Unmanned Aerial Vehicles (UAVs) combined with Hyperspectral imaging (HSI) offer potential for environmental and agricultural applications by capturing detailed spectral information that enables the prediction of invisible features like biochemical leaf properties. However, the data-intensive nature of HSI poses challenges for remote devices, which have limited computational resources and storage. This paper introduces an Online Hyperspectral Simple Linear Iterative Clustering algorithm (OHSLIC) framework for real-time tree phenotype segmentation. OHSLIC reduces inherent noise and computational demands through adaptive incremental clustering and a lightweight neural network, which phenotypes trees using leaf contents such as chlorophyll, carotenoids, and anthocyanins. A hyperspectral dataset is created using a custom simulator that incorporates realistic leaf parameters, and light interactions. Results demonstrate that OHSLIC achieves superior regression accuracy and segmentation performance compared to pixel- or window-based methods while significantly reducing inference time. The method`s adaptive clustering enables dynamic trade-offs between computational efficiency and accuracy, paving the way for scalable edge-device deployment in HSI applications.
Classification is an important aspect of hyperspectral images processing and application. At present, the researchers mostly use the classic airborne hyperspectral imagery as the benchmark dataset. However, existing datasets suffer from three bottlenecks: (1) low spatial resolution; (2) low labeled pixels proportion; (3) low degree of subclasses distinction. In this paper, a new benchmark dataset named the Wuhan UAV-borne hyperspectral image (WHU-Hi) dataset was built for hyperspectral image classification. The WHU-Hi dataset with a high spectral resolution (nm level) and a very high spatial r…
A UAV-Based VNIR Hyperspectral Benchmark Dataset for Landmine and UXO Detection
Rochester Institute of Technology, Chester F. Carlson Center for Imaging Science（罗切斯特理工学院，切斯特·F·卡尔森成像科学中心）；Lamont Doherty Earth Observatory, Columbia University（拉蒙特-多赫蒂地球观测站，哥伦比亚大学）；Demining Research Community（排雷研究社区）
A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images
Classification of grapevine varieties using UAV hyperspectral imaging
Department of Computer Science（计算机科学系）；Department of Software Engineering（软件工程系）；Centre for Robotics in Industry（工业机器人中心）；University of Trás-os-Montes e Alto Douro（特拉斯-奥斯-蒙特斯和上杜罗大学）
Mercedes-Benz Research and Development India Pvt. Ltd.（梅赛德斯-奔驰印度私人有限公司研发中心）；Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay（印度理工学院孟买分校资源工程研究中心）
Hyperspectral Imagery (and Remote Sensing in general) captured from UAVs or satellites are highly voluminous in nature due to the large spatial extent and wavelengths captured by them. Since analyzing these images requires a huge amount of computational time and power, various dimensionality reduction techniques have been used for feature reduction. Some popular techniques among these falter when applied to Hyperspectral Imagery due to the famed curse of dimensionality. In this paper, we propose a novel approach, LEt-SNE, which combines graph based algorithms like t-SNE and Laplacian Eigenmaps…
LeafInst - Unified Instance Segmentation Network for Fine-Grained Forestry Leaf Phenotype Analysis: A New UAV based Benchmark
organization= College of Information Science；Technology \& Artificial Intelligence, Nanjing Forestry University , city= Nanjing , postcode= 210037 , state= Jiangsu , country= China；organization= Department of Geography, College of Natural Resources；organization= Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University , city= Nanjing , postcode= 210037 , state= Jiangsu , country= China；organization= School of Architecture \& Urban Planning, Shenzhen University , city= Shenzhen , postcode= 518060 , state= Guangdong , country= China；organization= School of Software, Hefei University of Technology , city= Hefei , postcode= 230601 , state= Anhui , country= China
BCG-Former: Toward Pareto-Efficient Hyperspectral Image Classification via Band-Contextual Gating
College of Information Science, University of Arizona（亚利桑那大学信息科学学院）；Department of Electrical and Computer Engineering, University of Arizona（亚利桑那大学电气与计算机工程系）
Pontifical Catholic University of Rio de Janeiro（里约热内卢天主教大学）；IBM Research（IBM研究院）；McMaster University（麦克马斯特大学）；National Institute for Space Research (INPE)（巴西国家空间研究所）；Santa Catarina State University (UDESC)（圣卡塔琳娜州立大学）
This work proposes a multi-task fully convolutional architecture for tree species mapping in dense forests from sparse and scarce polygon-level annotations using hyperspectral UAV-borne data. Our model implements a partial loss function that enables dense tree semantic labeling outcomes from non-dense training samples, and a distance regression complementary task that enforces tree crown boundary constraints and substantially improves the model performance. Our multi-task architecture uses a shared backbone network that learns common representations for both tasks and two task-specific decoder…
Physics-Guided Deep Unfolding for Blind Cross-Sensor Spectral Super-Resolution via Learning the Spectral Transformation Function
Center for Geo-Spatial Information, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（地理信息中心，深圳先进技术研究院，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen Engineering Laboratory of Ocean Environmental Big Data Analysis and Application（深圳海洋环境大数据分析与应用工程实验室）
Manchester Metropolitan University（曼彻斯特城市大学）；Hebei Agriculture University（河北农业大学）
Late blight disease is one of the most destructive diseases in potato crop, leading to serious yield losses globally. Accurate diagnosis of the disease at early stage is critical for precision disease control and management. Current farm practices in crop disease diagnosis are based on manual visual inspection, which is costly, time consuming, subject to individual bias. Recent advances in imaging sensors (e.g. RGB, multiple spectral and hyperspectral cameras), remote sensing and machine learning offer the opportunity to address this challenge. Particularly, hyperspectral imagery (HSI) combini…
