Abstract
For satellite image processing using deep learning, the images are first resized to one standard size and then applied to the convolutional neural network(CNN) model to train and test the model. The primary step of preprocessing the images is resizing, which is done by various approaches such as bicubic interpolation, bilinear interpolation, and nearest neighbour interpolation. The impact of resizing technique on the performance of the deep learning model is unexplored yet. In the proposed work, the authors have proposed a CNN architecture and investigated the impact of resizing technique on the performance of trained deep learning model and evaluating the uncertainty of classification. The classification performed by the proposed model is a multilabel classification problem with 6 possible labels denoting land coverage. The proposed model is trained using SAT-6 dataset images. The SAT-6 dataset contains images of size \(28 \times 28\), which are resized to \(56 \times 56\) using nearest neighbor interpolation for the training of the model, and testing is done using the aforementioned three interpolation techniques. Finally, the model performance is evaluated in term of specificity, sensitivity, accuracy, and uncertainty of the classification. This paper’s contribution is twofold – first, to put forward a generic model that will help other remote-sensing applications, and second to evaluate the uncertainty of the classification task by varying the image resizing technique.
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Rathee, N., Pahal, S. & Sheoran, D. Evaluating the Uncertainty of Classification Due to Image Resizing Techniques for Satellite Image Classification . MAPAN 36, 243–251 (2021). https://doi.org/10.1007/s12647-021-00456-y
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DOI: https://doi.org/10.1007/s12647-021-00456-y