{"_id":"57dc3bed2cfd450e00643516","__v":1,"parentDoc":null,"project":"55faeacad0e22017005b8265","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"},"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"},"user":"55fae9d4825d5f19001fa379","updates":[],"next":{"pages":[],"description":""},"createdAt":"2016-09-16T18:37:33.741Z","link_external":false,"link_url":"","githubsync":"","sync_unique":"","hidden":false,"api":{"settings":"","results":{"codes":[]},"auth":"required","params":[],"url":""},"isReference":false,"order":20,"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/113a628-ENVI_ISO.jpg\",\n        \"ENVI_ISO.jpg\",\n        800,\n        320,\n        \"#4c4239\"\n      ],\n      \"caption\": \"Before: Input raster image before ENVI ISODATA Classification\"\n    }\n  ]\n}\n[/block]\n\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/def48de-ENVI_ISO_Legend.jpg\",\n        \"ENVI_ISO_Legend.jpg\",\n        800,\n        320,\n        \"#ecb48b\"\n      ],\n      \"caption\": \"After: New raster image with classification classes after running ENVI ISODATA Classification. This example used 4 classes, as shown in the legend\"\n    }\n  ]\n}\n[/block]\n## <a name=\"Quickstart\"></a>Quickstart Tutorial\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\\ndata = 's3://gbd-customer-data/CustomerAccount#/PathToImage/'\\n\\nenvitask = gbdx.Task(\\\"ENVI_ISODATAClassification\\\")\\nenvitask.inputs.input_raster = data\\n\\nworkflow = gbdx.Workflow([ envitask ])\\n\\nworkflow.savedata(\\n    envitask.outputs.output_raster_uri,\\n    location=\\\"ISODATA/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 | 308.27 | 0.99 \nWV02|35,872,942|329.87|1,939.17 | 5.88\nWV03|35,371,971|196.27| 858.28|4.37\nGE| 57,498,000|332.97|490.32| 1.47\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| change_threshold_percent   |  False   |  '2.0'  |              string double               | The change threshold percentage that determines when to complete the classification.  When the percentage of pixels that change classes during an iteration is less than the threshold value, the classification completes. -- Value Type: DOUBLE |\n| number_of_classes          |  False   |   '5'   |               string uint                | The requested number of classes to generate. -- Value Type: UINT |\n| iterations                 |  False   |  '10'   |               string uint                | The maximum iterations to perform.  If the change threshold percent is not met before the maximum number of iterations is reached, the classification completes. -- 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### Outputs\n\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\n\nThis script links the [Advanced Image Preprocessor](https://github.com/TDG-Platform/docs/blob/master/Advanced_Image_Preprocessor.md) to the ENVI ISODATA Classification task.\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\\nisodata.inputs.number_of_classes = '10'\\nisodata.inputs.iterations = '16'\\n\\nworkflow = gbdx.Workflow([ aoptask, isodata ])\\n\\nworkflow.savedata(\\n    envitask.outputs.output_raster_uri,\\n    location=\\\"ISODATA/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\": \"json\"\n    }\n  ]\n}\n[/block]\n##<a name=\"Known Issues\"></a>Known Issues\nNo known issues.\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\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 is used to identify areas of spectral similarity within the input raster. The unsupervised classifier will produce a raster with classification classes matching the input number of classes. \n\n**GBDX Registered Name**: ENVI_ISODATAClassification\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-isodata-classification","type":"basic","title":"ENVI® ISODATA Classification"}

ENVI® ISODATA Classification

This task is used to identify areas of spectral similarity within the input raster. The unsupervised classifier will produce a raster with classification classes matching the input number of classes. **GBDX Registered Name**: ENVI_ISODATAClassification **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/113a628-ENVI_ISO.jpg", "ENVI_ISO.jpg", 800, 320, "#4c4239" ], "caption": "Before: Input raster image before ENVI ISODATA Classification" } ] } [/block] [block:image] { "images": [ { "image": [ "https://files.readme.io/def48de-ENVI_ISO_Legend.jpg", "ENVI_ISO_Legend.jpg", 800, 320, "#ecb48b" ], "caption": "After: New raster image with classification classes after running ENVI ISODATA Classification. This example used 4 classes, as shown in the legend" } ] } [/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\ndata = 's3://gbd-customer-data/CustomerAccount#/PathToImage/'\n\nenvitask = gbdx.Task(\"ENVI_ISODATAClassification\")\nenvitask.inputs.input_raster = data\n\nworkflow = gbdx.Workflow([ envitask ])\n\nworkflow.savedata(\n envitask.outputs.output_raster_uri,\n location=\"ISODATA/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 | 308.27 | 0.99 WV02|35,872,942|329.87|1,939.17 | 5.88 WV03|35,371,971|196.27| 858.28|4.37 GE| 57,498,000|332.97|490.32| 1.47 ## <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 | | change_threshold_percent | False | '2.0' | string double | The change threshold percentage that determines when to complete the classification. When the percentage of pixels that change classes during an iteration is less than the threshold value, the classification completes. -- Value Type: DOUBLE | | number_of_classes | False | '5' | string uint | The requested number of classes to generate. -- Value Type: UINT | | iterations | False | '10' | string uint | The maximum iterations to perform. If the change threshold percent is not met before the maximum number of iterations is reached, the classification completes. -- 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 | ### 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 This script links the [Advanced Image Preprocessor](https://github.com/TDG-Platform/docs/blob/master/Advanced_Image_Preprocessor.md) to the ENVI ISODATA Classification task. [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\nisodata.inputs.number_of_classes = '10'\nisodata.inputs.iterations = '16'\n\nworkflow = gbdx.Workflow([ aoptask, isodata ])\n\nworkflow.savedata(\n envitask.outputs.output_raster_uri,\n location=\"ISODATA/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": "json" } ] } [/block] ##<a name="Known Issues"></a>Known Issues No known issues. ## 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).