Super-resolution decision-making tool using deep convolution neural networks for panchromatic images

In this paper, a Deep Convolution Neural Network (CNN) based Super-Resolution (SR) decision-making tool is proposed for the raw panchromatic satellite image. Conventionally, human visual interpretation through spotting recognizable objects and visualizing the patterns at a certain zooming level in terms of sharp edges, blurs, etc., is in practice at the cost of time consumption and expertise knowledge requirement. A 10-layer deep convolutional neural network is carried out in three steps with inspiration from the categorical signature classification. In the first step, the higher the potential interpretive features for defining the pattern are analyzed. The cross-correlation of interpretative features with the trained images is done in the second step. In the final step, whether the SR technique application is inevitable for the image or not rendered is provided with an accuracy of 83.3% at a minimized loss. The proposed method is tested with seven state-of-the-art classifier architectures for the decision-making tool. The proposed method works well with the dataset considered counterfeiting other techniques.

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