The Influence of Input Image Scale on Deep Learning-Based Beluga Whale Detection from Aerial Remote Sensing Imagery

Muhammed Patel, Xinwei Chen, Linlin Xu, Fernando J. Pena Cantu, Javier Noa Turnes, Neil C. Brubacher, David A. Clausi, Katharine Andrea Scott

Abstract: This paper investigates the influence of input image scale on deep learning-based Beluga whale detection from aerial remote sensing imagery. Beluga whales in the Arctic are jeopardized due to increased coastal activities and climate change. Aerial survey is a common population counting method, and it can be laborious and exhausting to count the number of whales manually. Convolutional neural networks (CNNs) have greatly improved the performance of detecting and counting whales. Since most remote sensing images are very high in resolution, it is a common practice to slice the image into small patches. In this work, we input the full image (after resizing) into an object detection model and compare its performance with the sliding window approach. Experimental results suggest that increasing the input image size helps improve the model’s performance, and the model is able to learn the contextual information.