A Deep Learning Ensemble Framework for Off-Nadir Geocentric Pose Prediction

• URL: http://arxiv.org/abs/2205.11230v3
• Date: Sat, 6 Aug 2022 08:16:21 GMT
• Title: A Deep Learning Ensemble Framework for Off-Nadir Geocentric Pose Prediction
• Authors: Christopher Sun, Jai Sharma, Milind Maiti
• Abstract summary: Current software functions optimally only on near-nadir images, though off-nadir images are often the first sources of information following a natural disaster. This study proposes a deep learning ensemble framework to predict geocentric pose using 5,923 near-nadir and off-nadir RGB satellite images of cities worldwide.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computational methods to accelerate natural disaster response include change detection, map alignment, and vision-aided navigation. Current software functions optimally only on near-nadir images, though off-nadir images are often the first sources of information following a natural disaster. The use of off-nadir images for the aforementioned tasks requires the computation of geocentric pose, which is an aerial vehicle's spatial orientation with respect to gravity. This study proposes a deep learning ensemble framework to predict geocentric pose using 5,923 near-nadir and off-nadir RGB satellite images of cities worldwide. First, a U-Net Fully Convolutional Neural Network predicts the pixel-wise above-ground elevation mask of the RGB images. Then, the elevation masks are concatenated with the RGB images to form four-channel inputs fed into a second convolutional model, which predicts orientation angle and magnification scale. A performance accuracy of R2=0.917 significantly outperforms previous methodologies. In addition, outlier removal is performed through supervised interpolation, and a sensitivity analysis of elevation masks is conducted to gauge the usefulness of data features, motivating future avenues of feature engineering. The high-accuracy software built in this study contributes to mapping and navigation procedures for effective disaster response to save lives.

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