  In this research, we deal with the problem of visual question answering (VQA)
in remote sensing. While remotely sensed images contain information significant
for the task of identification and object detection, they pose a great
challenge in their processing because of high dimensionality, volume and
redundancy. Furthermore, processing image information jointly with language
features adds additional constraints, such as mapping the corresponding image
and language features. To handle this problem, we propose a cross attention
based approach combined with information maximization. The CNN-LSTM based
cross-attention highlights the information in the image and language modalities
and establishes a connection between the two, while information maximization
learns a low dimensional bottleneck layer, that has all the relevant
information required to carry out the VQA task. We evaluate our method on two
VQA remote sensing datasets of different resolutions. For the high resolution
dataset, we achieve an overall accuracy of 79.11% and 73.87% for the two test
sets while for the low resolution dataset, we achieve an overall accuracy of
85.98%.
