rschip 0.4.7

Prepare satellite images and training data for use with deep learning models

rschip

Split satellite images into smaller fixed-sized tiles, for input into convolutional neural networks (cnn), or vision transformers (ViT) such as Segment Anything.

Features

• Tile Satellite Images: Split large satellite images into smaller chips of specified dimensions. Can min-max normalise or standard scale before writing chips as required.
• Mask Segmentation: Generate segmentation mask images from geopackage or shapefile features for supervised segmentation, e.g using U-Net.
• Check Background Chips: Identify image chips containing only background. Useful for when preparing training and testing datasets.

Installation

Install rschip with pip:

pip install rschip

Requires rasterio, numpy, geopandas, and shapely.

Usage

1. ImageChip Class

The ImageChip class provides functionality for creating tiles (also known as chips) from large satellite images.

from rschip import ImageChip

# Initialize the ImageChip instance for 128 by 128 tiles
image_chipper = ImageChip(
    input_image_path="path/to/large_image.tif",
    output_path="path/to/output_directory_image",
    pixel_dimensions=128,
    offset=64,
)

# set a min max normaliser 
# e.g for 16 bit Sentinel 2 RGB might use
image_chipper.set_normaliser(min_val=500, max_val=3000)

# Generate chips
image_chipper.chip_image()

Each resulting tile is named using a suffix that represents the bottom left (x, y) pixel coordinate position. By default, the prefix of each tile name is taken from the input image file name (input_image_path), unless you specify output_name.

Using the parameter use_multiprocessing=True (default) makes chipping process faster by using multiple cores.

It is possible to resample, i.e. downscale or upscale image resolution by specifying a scale_factor argument:

# to downscale images by factor of 0.5 e.g. 0.125 metre per pixel becomes 0.25 metre per pixel. 
image_chipper = ImageChip(
    input_image_path="path/to/large_image.tif",
    output_path="path/to/output_directory_image",
    pixel_dimensions=128,
    offset=64,
    scale_factor=0.5,
)

2. SegmentationMask Class

The SegmentationMask class is used to create a segmentation mask images from geopackage or shapefile using an input image as extent and pixel size reference.

Once the segmentation mask has been created, the segmentation image can also be split into tiles. Some deep learning frameworks expect images and corresponding masks to have the same file name in separate directories. The output_name argument of ImageChip can ensure this is the case.

from rschip import SegmentationMask, ImageChip

# Initialize the SegmentationMask
seg_mask = SegmentationMask(
    input_image_path="path/to/large_image.tif",
    input_features_path="path/to/geopackage_features.gpkg",
    output_path="path/to/output_mask.tif",
    class_field="ml_class"
)

# Generate segmentation mask image
seg_mask.create_mask()

# Chip the segmentation image to match satellite image
image_chipper = ImageChip(
    input_image_path="path/to/output_mask.tif",
    output_path="path/to/output_directory_mask",
    output_name="large_image",
    pixel_dimensions=128,
    offset=64,
)
image_chipper.chip_image()

3. CheckBackgroundOnly Class

The CheckBackgroundOnly class provides functionality to list image chips that contain only background. Filtering out images only containing background helps to prepare a dataset more suitable for training models.

from rschip import CheckBackgroundOnly

# Initialize the CheckBackgroundOnly instance
checker = checkBackgroundOnly(background_val=0, non_background_min=1)

# Find chips with only background
checker.check_background_chips(
    class_chips_dir="path/to/mask_directory",
    image_chips_dir="path/to/image_directory"
)

The default assumption is that image and mask equivalent have the same file names as shown in example 2. above. If that is not the case, use the masks_prefix, images_prefix arguments which are prefix strings which are considered on checking for image to mask equivalent using the bottom left (x,y) indices found in the outputs generated by ImageChip.create_chips().

4. Create training, validation, test dataset

The DatasetSplitter class can be used to split a directory of image chips into a training, a validation, and (optionally) a test set. The process creates a dataset directory as follows:

final_dataset_output/
└── dataset/
    ├── images/
    │   ├── train/
    │   │   ├── image_chip_1.tif
    │   │   └── ...
    │   ├── val/
    │   │   ├── image_chip_2.tif
    │   │   └── ...
    │   └── test/
    │       ├── image_chip_3.tif
    │       └── ...
    └── masks/
        ├── train/
        │   ├── image_chip_1.tif
        │   └── ...
        ├── val/
        │   ├── image_chip_2.tif
        │   └── ...
        └── test/
            ├── image_chip_3.tif
            └── ...

By default this process uses the CheckBackgroundOnly to first find chips with only background and not use those in the list of images to split. This means you can skip straight to this step without explicitly running the CheckBackgroundOnly process first if you wish.

from rschip import DatasetSplitter

# initialize the DatasetSplitter
splitter = DatasetSplitter(
    image_dir="path/to/image_chips",
    mask_dir="path/to/mask_chips",
    output_dir="path/to/final_dataset_output",
    train_ratio=0.7,
    val_ratio=0.2,
    test_ratio=0.1,
    seed=42,
    filter_background_only=True,
)

#  the split
splitter.split()

License

This project is licensed under the MIT License - see the LICENSE file for details.