{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9854221","patent":{"patent_number":"US-9854221","title":"Hyperspectral imaging devices using hybrid vector and tensor processing","assignee":null,"inventors":[],"filing_date":"2014-09-26T00:00:00.000Z","publication_date":"2017-12-26T00:00:00.000Z","cpc_codes":["H04N","H04N","H04N","H04N","H04N","H04N"],"num_claims":16,"abstract":"Methods and systems obtain data representative of a scene across spectral bands using a compressive-sensing-based hyperspectral imaging system comprising optical elements. These methods and systems sample two modes of a three-dimensional tensor corresponding to a hyperspectral representation of the scene using sampling matrices, one for each of the two modes, to generate a modified three-dimensional tensor. After sampling the two modes, such methods and systems sample a third mode of the modified three-dimensional tensor using a third sampling matrix to generate a further modified three-dimensional tensor. Then, the methods and systems reconstruct hyperspectral data from the further modified three-dimensional tensor using the sampling matrices and the third sampling matrix."},"analysis":{"summary":"The Hyperspectral Imaging Devices Using Hybrid Vector and Tensor Processing patent introduces a system that efficiently acquires and reconstructs hyperspectral data using compressive sensing and hybrid vector/tensor processing. The problem being solved is the high data volume and computational burden associated with traditional hyperspectral imaging, which limits its practicality in real-world applications. The key technical approach involves using sampling matrices to selectively acquire data across spectral bands and then employing tensor processing to reconstruct the complete hyperspectral data from the sampled information. This reduces the amount of data that needs to be processed, leading to faster analysis and reduced energy consumption. The business value and applications are broad, including improved crop monitoring in agriculture, non-invasive disease detection in medical diagnostics, and more accurate environmental monitoring. The market opportunity is significant, as the enhanced efficiency and accuracy of this technology can drive its adoption in various sectors, enabling new applications and improved performance in existing ones. This innovation represents a significant step forward in hyperspectral imaging technology, paving the way for new applications and improved performance in existing ones by strategically sampling and efficiently reconstructing hyperspectral data.","layman_explanation":"The Hyperspectral Imaging Devices Using Hybrid Vector and Tensor Processing patent addresses a significant challenge in the field of hyperspectral imaging: the massive amount of data generated and the time it takes to process it. Hyperspectral imaging is like taking pictures with a camera that sees hundreds of colors instead of just the three (red, green, and blue) that our eyes see. This allows us to analyze materials and objects in much greater detail, identifying things like the health of plants, the composition of chemicals, or even hidden objects. However, because it captures so much information, the resulting data is huge and takes a long time to analyze.\n\nThe invention solves this problem by using a technique called \"compressive sensing.\" Imagine you want to know what a field of crops looks like. Instead of taking a picture of every single plant, you only take pictures of a few carefully chosen plants that represent the whole field. Compressive sensing does something similar: it selectively samples the data, capturing only the most important information. This significantly reduces the amount of data that needs to be processed.\n\nOnce the data is sampled, the invention uses \"tensor processing\" to reconstruct the full image. Tensors are like multi-dimensional arrays, and tensor processing is a way of efficiently manipulating and analyzing these arrays. By using tensor processing, the invention can quickly and accurately reconstruct the full hyperspectral image from the sampled data. This is much faster and more efficient than traditional methods of processing hyperspectral data.\n\nThis matters because it makes hyperspectral imaging much more practical and accessible. It opens up new opportunities in various industries, such as agriculture, where farmers can use it to monitor the health of their crops in real-time; medicine, where doctors can use it to diagnose diseases non-invasively; and environmental monitoring, where scientists can use it to track pollution levels. By making hyperspectral imaging faster and more efficient, this invention can help us solve some of the world's most pressing problems. The reduced processing time and data volume translate to cost savings and faster decision-making, making it a valuable tool for businesses and organizations across various sectors. In essence, this innovation makes a powerful technology more usable and impactful.","technical_analysis":"The Hyperspectral Imaging Devices Using Hybrid Vector and Tensor Processing patent details a system that leverages compressive sensing and hybrid vector/tensor