Pattern recognition is one of the most important tasks in aerospace image processing. Various methods based on convolutional neural networks attain state-of-the-art accuracy; however, their effectiveness on exact images is influenced by the chosen architecture and its training parameters.This work present methods based on convolutional neural networks for pattern recognition on the aerospace images. A possibility for objects segmentation into ten classes is demonstrated on example of the multispectral images from the World View 3 satellite. Four networks with different architectures were built, trained and optimized parametrically based on the auto-encoder neural networks. Segmentation results has been analyzed by means of three parameters: training Jacard Index, testing Jacard Index and weight numbers. The positive impact of the properly selected shearing augmentation on extension of a small marked dataset is discussed. The influence of the nonequilibrium classes on the segmentation accuracy and how to account this feature during training of deep neural networks is pointing out.


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    Title :

    Pattern recognition on aerospace images using deep neural networks


    Contributors:


    Publication date :

    2020-06-01


    Size :

    1591488 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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