Scene semantic classification based on scale invariance convolutional neural networks

Convolutional neural networks (CNNs) has been introduced into remote sensing scene classification, achieving outstanding performance. However, the scale change of objects contained in remote sensing scene image make it difficult to extract feature robust to scale, limiting the further improvement of classification accuracy. In this paper, a scene classification method named Scale Invariance Convolutional Neural Networks (SICNNs) is proposed for remote sensing scene classification. In the proposed method, two images with different scales generated by randomly stretching one image are fed into CNNs simultaneously for training at intervals of several iterations. Then a similarity measure layer was added in SICNN to make the distance of the two feature vectors extracted from the two images as close as possible, leading extracted feature to be robust to scale. Experimental results using two datasets, i.e. the UC Merced dataset, Google dataset of SIRI-WHU, demonstrated the effectiveness of the proposed method.