{"_id":"57eab5eb2e84700e00743350","project":"55faeacad0e22017005b8265","user":"55fae9d4825d5f19001fa379","parentDoc":null,"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":7,"slug":"algorithm-guide","title":"Certified Algorithms"},"version":{"_id":"55faeacad0e22017005b8268","project":"55faeacad0e22017005b8265","__v":34,"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"],"is_deprecated":false,"is_hidden":false,"is_beta":false,"is_stable":true,"codename":"v1","version_clean":"1.0.0","version":"1"},"__v":1,"updates":[],"next":{"pages":[],"description":""},"createdAt":"2016-09-27T18:09:47.187Z","link_external":false,"link_url":"","githubsync":"","sync_unique":"","hidden":false,"api":{"settings":"","results":{"codes":[]},"auth":"required","params":[],"url":""},"isReference":false,"order":13,"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\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/9c0b1ce-NotSmoothedFinal.jpg\",\n        \"NotSmoothedFinal.jpg\",\n        800,\n        320,\n        \"#2f532c\"\n      ],\n      \"caption\": \"Before: This is a classified image before ENVI Classification Smoothing\"\n    }\n  ]\n}\n[/block]\n\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/d7dc164-AfterSmoothingKernel7_1.jpg\",\n        \"AfterSmoothingKernel7 (1).jpg\",\n        800,\n        320,\n        \"#177014\"\n      ],\n      \"caption\": \"After: This is the output of the ENVI Classification Smoothing task\"\n    }\n  ]\n}\n[/block]\n\n## <a name=\"Quickstart\"></a>Quickstart Tutorial\nThis task requires that the image has been pre-processed using the [Advanced Image Preprocessor](doc:advanced-image-preprocessor), and that a classification has been run on the output from pre-processing. In the example workflow below, the [ENVI ISODATAClassification](doc:envi-isodataclassification)  task was utilized to perform the classification step on preprocessed data. \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\\n#\\tNote: Input raster must be a classification image, see advanced for example\\ndata = 's3://gbd-customer-data/CustomerAccount#/PathToImage/'\\n\\nenvi = gbdx.Task(\\\"ENVI_ClassificationSmoothing\\\")\\nenvi.inputs.input_raster = data\\n\\nworkflow = gbdx.Workflow([ envi ])\\n\\nworkflow.savedata(\\n    envi.outputs.output_raster_uri, \\n    location=\\\"Smoothing/output_raster_uri\\\"\\n)\\n\\nprint workflow.execute()\\nprint workflow.status\\n# Repeat workflow.status as needed to monitor progress.\\n\",\n      \"language\": \"python\"\n    }\n  ]\n}\n[/block]\n\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--------|:----------:|-----------|----------------|---------------\nQB02 | 41,551,668 | 312.07 | 173.91 | 0.56 \nWV02|35,872,942 | 329.87 | 174.86 | 0.53 \nWV03|35,371,971 | 196.27 | 161.52 | 0.82 \nGE01| 57,498,000 | 332.97 | 181.06 | 0.54 \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| 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| kernel_size                |  False   |   '3'   |    string uint (any odd number >= 3)     | The smooth kernel size, using an odd number (e.g., 3 = 3x3 pixels). -- Value Type: UINT |\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\n## <a name=\"Outputs\"></a>Outputs\nThe following table lists all the tasks outputs.\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\nBelow is a complete end-to-end workflow for Advanced Image Preprocessor =>  ENVI ISODATA Classification => ENVI Classification Smoothing:\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\\nisodata = gbdx.Task(\\\"ENVI_ISODATAClassification\\\")\\nisodata.inputs.input_raster = aoptask.outputs.data.value\\n\\nsmooth = gbdx.Task(\\\"ENVI_ClassificationSmoothing\\\")\\nsmooth.inputs.input_raster = isodata.outputs.output_raster_uri.value\\nsmooth.inputs.kernel = '9'\\n\\nworkflow = gbdx.Workflow([ aoptask, isodata, smooth ])\\n\\nworkflow.savedata(\\n    smooth.outputs.output_raster_uri, \\n    location=\\\"Classification/Smoothing\\\"\\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=\"Known Issues\"></a>Known Issues\nNone at this time.