Spectral camera system for multi-domain earth observation using FPGA-enabled onboard intelligence

Abstract

The present invention relates to the field of remote sensing and Earth observation, particularly a satellite-based artificial intelligence (AI) system with high-resolution spectral imaging capabilities. It enables domain-specific anomaly detection for sectors such as agriculture, mining, and environmental monitoring by utilizing onboard reconfigurable hardware (FPGA) for real-time spectral band optimization, image classification, noise filtering, anomaly scoring, and advanced compression algorithms. The system integrates deep learning models with edge inference capabilities, offering a significant reduction in data transmission requirements while increasing the relevance and precision of Earth observation outputs.

Description

BRIEF DESCRIPTION This invention provides an intelligent FPGA-integrated satellite payload onboard the U12 satellite platform. A high-resolution spectral camera is combined with onboard AI models to:1. Detect domain-specific anomalies.2. Select the most relevant spectral bands using real-time inference.3. Apply adaptive region-of-interest (ROI) detection to focus compression.4. Reduce the data payload using AI-based spectral encoding techniques. The system comprises:1.      Neural encoder-decoder architectures for compression.2.      Hybrid CNN-LSTM models for temporal anomaly detection.3.      Reinforcement learning-based band selector agents.4.      FPGA acceleration for low-latency model execution. DETAILED DESCRIPTION 1.       Spectral Camera Array   Spectral Range400–2500 nm (VIS to SWIR),extended to 8–14 µm (TIR) with microbolometer integrationResolution5–10 m / pixeldepending on orbital altitude and lens configurationFrame RateUp to 60 Hzwith temporal binning and band gating optionsDetectors HgCdTe for SWIR, InGaAs for NIR, and VOx for TIR imaging 2. Onboard AI System (FPGA-accelerated)2.1 Inference HardwareRadiation-tolerant FPGA integrated with AI-dedicated cores (e.g., Xilinx Kintex Ultrascale or AMD Versal with DPU). 2.2 FPGA Architecture:·         Configured with parallel pipelined data paths for real-time spectral stream ingestion.·         Hardware-accelerated CNN convolution kernels mapped to DSP slices.·         Dedicated BRAM used for LSTM state storage to enable low-latency recurrent inference.·         FSM-based controller for routing data through detection, classification, and compression stages. 2.3 Model Suite:2.3.1 CNN3-layer convolution stack with 5x5 kernels and ReLU activation; max-pooling used for feature reduction; designed to extract texture, pattern, and morphology from small tiles (32x32 px).2.3.2 LSTMDual-layer 128-unit gated recurrent unit for capturing temporal patterns in sequential frames; key for identifying trends in vegetation or thermal anomalies.2.3.3 AutoencoderShallow 3-layer symmetric encoder-decoder structure trained on sparse spectra; latent space used for entropy-efficient compression.2.3.4 Policy Gradient RL AgentState space includes scene class, sun angle, orbital velocity; action space is spectral band selection and compression setting.2.3.5 Transformer-based DetectorMulti-head attention model processes reduced feature vectors to evaluate out-of-distribution signatures or novel patterns. Unlike traditional AI ground pipelines, our FPGA-enabled edge inference system performs all feature extraction and classification in orbit. This significantly reduces latency and avoids excessive transmission of irrelevant or noisy spectral data. Moreover, FPGA configurability allows in-mission reprogramming, enabling model upgrades or retraining based on mission objectives. 3. Spectral Band Selection LogicReal-time decision pipeline:a) Scene segmentation using FPGA-accelerated CNN-LSTM combo.b) Scene classification using tree ensemble models embedded via lookup table approximation.c) Band selection via Q-learning or policy-gradient RL maximizing anomaly probability.d) FPGA controls rotating filter wheel or MEMS-tunable filters based on selection.  4. Compression PipelineMethods:a) Principal Component Analysis (PCA) for orthogonal subspace projection.b) Custom-trained spectral autoencoders using sparse training with L1 regularization.c) Standard-compliant CCSDS 123.0-B-2 entropy codec as fallback for lossless mode.Data Reduction:a) Empirically 8–12x in vegetation zones and 5–7x in mining zones.b) Enhanced by context-aware masking of non-informative zones via saliency maps. 5. Communication Modulea) Bandwidth ControlAI-prioritized packet scheduling using reinforcement-learned transmission policy.b) Data LayerEmbedded spectral metadata with confidence intervals and anomaly classification tags.c) Link LayerAuto-fallback to lower rate transmission on link degradation while preserving ROI cores. 6. Ground Station Integrationa) AI-based alignment engine that matches incoming images with Sentinel-2 and Landsat via structural similarity index (SSIM) and cross-correlation of NDVI layers.b) Spectral fusion via Bayesian inference models and U-Net style spatial upsampling.c) Platform delivers predictive overlays with user-defined thresholds for abnormality alerting. 

Claims

Claim 1. A system for anomaly-targeted Earth observation from orbit, comprising: · A spectral camera array configured to capture images in VIS, NIR, SWIR, and TIR bands; · An onboard FPGA processor executing AI models including CNN, LSTM, Transformer, and RL agents; · A real-time band selector configured by a policy-gradient reinforcement learning model; · An adaptive compression module including autoencoder and wavelet-based encoders; · A communication module configured to transmit compressed, classified images to Earth. Claim 2. The system of claim 1, wherein the FPGA executes spectral segmentation and anomaly detection using a hybrid CNN-LSTM architecture, with convolutional filters and recurrent memory blocks mapped to dedicated hardware paths. Claim 3. The system of claim 1, wherein spectral band selection is dynamically reconfigured by an RL agent trained with satellite state variables and classification confidence as input features. Claim 4. The system of claim 1, wherein the compression module includes a neural encoder-decoder trained with L1-sparse loss to reduce data volume while preserving informative spectral variation. Claim 5. A method for intelligent spectral image acquisition in orbit, comprising: a) Capturing multispectral image data using a spectral camera onboard a satellite; b) Processing the image data using FPGA-executed AI models including CNN, LSTM, and Transformer; c) Selecting relevant spectral bands using a reinforcement learning-based agent; d) Compressing the selected bands using autoencoders or wavelet transforms; e) Transmitting the compressed images to a ground station with priority tagging. Claim 6. The method of claim 5, further comprising dynamically reprogramming the FPGA to update AI inference models during the satellite's operational lifetime. Claim 7. The method of claim 5, wherein the neural models are trained on vegetation indices, mineralogical spectral signatures, and temporal anomaly series and fine-tuned with synthetic orbital simulation data. Claim 8. The method of claim 5, wherein transmission priority is assigned by AI-driven confidence scores, entropy maps, and policy-gradient optimized decision logic.