Convolutional Neural Networks with Deep Supervised Feature Learning for Remote Sensing Scene Classification

State-of-the-art remote sensing scene classification methods employ different Convolutional 1 Neural Network architectures for achieving very high classification performance. A trait shared 2 by the majority of these methods is that the class associated with each example is ascertained by 3 examining the activations of the last fully connected layer, and the networks are trained to minimize 4 the cross-entropy between predictions extracted from this layer and ground-truth annotations. In 5 this work, we extend this paradigm by introducing an additional output branch which maps the 6 inputs to low dimensional representations, effectively extracting additional feature representations 7 of the inputs. The proposed model imposes additional distance constrains on these representations 8 with respect to identified class representatives, in addition to the traditional categorical cross-entropy 9 between predictions and ground-truth. By extending the typical cross-entropy loss function with 10 a distance learning function, our proposed approach achieves significant gains across a wide set of 11 benchmark datasets in terms of classification, while providing additional evidence related to class 12 membership and classification confidence. 13