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 II: Image Captioning Performance on the Sydney-Captions 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 assessing the table, one can observe that the ResNet model at the depth of 101 layers leads to the highest scores under all metrics compared to the other CNNs. As an example, the ResNet101 provides almost 5% higher BLEU-1, more than 6% higher BLEU-2, almost 8% higher BLEU-3, more than 9% higher BLEU-4 and almost 3% higher ROUGE-L scores compared to the GoogleNet model. In detail, most of the CNN models (except ResNet101) achieve similar scores on the Sydney-Captions dataset under all metrics regardless of their depth. As an example, the VGG model at the lowest depth in considered CNNs (VGG16) provides less than 1% higher BLEU-1 and almost same BLEU-4 scores compared to the DenseNet model at the highest depth among all CNNs (DenseNet201).