Available with Spatial Analyst license.
Summary
Uses a remote sensing image to convert labeled vector or raster data into deep learning training datasets. The output is a folder of image chips and a folder of metadata files in the specified format.
Usage
This tool will create training datasets to support third-party deep learning applications, such as Google TensorFlow, PyTorch, or Microsoft CNTK.
Deep learning class training samples are based on small subimages containing the feature or class of interest, called image chips.
Use your existing classification training sample data, or GIS feature class data such as a building footprint layer, to generate image chips containing the class sample from your source image. Image chips are often 256 pixel rows by 256 pixel columns, unless the training sample size is larger.
The cell size and extent can be adjusted using the geoprocessing environment settings.
Syntax
ExportTrainingDataForDeepLearning(in_raster, out_folder, in_class_data, image_chip_format, {tile_size_x}, {tile_size_y}, {stride_x}, {stride_y}, {output_nofeature_tiles}, {metadata_format}, {start_index}, {class_value_field}, {buffer_radius})| Parameter | Explanation | Data Type |
in_raster | The input source imagery, typically multispectral imagery. Examples of the type of input source imagery include multispectral satellite, drone, aerial, or National Agriculture Imagery Program (NAIP). | Raster Dataset; Raster Layer; Mosaic Layer; Image Service; MapServer; Map Server Layer; Internet Tiled Layer |
out_folder | The folder where the output image chips and metadata will be stored. | Folder |
in_class_data | The training sample data, in either vector or raster form. Vector inputs should follow a training sample format as generated by the ArcGIS Desktop Image Classification toolbar. Raster inputs should follow a classified raster format as generated by the Classify Raster tool. | Feature Class; Feature Layer; Raster Dataset; Raster Layer; Mosaic Layer; Image Service |
image_chip_format | Specifies the raster format for the image chip outputs. PNG and JPEG support up to 3 bands.
| String |
tile_size_x (Optional) | The size of the image chips for the X dimension. | Long |
tile_size_y (Optional) | The size of the image chips for the Y dimension. | Long |
stride_x (Optional) | The distance to move in the X direction when creating the next image chips. When stride is equal to tile size, there will be no overlap. When stride is equal to half the tile size, there will be 50 percent overlap. | Long |
stride_y (Optional) | The distance to move in the Y direction when creating the next image chips. When stride is equal to tile size, there will be no overlap. When stride is equal to half the tile size, there will be 50 percent overlap. | Long |
output_nofeature_tiles (Optional) | Specifies whether image chips that do not capture training samples will be exported.
| Boolean |
metadata_format (Optional) | Specifies the format of the output metadata labels. The four options for output metadata labels for the training data are KITTI rectangles, PASCAL VOC rectangles, Classified Tiles (a class map), and RCNN Masks. If your input training sample data is a feature class layer, such as a building layer or standard classification training sample file, use the KITTI or PASCAL VOC rectangles option. The output metadata is a .txt file or .xml file containing the training sample data contained in the minimum bounding rectangle. The name of the metadata file matches the input source image name. If your input training sample data is a class map, use the Classified Tiles option as your output metadata format.
For the KITTI metadata format, 15 columns are created, but only 5 of them are used in the tool. The first column is the class value. The next 3 columns are skipped. Columns 5-8 define the minimum bounding rectangle, which is comprised of 4 image coordinate locations: left, top, right, and bottom pixels, respectively. The minimum bounding rectangle encompasses the training chip used in the deep learning classifier. The remaining columns are not used. For more information, see KITTI metadata format The following is an example of the PASCAL VOC option: For more information, see PASCAL Visual Object Classes | String |
start_index (Optional) | The start index for the sequence of image chips. This allows you to append more image chips to an existing sequence. The default value is 0. | Long |
class_value_field (Optional) | The field that contains the class values. If no field is specified, the system searches for a value or classvalue field. If the feature does not contain a class field, the system determines that all records belong to one class. | Field |
buffer_radius (Optional) | The radius for a buffer around each training sample to delineate a training sample area. This allows you to create circular polygon training samples from points. The linear unit of the in_class_data spatial reference is used. | Double |
.Code sample
ExportTrainingDataForDeepLearning example 1 (Python window)
This example creates training samples for deep learning.
from arcpy.sa import *
ExportTrainingDataForDeepLearning("c:/test/image.tif", "c:/test/outfolder",
"c:/test/training.shp", "TIFF", "256",
"256", "128", "128", "NO", "KITTI_rectangles")
ExportTrainingDataForDeepLearning example 2 (stand-alone script)
This example creates training samples for deep learning.
# Import system modules and check out ArcGIS Spatial Analyst extension license
import arcpy
arcpy.CheckOutExtension("Spatial")
from arcpy.sa import *
# Set local variables
inRaster = "c:/test/image.tif"
out_folder = "c:/test/outfolder"
in_training = "c:/test/training.shp"
image_chip_format = "TIFF"
tile_size_x = "256"
tile_size_y = "256"
stride_x="128"
stride_y="128"
output_nofeature_tiles="NO"
metadata_format="KITTI_rectangles"
# Execute
ExportTrainingDataForDeepLearning(inRaster, out_folder, in_training,
image_chip_format,tile_size_x, tile_size_y,
stride_x, stride_y,output_nofeature_tiles,
metadata_format)
Environments
Licensing information
- Basic: Requires Spatial Analyst
- Standard: Requires Spatial Analyst
- Advanced: Requires Spatial Analyst