processing to optimize hyperspectral data acquisition and reconstruction. The system's architecture comprises optical elements for initial data capture, a compressive sensing module employing sampling matrices for selective data acquisition across spectral bands, and a tensor processing module that reconstructs the hyperspectral data. Implementation hinges on the design of efficient sampling matrices and optimized tensor processing algorithms. These algorithms are crucial for minimizing computational load while maintaining accuracy. The system's integration patterns include compatibility with various hyperspectral sensors and remote sensing platforms such as satellites and drones. Performance is characterized by reduced data volume, faster processing times, and enhanced spectral resolution. Code-level considerations involve advanced programming skills for implementing the compressive sensing and tensor processing algorithms, emphasizing numerical methods and hardware optimization. The core algorithm leverages the inherent sparsity in hyperspectral data, using compressive sensing to acquire a reduced set of measurements. Tensor decomposition methods are then applied to reconstruct the full hyperspectral cube from these measurements. The choice of tensor decomposition method (e.g., CANDECOMP/PARAFAC, Tucker decomposition) impacts both performance and accuracy. The system's performance is influenced by factors such as the sparsity level of the data, the choice of sampling matrices, and the efficiency of the tensor decomposition algorithm. The implications for developers and engineers involve expertise in signal processing, numerical analysis, and high-performance computing. The system's architecture and algorithms must be carefully optimized to achieve the desired performance characteristics. Code-level optimization is crucial for maximizing the efficiency of the tensor processing module. The system's integration patterns also require careful consideration, ensuring compatibility with various hyperspectral sensors and remote sensing platforms.","business_analysis":"The Hyperspectral Imaging Devices Using Hybrid Vector and Tensor Processing patent holds significant business implications across multiple sectors. The market opportunity size is substantial, driven by the increasing demand for hyperspectral imaging in agriculture, medical diagnostics, environmental monitoring, and defense. The competitive advantages stem from the technology's ability to reduce data volume and processing time, leading to faster and more efficient analysis. Revenue potential is high, with opportunities for licensing the technology, developing integrated hyperspectral imaging systems, and offering data analysis services. The business models include licensing the patented technology to sensor manufacturers, providing turnkey hyperspectral imaging solutions, and offering cloud-based data analysis services. Strategic positioning involves targeting industries where the benefits of faster and more efficient hyperspectral imaging are most pronounced. The competitive landscape includes companies offering traditional hyperspectral imaging systems, as well as those developing alternative data compression techniques. ROI projections are favorable, driven by the potential for increased efficiency, reduced costs, and new revenue streams. Key business opportunities include: Precision Agriculture: Enabling farmers to optimize irrigation, fertilization, and pest control strategies. Medical Diagnostics: Facilitating non-invasive disease detection and monitoring. Environmental Monitoring: Improving the accuracy and efficiency of pollution detection and ecosystem health assessments. Defense and Security: Enhancing surveillance capabilities and threat detection. The technology's ability to acquire and process hyperspectral data more efficiently provides a significant competitive advantage, enabling businesses to offer more cost-effective and timely solutions. The market for hyperspectral imaging is expected to grow significantly in the coming years, driven by the increasing demand for data-driven decision-making across various industries.","faqs":null,"topics":["hyperspectral imaging","compressive sensing","tensor processing","spectral data","remote sensing","hyperspectral","imaging","presents"],"tech_cluster":null},"seo":{"title":"Hyperspectral Imaging - Hybrid Vector & Tensor Patent","description":"Explore the Hyperspectral Imaging Devices Using Hybrid Vector and Tensor Processing patent for efficient data acquisition. Analysis, claims, and market impact.","keywords":["hyperspectral imaging","compressive sensing","tensor processing","spectral data","remote sensing","patent","patent US-9854221"]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9854221","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-9854221","citation_suggestion":"Patentable. \"Hyperspectral imaging devices using hybrid vector and tensor processing\" (US-9854221). https://patentable.app/patents/US-9854221","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9854221","json":"https://patentable.app/api/llm-context/US-9854221","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-31T07:43:05.941Z"}