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samiradaneshgar edited this page Oct 26, 2018 · 35 revisions

10/26/2018

Initial image classification results using a pretrained DenseNet201 convolutional neural network.

10/12/2018

In the second attempt, windows of 227 × 227 pixels were defined in the WorldView2 satellite imagery. The defined 332 regions of interest (ROI’s) were consisted of four classes:

  1. Large river (134 ROI’s)
  2. Small river (119 ROI’s)
  3. Crevasse (40 ROI’s)
  4. Ice/slush (39 ROI’s)

We fine-tuned a pretrained AlexNet convolutional neural network to perform classification in the WorldView2 satellite imagery. The network requires input images of size 227-by-227-by-3 (3 is the number of color channels), and outputs a label for the feature in the image together with the probabilities for each of the classes.

We performed the following steps:

  • Loaded the 333 images as an image datastore.
  • Divided the data into training (80%) and validation (20%) data sets.
  • Replaced the last three layers of the pretrained AlexNet with a fully connected layer, a softmax layer, and a classification output layer.
  • Set the fully connected layer to have the same size as the number of classes (four classes).
  • Increased the WeightLearnRateFactor and BiasLearnRateFactor values of the fully connected layer to 20.
  • Set the initial learning rate to a small value of 0.0001.

We reached the classification accuracy of 91% on the validation set.

09/28/2018

The goal of the first attempt was to design a supervised method to estimate probabilities of actively flowing supraglacial streams and glacio-hydrological features such as crevasses, slush, and ice in high-resolution multispectral WorldView2 satellite imagery collected over the Greenland ice sheet ablation zone on July 2012.

Firstly, 565 windows of 25 × 25 pixels were defined in the WorldView2 satellite imagery. The defined 565 regions of interest (ROI’s) were consisted of six classes:

  1. Large river (170 ROI’s)
  2. Lake (28 ROI’s)
  3. Small river (139 ROI’s)
  4. Crevasse (35 ROI’s)
  5. Dust/slush (37 ROI’s)
  6. Ice/slush (156 ROI’s)

For each ROI (25 × 25 pixels) that we defined in the WorldView2 satellite imagery, we extracted the mean and standard deviation of the following 21 features:

  • Multi-spectral signatures (8 features)
  • Modified normalized difference water index (MNDWI) (one feature)
  • Gaussian filtering in four scales (four features)
  • Morphological transformation (six features)
  • Phase congruency (one feature)
  • Hessian (one feature)

Therefore, a set of 42-D feature vectors was generated for each ROI. Then, we used the Neural Network Pattern Recognition app in MATLAB https://www.mathworks.com/help/deeplearning/gs/classify-patterns-with-a-neural-network.html to classify input data into a set of target categories. Input data to the Network were 565 42-D feature vectors and the target data of the Network were 6 classes of large river, lake, small river, crevasse, dust/slush, ice/slush. Input vectors and target vectors were randomly divided into three sets as follows:

  • 70% are used for training.
  • 15% are used to validate that the network is generalizing and to stop training before overfitting.
  • The last 15% are used as a completely independent test of network generalization.

After we trained the Network, we evaluated the performance of the Network on some test areas of 50 × 50 pixels. The output probabilities indicated the high accuracy of the trained Network in detection of large river, lake, and small river.

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