndvi])\\n\\nworkflow.savedata(\\n   envi_ndvi.outputs.output_raster_uri,\\n   location='NDVI/output_raster_uri' # edit location to suit account\\n)\\n\\nprint workflow.execute()\\nprint workflow.status\\n# Repeat workflow.status as needed to monitor progress.\",\n      \"language\": \"python\"\n    }\n  ]\n}\n[/block]\n## <a name=\"Task Runtime\"></a>Task Runtime\nThese are the average runtimes for this algorithm. All benchmark tests were run using a standard set of images, based on our most common customer scenarios. Runtime benchmarks apply to the specific algorithm, and don’t represent the runtime of a complete workflow.\n\n Sensor Name  | Total Pixels |  Total Area (k2)  |  Time(secs)  |  Time/Area k2\n--------|:----------:|-----------|----------------|---------------\nQB | 41,551,668 | 312.07 |194.58|0.62    \nWV01| 1,028,100,320 |NA |NA | NA\nWV02|35,872,942|1,265.14|216.12|0.66\nWV03|35,371,971|196.27|1,265.14|6.45\nGE| 57,498,000|332.97|185.33|0.56\n\n\n\n\n## <a name=\"Input Options\"></a>Input Options\nThe following table lists all inputs for this task. For details regarding the use of all ENVI input types refer to the [ENVI Task Runner Inputs]([See ENVIRASTER input type](https://github.com/TDG-Platform/docs/blob/master/ENVI_Task_Runner_Inputs.md)) documentation.\n\n| Name                       | Required | Default |               Valid Values               | Description                              |\n| -------------------------- | :------: | :-----: | :--------------------------------------: | ---------------------------------------- |\n| file_types                 |  False   |  None   |                  string                  | GBDX Option. Comma separated list of permitted file type extensions. Use this to filter input files -- Value Type: STRING |\n| input_raster               |   True   |  None   |  A valid S3 URL containing image files.  | Specify a raster from which to run the task. -- Value Type: ENVIRASTER |\n| input_raster_format        |  False   |  None   | [See ENVIRASTER input type](https://github.com/TDG-Platform/docs/blob/master/ENVI_Task_Runner_Inputs.md) | Provide the format of the image, for example: landsat-8. -- Value Type: STRING |\n| input_raster_band_grouping |  False   |  None   | [See ENVIRASTER input type](https://github.com/TDG-Platform/docs/blob/master/ENVI_Task_Runner_Inputs.md) | Provide the name of the band grouping to be used in the task, ie - panchromatic. -- Value Type: STRING |\n| input_raster_filename      |  False   |  None   | [See ENVIRASTER input type](https://github.com/TDG-Platform/docs/blob/master/ENVI_Task_Runner_Inputs.md) | Provide the explicit relative raster filename that ENVI will open. This overrides any file lookup in the task runner. -- Value Type: STRING |\n| index                      |   True   |  None   |                  string                  | Specify a string, or array of strings, representing the pre-defined spectral indices to apply to the input raster. -- Value Type: STRING |\n| output_raster_uri_filename |  False   |  None   |                  string                  | Specify a string with the fully-qualified path and filename for OUTPUT_RASTER. -- Value Type: STRING |\n\n## <a name=\"Outputs\"></a>Outputs\nThe following table lists all the outputs from this task.\n\n| Name              | Required | Description                              |\n| ----------------- | :------: | ---------------------------------------- |\n| output_raster_uri |   True   | Output for OUTPUT_RASTER.                |\n| task_meta_data    |  False   | GBDX Option. Output location for task meta data such as execution log and output JSON. |\n\n##### Output Structure\n\nThe output_raster image file will be written to the specified S3 Customer Account Location in GeoTiff (\\*.tif) format, with an ENVI header file (\\*.hdr).\n\n## <a name=\"Advanced Options\"></a>Advanced Options\nTo link the workflow of 1 input image into AOP_Strip_Processor and the Spectral Index task, use the following gbdxtools script in python.