SD-RSIC: Summarization DrivenDeep Remote Sensing Image CaptioningThis work is funded by the European Research Council (ERC) through the ERC-2017-STG BigEarth Project under Grant 759764.
TABLE IV: Image Captioning Performance on the RSCID Dataset When Using Different CNN Models for the Proposed SD-RSIC Approach
['8pt.', 'BLEU-1', 'BLEU-2', 'BLEU-3', 'BLEU-4', 'METEOR', 'ROUGE-L', 'CIDEr']
By analyzing the table, one can observe that the ResNet model at the depth of 152 layers provides the highest scores under most of the metrics compared to the other CNNs. As an example, the ResNet152 achieves more than 2% higher BLEU-3 and BLUE-4 scores and almost 8% higher CIDEr score compared to the InceptionV3. It also achieves almost the same BLEU-1 and METEOR scores with the VGG19 and the ResNet50, which provide the highest score in BLEU-1 and METEOR metrics, respectively. In detail, the VGG model (which has the shallowest CNNs compared to the others) leads to higher scores under most of the metrics compared to the DenseNet model. As an example, the VGG model at the depth of 19 layers achieves more than 2% BLEU-1 and CIDEr scores compared to the DenseNet201, which has the highest depth in considered CNNs. These results show that accuracies obtained by most of the CNNs are, again, similar to each other.