/Hyperspectral-Image-Segmentation

Semantic Segmentation of HyperSpectral Images using a U-Net with Depthwise Separable Convolutions

Primary LanguagePythonMIT LicenseMIT

Hyperspectral-Image-Segmentation

Semantic Segmentation of HyperSpectral Images using a U-Net with Separable Convolutions.

Features

  • HyperSpectral Images (HSI) are semantically segmented using two variants of U-Nets and their performance is comparaed.
  • Model A uses Depthwise Separable Convolutions in the downsampling arm of the U-Net, and Model B uses Convolutions in the downsampling arm of the U-Net.
  • Due to the lack of multiple HSI image and ground truth pairs, we train the models by extracting patches of the image. Here, patches are smaller square regions of the image. After training the model, we make predictions patch-wise.
  • Patches are extracted using a stride of 1 for training. We used patches of size patch_size = 4 (4x4 square regions) for our experiment.
  • We use the sample-weight feature offered by Keras to weight the classes in the loss function by their log frequency. We use this as there is a skew in the number of examples per class.
  • Some classes do not have patches of size patch_size = 4. For these classes, we create synthetic patches of size patch_size = 4 by using patches of size 1.
  • Experimental results are tabulated below.

Requirements

  • Keras
  • TensorFlow (>1.4)
  • Scikit-Learn
  • Numpy
  • Scipy

Instructions

  1. Clone the repository and change working directory using:
git clone https://github.com/thatbrguy/Hyperspectral-Image-Segmentation.git \
&& cd Hyperspectral-Image-Segmentation
  1. Download the dataset from here or by using the following commands:
wget http://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat
wget http://www.ehu.eus/ccwintco/uploads/c/c4/Indian_pines_gt.mat
  1. Train the model using:
python main.py \
--model A \
--mode train

The file main.py supports a few options, which are listed below:

  • --model: (required) Choose between models A and B.
  • --mode: (required) Choose between training (train) and inference (infer) modes.
  • --weights: (required for inference only) Path of the weights file for inference mode. Pretrained weights for both models (A and B) are available in the weights directory.
  • --epochs: Set the number of epochs. Default value is 100.
  • --batch_size: Set the batch size. Default value is 200.
  • --lr: Set the learning rate. Default value is 0.001.

Results

Indian Pines Dataset

Output Maps

Ground Truth Model A Model B
GT modelA modelB

Epoch vs Accuracy

Plot

Tabulatation

Class Number Class Name Ground Truth Samples Model A Model B
Predicted Samples Accuracy Predicted Samples Accuracy
1 Alfalfa 46 30 65.21 29 63.04
2 Corn notill 1428 1343 94.05 1324 92.72
3 Corn mintill 830 753 90.72 762 91.81
4 Corn 237 189 79.75 186 78.48
5 Grass pasture 483 449 92.96 439 90.89
6 Grass trees 730 717 98.22 710 97.26
7 Grass pasture mowed 28 28 100 28 100
8 Hay windrowed 478 476 99.58 473 98.95
9 Oats 20 20 100 7 35
10 Soybean notill 972 843 86.72 844 86.83
11 Soybean mintill 2455 2328 94.83 2311 94.13
12 Soybean clean 593 524 88.36 530 89.38
13 Wheat 205 175 85.37 176 85.85
14 Woods 1265 1246 98.50 1229 97.15
15 Buildings Grass Trees Drives 386 386 100 382 98.96
16 Stone Steel Towers 93 92 98.92 91 97.85
Overall Accuracy (OA) 93.66 92.90
Average Accuracy (AA) 92.07 87.39
Kappa Coefficient (k) 92.77 91.91

References:

  1. Dataset