A DEEP LEARNING APPROACH TO THE CLASSIFICATION OF SUB-DECIMETRE RESOLUTION AERIAL IMAGES

Spatial-contextual features play a vital role in the classification of very high resolution aerial images characterized by sub-decimetre resolution. However, manually extracting relevant contextual features is difficult and time-consuming in the analysis of sub-decimetre resolution images, where the objects of interest are significantly larger than the pixel size. Deep learning methods allow us to replace hand-crafted features by automatically learning contextual features from the image. In this paper, we investigate the use of convolutional neural networks (CNN) for the classification of urban areas using high resolution airborne images. We also analyse the sensitivity of network hyperparameters providing an interpretation of their effect on the extraction of spatial-contextual features. Experimental results show the effectiveness of CNN in learning discriminative contextual features leading to accurate classified maps and outperforming traditional classification methods based on the extraction of textural features.