\n[block:code]\n{\n  \"codes\": [\n    {\n      \"code\": \"from gbdxtools import Interface\\ngbdx = Interface()\\n\\n# Edit the following path to reflect a specific path to an image\\ndata = 's3://gbd-customer-data/CustomerAccount#/PathToImage/'\\n\\naoptask = gbdx.Task(\\\"AOP_Strip_Processor\\\") \\naoptask.inputs.data = data\\naoptask.inputs.enable_dra = False\\naoptask.inputs.bands = 'MS'\\n\\nenvi_ndvi = gbdx.Task(\\\"ENVI_SpectralIndex\\\")\\nenvi_ndvi.inputs.input_raster = aoptask.outputs.data.value\\nenvi_ndvi.inputs.index = \\\"Normalized Difference Vegetation Index\\\"\\n\\nworkflow = gbdx.Workflow([aoptask, envi_ndvi])\\n\\nworkflow.savedata(\\n  envi_ndvi.outputs.output_raster_uri,\\n  location='AOP_NDVI/output_raster_uri' # edit location to suit account\\n)\\n\\nprint workflow.execute()\\nprint workflow.status\\n# Repeat workflow.status as needed to monitor progress.\",\n      \"language\": \"python\"\n    }\n  ]\n}\n[/block]\n\n##<a name=\"Known Issues\"></a>Known Issues\n1) To run the task in a single workflow with Advanced Image Preprocessor, the tif file must first be removed from the AOP folder with the additional python commands listed in Advanced\n\n## Background\nFor additional background information on this task please refer to the <a href=\"http://www.harrisgeospatial.com/docs/home.html\">Harris Geospatial ENVI documentation.</a>​\n\n#### Contact Us   \nIf you have any questions or issues with this task, please contact [**gbdx-support:::at:::digitalglobe.com** ](mailto:gbdx-support@digitalglobe.com).","excerpt":"This task creates a spectral index raster from one pre-defined spectral index. Spectral indices are combinations of surface reflectance at two or more wavelengths that indicate relative abundance of features of interest. The Normalized Difference Vegetation Index (NDVI) is an example.\n\n**GBDX Registered Name**: ENVI_SpectralIndex\t\t\n**Provider**: Harris Geospatial Solutions\nFor more information on how to execute this task please refer to the [ENVI® Task Runner Inputs](doc:envi-task-runner-inputs) .","slug":"envi-spectral-index","type":"basic","title":"ENVI® Spectral Index"}

ENVI® Spectral Index

This task creates a spectral index raster from one pre-defined spectral index. Spectral indices are combinations of surface reflectance at two or more wavelengths that indicate relative abundance of features of interest. The Normalized Difference Vegetation Index (NDVI) is an example. **GBDX Registered Name**: ENVI_SpectralIndex **Provider**: Harris Geospatial Solutions For more information on how to execute this task please refer to the [ENVI® Task Runner Inputs](doc:envi-task-runner-inputs) .

## Table of Contents Section | Description --- | --- [Imagery Examples](#Imagery Examples) | Before and after examples [Quickstart](#Quickstart) | Get started with a Python-based quickstart tutorial [Task Runtime](#Task Runtime) | Benchmark runtimes for the algorithm [Input Options](#Input Options) | Required and optional task inputs [Outputs](#Outputs) | Task outputs and example contents [Advanced Options](#Advanced Options) | Additional information for advanced users [Known Issues](#Known Issues) | Issues users should be aware of ## <a name="Imagery Examples"></a>Imagery Examples [block:image] { "images": [ { "image": [ "https://files.readme.io/eb49ea4-ENVI_SpectralIndex_before.jpg", "ENVI_SpectralIndex_before.jpg", 800, 320, "#4b4239" ], "caption": "Before: Input image before creating a spectral index" } ] } [/block] [block:image] { "images": [ { "image": [ "https://files.readme.io/5bb858a-ENVI_SpectralIndex.jpg", "ENVI_SpectralIndex.jpg", 800, 320, "#edf0ad" ], "caption": "After: Output image with spectral index created" } ] } [/block] ## <a name="Quickstart"></a>Quickstart Tutorial Example Script: Run in a python environment (i.e. - IPython) using the gbdxtools interface. [block:code] { "codes": [ { "code": "from gbdxtools import Interface\ngbdx = Interface()\n\n# Edit the following path to reflect a specific path to an image\nimage = 's3://gbd-customer-data/CustomerAccount#/PathToImage/'\n\nenvi_ndvi = gbdx.Task(\"ENVI_SpectralIndex\")\nenvi_ndvi.inputs.input_raster = image\nenvi_ndvi.inputs.index = \"Normalized Difference Vegetation Index\"\n\nworkflow = gbdx.Workflow([envi_ndvi])\n\nworkflow.savedata(\n envi_ndvi.outputs.output_raster_uri,\n location='NDVI/output_raster_uri' # edit location to suit account\n)\n\nprint workflow.execute()\nprint workflow.status\n# Repeat workflow.status as needed to monitor progress.", "language": "python" } ] } [/block] ## <a name="Task Runtime"></a>Task Runtime These are the average runtimes for this algorithm. All benchmark tests were run using a standard set of images, based on our most common customer scenarios. Runtime benchmarks apply to the specific algorithm, and don’t represent the runtime