Deep learning based whale detection from satellite imagery

Abstract

In spite of their fascinating and vulnerable status, the number of whales in the world's oceans remain debatable because of their living environment. As per statics, with the efforts made towards their conservation, there have been signs of recovery in the population of whales but still, many remote areas remain unsurveyed and thus the number of whales remain ambiguous. To maintain the balance of ecology it is needed to preserve the whales to balance the ecosystem and a key component of that will be to have a count of whales.To date, whale detection has been carried out using ship/aerial surveys, acoustic surveys, etc. but with the development in the field of Very High Resolution (VHR) satellite imagery, satellite images along with deep learning is also being effectively used for the detection of whales.Most of the previous work carried out for the detection of whales via satellite imagery is based upon the manual detection of whales by observers or scientists and that suffers from a high risk of false and inappropriate detections. There is also limited availability of satellite data in this field and there is a lack of usage of Artificial Intelligence for the automatic detection of whales.In this work we propose, a novel deep learning-based automatic whale detection approach from VHR satellite feed. We created a dataset of satellite images having evident chances of whales or probable whales by combining images from two different sources, used Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) to improve the quality of the images and maintain the natural texture of the images, augmented the captured satellite images to train the model, labelled the dataset as whale and probable whale. Finally, a whale dataset of 6000 whale satellite images is created which is the largest dataset available in this field. A deep learning-based whale detection approach is proposed to detect the existence of whales in the satellite image. The proposed approach performed better in terms of precision, recall, and mAP (Mean Average Precision). The precision achieved by the proposed system is 0.95, recall is 0.85, and mAP is 0.74 which is better as compared to the state-of-the-art work in this field.