Compress-Align-Detect: onboard change detection from unregistered images
Gabriele Inzerillo, Diego Valsesia, Aniello Fiengo, Enrico Magli

TL;DR
This paper introduces an onboard satellite change detection framework using a deep neural network that compresses, registers, and detects changes in images in real-time, enabling faster response times for satellite monitoring.
Contribution
It presents the first end-to-end deep learning framework combining image compression, lightweight registration, and change detection for onboard satellite processing under strict resource constraints.
Findings
Achieves high F1 scores at various compression rates.
Operates at 0.7 Mpixel/sec on a 15W hardware.
Outperforms current state-of-the-art in each submodule.
Abstract
Change detection from satellite images typically incurs a delay ranging from several hours up to days because of latency in downlinking the acquired images and generating orthorectified image products at the ground stations; this may preclude real- or near real-time applications. To overcome this limitation, we propose shifting the entire change detection workflow onboard satellites. This requires to simultaneously solve challenges in data storage, image registration and change detection with a strict complexity constraint. In this paper, we present a novel and efficient framework for onboard change detection that addresses the aforementioned challenges in an end-to-end fashion with a deep neural network composed of three interlinked submodules: (1) image compression, tailored to minimize onboard data storage resources; (2) lightweight co-registration of non-orthorectified…
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