of a complete workflow. Sensor Name | Total Pixels | Total Area (k2) | Time(secs) | Time/Area k2 --------|:----------:|-----------|----------------|--------------- QB | 41,551,668 | 312.07 |194.58|0.62 WV01| 1,028,100,320 |NA |NA | NA WV02|35,872,942|1,265.14|216.12|0.66 WV03|35,371,971|196.27|1,265.14|6.45 GE| 57,498,000|332.97|185.33|0.56 ## <a name="Input Options"></a>Input Options The following table lists all inputs for this task. For details regarding the use of all ENVI input types refer to the [ENVI Task Runner Inputs]([See ENVIRASTER input type](https://github.com/TDG-Platform/docs/blob/master/ENVI_Task_Runner_Inputs.md)) documentation. | Name | Required | Default | Valid Values | Description | | -------------------------- | :------: | :-----: | :--------------------------------------: | ---------------------------------------- | | file_types | False | None | string | GBDX Option. Comma separated list of permitted file type extensions. Use this to filter input files -- Value Type: STRING | | input_raster | True | None | A valid S3 URL containing image files. | Specify a raster from which to run the task. -- Value Type: ENVIRASTER | | input_raster_format | False | None | [See ENVIRASTER input type](https://github.com/TDG-Platform/docs/blob/master/ENVI_Task_Runner_Inputs.md) | Provide the format of the image, for example: landsat-8. -- Value Type: STRING | | input_raster_band_grouping | False | None | [See ENVIRASTER input type](https://github.com/TDG-Platform/docs/blob/master/ENVI_Task_Runner_Inputs.md) | Provide the name of the band grouping to be used in the task, ie - panchromatic. -- Value Type: STRING | | input_raster_filename | False | None | [See ENVIRASTER input type](https://github.com/TDG-Platform/docs/blob/master/ENVI_Task_Runner_Inputs.md) | Provide the explicit relative raster filename that ENVI will open. This overrides any file lookup in the task runner. -- Value Type: STRING | | index | True | None | string | Specify a string, or array of strings, representing the pre-defined spectral indices to apply to the input raster. -- Value Type: STRING | | output_raster_uri_filename | False | None | string | Specify a string with the fully-qualified path and filename for OUTPUT_RASTER. -- Value Type: STRING | ## <a name="Outputs"></a>Outputs The following table lists all the outputs from this task. | Name | Required | Description | | ----------------- | :------: | ---------------------------------------- | | output_raster_uri | True | Output for OUTPUT_RASTER. | | task_meta_data | False | GBDX Option. Output location for task meta data such as execution log and output JSON. | ##### Output Structure The output_raster image file will be written to the specified S3 Customer Account Location in GeoTiff (\*.tif) format, with an ENVI header file (\*.hdr). ## <a name="Advanced Options"></a>Advanced Options To link the workflow of 1 input image into AOP_Strip_Processor and the Spectral Index task, use the following gbdxtools script in python. [block:code] { "codes": [ { "code": "from gbdxtools import Interface\ngbdx = Interface()\n\n# Edit the following path to reflect a specific path to an image\ndata = 's3://gbd-customer-data/CustomerAccount#/PathToImage/'\n\naoptask = gbdx.Task(\"AOP_Strip_Processor\") \naoptask.inputs.data = data\naoptask.inputs.enable_dra = False\naoptask.inputs.bands = 'MS'\n\nenvi_ndvi = gbdx.Task(\"ENVI_SpectralIndex\")\nenvi_ndvi.inputs.input_raster = aoptask.outputs.data.value\nenvi_ndvi.inputs.index = \"Normalized Difference Vegetation Index\"\n\nworkflow = gbdx.Workflow([aoptask, envi_ndvi])\n\nworkflow.savedata(\n envi_ndvi.outputs.output_raster_uri,\n location='AOP_NDVI/output_raster_uri' # edit location to suit account\n)\n\nprint workflow.execute()\nprint workflow.status\n# Repeat workflow.status as needed to monitor progress.", "language": "python" } ] } [/block] ##<a name="Known Issues"></a>Known Issues 1) To run the task in a single workflow with Advanced Image Preprocessor, the tif file must first be removed from the AOP folder with the additional python commands listed in Advanced ## Background For additional background information on this task please refer to the <a href="http://www.harrisgeospatial.com/docs/home.html">Harris Geospatial ENVI documentation.</a>​ #### Contact Us If you have any questions or issues with this task, please contact [**gbdx-support@digitalglobe.com** ](mailto:gbdx-support@digitalglobe.com).