Remote Sensing Image Classification via Improved Cross-Entropy Loss and Transfer Learning Strategy Based on Deep Convolutional Neural Networks

Recently, deep convolutional neural networks (DCNNs) have gained great success in classifying aerial images, but in this area, the existence of the hard images, due to their innate characteristics, and weak focus of the network on them, due to the use of the cross-entropy (CE) loss, lead to reducing the accuracy of classification of aerial images. Moreover, since the last convolutional layer in a CNN has highly class-specific information, giving equal importance to all the channels causes to extract less discriminative features in comparison to weighting each of the channels adaptively. The fact that data labeling as well as creating ground truth on large data set is expensive is another point of concern in this regard. To address these problems, we have proposed a novel method for classification of aerial images. Our method includes proposing a new loss function, which enhances the focus of the network on hard examples by adding a new term to CE as a penalty term, bringing about the state-of-the-art results; designing a new multilayer perceptron (MLP) as a classifier, in which the used attention mechanism extracts more discriminative features by weighting each of the channels adaptively; and applying transfer learning strategy by adopting neural architecture search network mobile (NASNet Mobile) as a feature descriptor for the first time in the field of aerial images, which can mitigate the aforementioned costs. As indicated in the results, our proposed method outperforms the existing baseline methods and achieves state-of-the-art results on all three data sets.