\n\n## <a name=\"Background\"></a>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 removes speckling noise from a classification image. It uses majority analysis to change spurious pixels within a large single class to that class.  The ENVI Classification Smoothing task requires that the image has been pre-processed using the Advanced Image Preprocessor, and that a classification (e.g. the ENVI ISODATAClassification) has been run on the output from pre-processing. \n\n**GBDX Registered Name:** ENVI_ClassificationSmoothing\n**Provider:** Harris\tGeospatial 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-classification-smoothing","type":"basic","title":"ENVI® Classification Smoothing"}

ENVI® Classification Smoothing

This task removes speckling noise from a classification image. It uses majority analysis to change spurious pixels within a large single class to that class. The ENVI Classification Smoothing task requires that the image has been pre-processed using the Advanced Image Preprocessor, and that a classification (e.g. the ENVI ISODATAClassification) has been run on the output from pre-processing. **GBDX Registered Name:** ENVI_ClassificationSmoothing **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/9c0b1ce-NotSmoothedFinal.jpg", "NotSmoothedFinal.jpg", 800, 320, "#2f532c" ], "caption": "Before: This is a classified image before ENVI Classification Smoothing" } ] } [/block] [block:image] { "images": [ { "image": [ "https://files.readme.io/d7dc164-AfterSmoothingKernel7_1.jpg", "AfterSmoothingKernel7 (1).jpg", 800, 320, "#177014" ], "caption": "After: This is the output of the ENVI Classification Smoothing task" } ] } [/block] ## <a name="Quickstart"></a>Quickstart Tutorial This task requires that the image has been pre-processed using the [Advanced Image Preprocessor](doc:advanced-image-preprocessor), and that a classification has been run on the output from pre-processing. In the example workflow below, the [ENVI ISODATAClassification](doc:envi-isodataclassification) task was utilized to perform the classification step on preprocessed data. 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\n#\tNote: Input raster must be a classification image, see advanced for example\ndata = 's3://gbd-customer-data/CustomerAccount#/PathToImage/'\n\nenvi = gbdx.Task(\"ENVI_ClassificationSmoothing\")\nenvi.inputs.input_raster = data\n\nworkflow = gbdx.Workflow([ envi ])\n\nworkflow.savedata(\n envi.outputs.output_raster_uri, \n location=\"Smoothing/output_raster_uri\"\n)\n\nprint workflow.execute()\nprint workflow.status\n# Repeat workflow.status as needed to monitor progress.\n", "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 --------|:----------:|-----------|----------------|--------------- QB02 | 41,551,668 | 312.07 | 173.91 | 0.56 WV02|35,872,942 | 329.87 | 174.86 | 0.53 WV03|35,371,971 | 196.27 | 161.52 | 0.82 GE01| 57,498,000 | 332.97 | 181.06 | 0.54 ## <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 | | -------------------------- | :------: | :-----: | :--------------------------------------: | ---------------------------------------- | | 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 | | kernel_size | False | '3' | string uint (any odd number >= 3) | The smooth kernel size, using an odd number (e.g., 3 = 3x3 pixels). -- Value Type: UINT | | 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 tasks outputs. | 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 Below is a complete end-to-end workflow for Advanced Image Preprocessor => ENVI ISODATA Classification => ENVI Classification Smoothing: [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\nisodata = gbdx.Task(\"ENVI_ISODATAClassification\")\nisodata.inputs.input_raster = aoptask.outputs.data.value\n\nsmooth = gbdx.Task(\"ENVI_ClassificationSmoothing\")\nsmooth.inputs.input_raster = isodata.outputs.output_raster_uri.value\nsmooth.inputs.kernel = '9'\n\nworkflow = gbdx.Workflow([ aoptask, isodata, smooth ])\n\nworkflow.savedata(\n smooth.outputs.output_raster_uri, \n location=\"Classification/Smoothing\"\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 None at this time. ## <a name="Background"></a>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).