Multiple-Frame Subpixel Wildfire Tracking

We present a method to improve subpixel signal detection in airborne or orbital image sequences. The proposed technique recognizes stable interest point features in multiple overlapping frames. It estimates motion between consecutive frames and tracks candidate detections over time. The final detection decision combines signal strengths from multiple views to improve sensitivity. The algorithm is computationally tractable for real-time use on autonomous robotic platforms and spacecraft. Additionally, the interest points enable image-relative localization, obviating the need to transmit the entire image and reducing transmission bandwidth requirements by one or more orders of magnitude. This permits higher acquisition rates and potentially improved coverage for remote monitoring. Ground-based systems can reconstruct absolute positions from landmarks without measurements of sensor pose. We demonstrate the algorithm using airborne 4- μm imagery from multiple overpasses of a controlled wildfire.