Use the Build Object Detection Rasters from COCO tool to generate object detection rasters using a JSON dataset containing COCO (Common Objects in Context) formatted annotations. Each raster corresponds to labeled regions of interest defined in the COCO input annotations.

An object detection raster is the same as a deep learning raster, except that it contains additional GeoJSON information about bounding boxes, stored in the raster metadata.

You can also write a script to build an object detection raster using the BuildObjectDetectionRastersFromCOCO task.

Follow these steps:

  1. In the ENVI Toolbox, select Deep Learning > Object Detection > Preprocessing > Build Object Detection Rasters from COCO. The Build Object Detection Rasters from COCO dialog appears.
  2. In the Input COCO JSON field, specify the COCO-format annotation JSON file that defines the input raster images and their associated labeled regions of interest. Each entry in the file should follow the COCO specification, including image metadata and bounding box annotations for object detection.
  3. In the Number of Rasters field, enter the number of object detection rasters to generate from the COCO-annotated dataset. Previously generated rasters are skipped, allowing incremental creation of the full dataset over time.
  4. For Random Selection, select Yes if you want to randomly select rasters from the COCO dataset. If the Number of Rasters value equals the total number available rasters, this parameter will be ignored.
  5. In the Random Seed field, enter a seed value to ensure reproducible results. When Random Selection is enabled, using the same seed will consistently generate the same subset of rasters from the dataset.

  6. For Enhance Display, select Yes if you want to apply an additional small stretch to the processed data to suppress noise and enhance feature visibility. The optional stretch is effective for improving visual clarity in imagery acquired from aerial platforms or sensors with higher noise profiles.

  7. For Visual RGB, select Yes if you want to encode the output raster as a three-band RGB composite (red, green, blue) for color image processing. This ensures consistent band selection from ENVI display types (such as RGB, CIR, and pan) and supports integration of diverse data sources (such as MSI, panchromatic, and VNIR) without band mismatch.

  8. In the Output Raster field, select a location and filename for the object detection raster.

  9. Enable the Display result check box to display the output in the view when processing is complete.

  10. To reuse these task settings in future ENVI sessions, save them to a file. Click the down arrow next to the OK button and select Save Parameter Values, then specify the location and filename to save to. Note that some parameter types, such as rasters, vectors, and ROIs, will not be saved with the file. To apply the saved task settings, click the down arrow and select Restore Parameter Values, then select the file where you previously stored your settings.

  11. To run the process in the background, click the down arrow and select Run Task in the Background. If an ENVI Server has been set up on the network, the Run Task on remote ENVI Server name is also available. The ENVI Server Job Console will show the progress of the job and will provide a link to display the result when processing is complete. See the ENVI Servers topic in ENVI Help for more information.

  12. Click OK. ENVI adds the resulting output to the Data Manager and Layer Manager.

See Also

Build Object Detection Raster From Annotation, Build Object Detection Raster From ROI, Build Object Detection Raster From Vector, Object Detection, View Object Detection Raster Labels