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 III: Image Captioning Performance on the UCM-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 analyzing the table, one can see that the VGG model at the depth of 16 layers (VGG16) provides the highest scores under all metrics except CIDEr. As an example, the VGG16 provides more than 5% higher BLEU-1, more than 8% higher BLEU-4 and more than 7% higher ROUGE-L scores compared to the InceptionV3. However, only under CIDEr metric, the VGG16 leads to less than 2% lower score compared to the highest score obtained by the GoogleNet model. In detail, the InceptionV3 provides the lowest scores under all metrics. As an example, it provides more than 5% lower BLEU-1 and almost 6 lower ROUGE-L scores compared to the DenseNet169. These results show that almost all CNN models (except the InceptionV3) achieve similar scores on the UCM-Captions dataset. This supports our conclusion on the Sydney-Captions dataset. In greater details, increasing the depths of the ResNet and DenseNet models up to some extent achieves slightly higher metric scores compared to those at the lowest depth. However, further increasing their depths do not provide the highest scores. As an example, the ResNet model at the depth of 152 leads to lowest score under most of the metrics compared to the other ResNet CNNs.