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bhlab / Label-Pixels

Label-Pixels is a tool for semantic segmentation of remote sensing images using fully convolutional networks (FCNs), designed for extracting the road network from remote sensing imagery and it can be used in other applications applications to label every pixel in the image ( Semantic segmentation).

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Label-Pixels

Label-Pixels is the tool for semantic segmentation of remote sensing imagery using Fully Convolutional Networks (FCNs). Initially, this tool developed for extracting the road network from high-resolution remote sensing imagery. And now, this tool can be used to extract various features (Semantic segmentation of remote sensing imagery). This is part of my MSc research work (Automatic Road Extraction from High-Resolution Remote Sensing Imagery using Fully Convolutional Networks and Transfer Learning), worked under Mr. Ashutosh Kumar Jha (IIRS) and Dr. Claudio Persello (University of Twente, ITC).

Tested on

python 3.6.13
Keras 2.3.1
Tensorflow 2.1.0
gdal 2.2.2
scikit-learn 0.23.2

Installation

Clone repository

git clone https://github.com/venkanna37/Label-Pixels.git
cd Label-Pixels

Setup environment

  • Create an environment and install above packages OR install with environment.yml file in Anaconda
  • Note: environment.yml file created with conda env export > environment.yml command in Anaconda
conda env create -f environment.yml
conda activate label-pixels
cd tools

Usage

Rasterize

  • Creates labels with shapefiles
  • The projection of imagery and shapefiles should be same
  • Projection units should be in meters if you want to buffer line feature
  • --buffer not required for polygon
  • --labels_atr not required for line and single class
  • output directory has to create to save label images Ex: ../data/spacenet/labels/
options Description
--help Print usage information
--raster_dir Directory that contains raster image/images
--vector_dir Directory that contains vector files with the same projection as raster data. And name of the vector and raster files should be same.
--raster_format Raster format of the image/images
--vector_format Vector format ex: shp, geojson, etc.
--output_dir Output directory to save labels
--buffer Buffer length for line feature. Not required for polygon
--buffer_atr Attribute from the vector file, this attribute can be buffer width and it multiplies with --buffer.
--labels_atr Attribute from the vector file, pixels inside the polygon will be assigned by its attribute value.

Examples:

python rasterize.py --raster_dir ../data/spacenet/raster/ --raster_format tif --vector_dir ../data/spacenet/vector/ --vector_format shp --buffer 2 --output_dir ../data/spacenet/labels/ --label_atr partialDec --buffer_atr lanes

python rasterize.py --raster_dir ../data/spacenet/raster/ --vector_dir ../data/spacenet/vector_multi/ --vector_format shp --output_dir ../data/spacenet/labels/ --label_atr value

Patch Generation

  • Generate patches from images/tiles
  • To generate patches for train, test and validation sets, the command needs to be run three times
  • Name of image and label files should be same
  • Output directory has to create to save patches Ex: ../data/mass_patches/
options Description
--image_folder Folder of input images/tiles with directory
--image_format Image format tiff/tif/jpg/png
--label_folder Folder of label images with directory
--label_format Label format tiff/tif/jpg/png
--patch_size Patch size to feed network. Default size is 256
--overlap Overlap between two patches on image/tile (units: pixels)
--output_folder Output folder to save patches

Example:

python patch_gen.py --image_folder ../data/mass_sample/test/image/ --image_format tiff --label_folder ../data/mass_sample/test/roads_and_buildings/ --label_format tif --patch_size 256 --output_folder ../data/mass_patches/

CSV Paths

  • Save directories of patches in CSV file instead of reading patches from folders directly
  • Output directory has to create to save the csv files Ex: ../paths/
options Description
--image_folder Folder of image patches with directory
--image_format Image format tif (patch_gen.py save patches in tif format)
--label_folder Folder of label patches with directory
--label_format Label format tif (patch_gen.py save patches in tif format)
--patch_size Patch size to feed network. Default size is 256
--output_csv csv filename with directory

Example

python csv_paths.py --image_folder ../data/mass_patches/image/ --label_folder ../data/mass_patches/label/ --output_csv ../paths/data_rnb.csv

Training

  • Training FCNs (UNet, SegNet, ResUNet and UNet-Mini) for semantic segmentation
  • For Binary classification, --num_classes = 1
  • For Binary classification with one-hot encoding, --num_classes = 2
  • For multi class classification, --num_classes = number of target classes (>1)
options Description
--model Name of the FCN model. Existing models are unet, unet_mini, segnet and resunet
--train_csv CSV file name with directory, consists of directories of image and label patches of training set.
--valid_csv CSV file name with directory, consists of directories of image and label patches of validation set.
--input_shape Input shape of model to feed patches (patch_size patch_size channels)
--batch_size Batch size, depends on GPU/CPU memory
--num_classes Number of classes in labels data
--epochs Number of epochs

Example

python train.py --model unet_mini --train_csv ../paths/data_rnb.csv --valid_csv ../paths/data_rnb.csv --input_shape 256 256 3 --batch_size 4 --num_classes 3 --epochs 100

Accuracy

  • Calculates the accuracy using different accuracy metrics.
  • IoU, F1-Score, Precision and Recall
options Description
--input_shape Input shape of model (patch_size, patch_size, channels)
--weights Trained model with directory
--csv_paths CSV file name with directory, consists of directories of image and label patches of test set.
--num_classes Number of classes in labels data

Example

python accuracy.py --model unet_mini --input_shape 256 256 3 --weights ../trained_models/unet_mini300_06_07_20.hdf5 --csv_paths ../paths/data_rnb.csv --num_classes 3

Prediction

  • Predicts the entire image/tile with trained model.
  • Output directory has to create to save the predicted images Ex: ../data/predictions/
options Description
--input_shape Input shape of model (patch_size, patch_size, channels)
--weights Trained model with directory
--image_folder Folder of input images/tiles with directory
--image_format Image format tiff/tif/jpg/png
--output_folder Output folder to save predicted images/tiles

Example:

python tile_predict.py --model unet_mini --input_shape 256 256 3 --weights ../trained_models/unet_mini300_06_07_20.hdf5 --image_folder ../data/mass_sample/test/image/ --image_format tiff --output_folder ../data/predictions/ --num_classes 3

Summary of the Model

  • Summary of FCNs
  • Useful to check the configuration of Fully Convolutional Networks
  • Replace unet, segnet and resunet with unet_mini to check configuration of all netowrks
options Description
--model Name of FCN model. Existing models are unet, unet_mini, segnet and resunet
--input_shape Input shape of model to feed (patch_size patch_size channels)
--num_classes Number of classes to train

Example

python summary.py --model unet_mini --input_shape 256 256 3 --num_classes 3

Example Outputs

Benchmark datasets

  1. Massachusetts Benchmark datasets for Roads and Buildings extraction
    https://academictorrents.com/browse.php?search=Volodymyr +Mnih
  2. List of Benchmark datasets for semantic segmentation, object detection from remote sensing imagery https://github.com/chrieke/awesome-satellite-imagery -datasets

Any problem with code?

Open issue or mail me 👉 g.venkanna37@gmail.com

About

Label-Pixels is a tool for semantic segmentation of remote sensing images using fully convolutional networks (FCNs), designed for extracting the road network from remote sensing imagery and it can be used in other applications applications to label every pixel in the image ( Semantic segmentation).

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