MULTI-LABEL CLASSIFICATION OF REMOTE SENSING IMAGERY WITH DEEP NEURAL NETWORKS

Multi-label classification problem aims to assign multiple class labels to the remote sensing image under analysis, which is more challenging compared to single-label classification. To this end, we propose a neural model based on multiple loss functions. The first loss seeks to increase the similarity between the image with its corresponding labels using a similarity layer. The second one is related to label discrimination, and it is achieved using a modified softmax layer suitable for multi-label classification. The third loss aims to detect automatically the number of labels present in the image through a regression layer. Experimental results on the well known Merced data are reported and discussed.