Hyper-Spectral Imaging (HSI) is a crucial technique for analysing remote sensing data acquired from Earth observation satellites. The rich spatial and spectral information obtained through HSI allows for better characterisation and exploration of the Earth's surface over traditional techniques like RGB and Multi-Spectral imaging on the downlinked image data at ground stations. Sometimes, these images do not contain meaningful information due to the presence of clouds or other artefacts, limiting their usefulness. Transmission of such artefact HSI images leads to wasteful use of already scarce energy and time costs required for communication. While detecting such artefacts before transmitting the HSI image is desirable, the computational complexity of these algorithms and the limited power budget on satellites (especially CubeSats) are key constraints. This paper presents an unsupervised learning-based convolutional autoencoder (CAE) model for artefact identification of acquired HSI images at the satellite and a deployment architecture on AMD's Zynq Ultrascale FPGAs. The model is trained and tested on widely used HSI image datasets: Indian Pines, Salinas Valley, the University of Pavia and the Kennedy Space Center. For deployment, the model is quantised to 8-bit precision, fine-tuned using the Vitis-AI framework and integrated as a subordinate accelerator using AMD's Deep-Learning Processing Units (DPU) instance on the Zynq device. Our tests show that the model can process each spectral band in an HSI image in 4 ms, 2.6x better than INT8 inference on Nvidia's Jetson platform & 1.27x better than SOTA artefact detectors. Our model also achieves an f1-score of 92.8% and FPR of 0% across the dataset, while consuming 21.52 mJ per HSI image, 3.6x better than INT8 Jetson inference & 7.5x better than SOTA artefact detectors, making it a viable architecture for deployment in CubeSats.

翻译：高光谱成像（HSI）是分析地球观测卫星获取的遥感数据的关键技术。相比地面站接收的传统RGB和多光谱成像技术，HSI获取的丰富空间与光谱信息能更有效地表征和探测地表特征。然而，由于云层或其他伪影的存在，部分影像可能不包含有效信息，从而限制了其应用价值。传输此类含伪影的HSSI影像会导致本已稀缺的通信能源与时间资源的浪费。虽然在传输前检测HSI影像中的伪影具有重要价值，但相关算法的计算复杂度与卫星（特别是立方星）有限的功耗预算构成了主要制约。本文提出一种基于无监督学习的卷积自编码器（CAE）模型，用于在卫星端对获取的HSI影像进行伪影识别，并基于AMD Zynq Ultrascale FPGA设计部署架构。该模型在广泛使用的HSI影像数据集（印第安纳松林、萨利纳斯谷地、帕维亚大学及肯尼迪航天中心数据集）上完成训练与测试。部署时，模型被量化为8位精度，通过Vitis-AI框架进行微调，并作为从属加速器集成到Zynq设备的AMD深度学习处理单元（DPU）实例中。实验表明，该模型处理HSI影像中每个光谱波段仅需4毫秒，比Nvidia Jetson平台的INT8推理快2.6倍，比现有最优（SOTA）伪影检测器快1.27倍。在全部数据集上，模型取得了92.8%的F1分数与0%的误报率，同时每幅HSI影像仅消耗21.52 mJ能量，能效比INT8 Jetson推理提升3.6倍，比SOTA伪影检测器提升7.5倍，证明该架构适合在立方星中部署。