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zhouenbo edited this page May 17, 2019 · 35 revisions

05/17/2019

Enbo Zhou: Pull request for SegNet codes has been open. Doing experiments in bridges. Developing postprocessing codes for RiverNet.

05/03/2019

Enbo Zhou: SegNet Codes are refactored and ready for pull request. Develop postprocessing codes. Prepare to transfer codes to bridges.

04/04/2019

Samira: U-Net river extraction algorithm was initially trained with 8-band WorldView satellite imagery. However, the results have been improved when the model was trained with 3-band imagery recently. Using the implemented U-Net algorithm, it only takes about 2 minutes to process and extract rivers from a WorldView satellite imagery.

03/22/2019

Enbo Zhou: A RiverNet adding more ingredients to SegNet is designed and trained for river classification. Some postprocessing procedures still need.

03/01/2019

Samira: Different layers of data has been passed through the algorithm. The average test accuracy of 85% was achieved without fine-tuning.

Enbo ZHou: Preliminary river extraction results have been gotten. Next step fine-tune the network and generate the vector file of river network.

02/15/2019

Samira: A Keras based deep CNN architecture is employed to perform pixel based segmentation of river and non-river in the WorldView satellite imagery. The model was trained on a small training set which was already digitized manually, each with 8 bands and 2 classification masks. Image augmentation is used for input images to increase training data. Training was done on a NVIDIA Tesla P100-16GB GPU. Accuracy will be updated soon.

01/02/2019

Enbo Zhou: Implement the Segnet to conduct image semantic labelling, i.e. remote sensing classification. Experiments using datasets provided by ISPRS and Greenland WorldView Images are now carried in Servers, which need a long time. The method's accuracy can achieve 85% now.

12/07/2018

Enbo Zhou: Prepare the training data to test pixel-based classfication.

Build a CNN network to train the classfication model.

Potential Pipeline: One image is fed into one pipeline. Building different scale's image --> Feature extraction for every pixel --> classify each pixel -> smooth the classfication result -->large river and small streams extraction.

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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