Papers Hyperspectral image analysis
“Hyperspectral image analysis” 태그가 달린 논문 39편 · 필터 해제
Quantum Enchanced Multi-Scale CNN with Bi-directional Mamba for Crop Field Analysis
Hyperspectral image (HSI) crop analysis is essential for precision agriculture because it captures rich spectral and spatial information for accurate crop monitoring and assessment. However, HSI classification remains ch…
Hyperspectral image analysisCrop ClassificationLooking into a Pixel by Nonlinear Unmixing -- A Generative Approach
Due to the large footprint of pixels in remote sensing imagery, hyperspectral unmixing (HU) has become an important and necessary procedure in hyperspectral image analysis. Traditional HU methods rely on a prior spectral…
Hyperspectral image analysisGeneralized Nonnegative Structured Kruskal Tensor Regression
This paper introduces Generalized Nonnegative Structured Kruskal Tensor Regression (NS-KTR), a novel tensor regression framework that enhances interpretability and performance through mode-specific hybrid regularization …
Hyperspectral image analysisRecent Advances in Diffusion Models for Hyperspectral Image Processing and Analysis: A Review
Hyperspectral image processing and analysis has important application value in remote sensing, agriculture and environmental monitoring, but its high dimensionality, data redundancy and noise interference etc. bring grea…
Anomaly DetectionDenoisingHyperspectral image analysisSpectral Unmixing Comparison with Sparse, Iterative and Mixed Integer Programming Models
Hyperspectral unmixing is the analytical process of determining the pure materials and estimating the proportions of such materials composed within an observed mixed pixel spectrum. We can unmix mixed pixel spectra using…
Hyperspectral image analysisHyperspectral UnmixingregressionHyperspectral Image Spectral-Spatial Feature Extraction via Tensor Principal Component Analysis
This paper addresses the challenge of spectral-spatial feature extraction for hyperspectral image classification by introducing a novel tensor-based framework. The proposed approach incorporates circular convolution into…
Hyperspectral image analysisHyperspectral Image Classificationimage-classificationImage ClassificationTheoretical and Practical Progress in Hyperspectral Pixel Unmixing with Large Spectral Libraries from a Sparse Perspective
Hyperspectral unmixing is the process of determining the presence of individual materials and their respective abundances from an observed pixel spectrum. Unmixing is a fundamental process in hyperspectral image analysis…
Hyperspectral image analysisHyperspectral UnmixingregressionHierarchical Homogeneity-Based Superpixel Segmentation: Application to Hyperspectral Image Analysis
Hyperspectral image (HI) analysis approaches have recently become increasingly complex and sophisticated. Recently, the combination of spectral-spatial information and superpixel techniques have addressed some hyperspect…
Hyperspectral image analysisSuperpixelsReal-Time Compressed Sensing for Joint Hyperspectral Image Transmission and Restoration for CubeSat
This paper addresses the challenges associated with hyperspectral image (HSI) reconstruction from miniaturized satellites, which often suffer from stripe effects and are computationally resource-limited. We propose a Rea…
compressed sensingDecoderHyperspectral image analysisRed Teaming Models for Hyperspectral Image Analysis Using Explainable AI
Remote sensing (RS) applications in the space domain demand machine learning (ML) models that are reliable, robust, and quality-assured, making red teaming a vital approach for identifying and exposing potential flaws an…
Hyperspectral image analysisHYPERVIEW ChallengeRed TeamingHyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques
Hyperspectral imaging provides precise classification for land use and cover due to its exceptional spectral resolution. However, the challenges of high dimensionality and limited spatial resolution hinder its effectiven…
Dimensionality ReductionHyperspectral image analysisKnowledge DistillationSelf-Supervised LearningHyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis
The exceptional spectral resolution of hyperspectral imagery enables material insights that are not possible with RGB or multispectral images. Yet, the full potential of this data is often underutilized by deep learning …
Contrastive LearningHyperspectral image analysisHyperspectral Image Classificationimage-classification+2HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding
The dissection of hyperspectral images into intrinsic components through hyperspectral intrinsic image decomposition (HIID) enhances the interpretability of hyperspectral data, providing a foundation for more accurate cl…
ClassificationHyperspectral image analysisHyperspectral Image Classificationimage-classification+2HySpecNet-11k: A Large-Scale Hyperspectral Dataset for Benchmarking Learning-Based Hyperspectral Image Compression Methods
The development of learning-based hyperspectral image compression methods has recently attracted great attention in remote sensing. Such methods require a high number of hyperspectral images to be used during training to…
BenchmarkingHyperspectral image analysisImage CompressionDeep Learning Techniques for Hyperspectral Image Analysis in Agriculture: A Review
In the recent years, hyperspectral imaging (HSI) has gained considerably popularity among computer vision researchers for its potential in solving remote sensing problems, especially in agriculture field. However, HSI cl…
Deep LearningHyperspectral image analysisPositionHyperspectral Image Analysis with Subspace Learning-based One-Class Classification
Hyperspectral image (HSI) classification is an important task in many applications, such as environmental monitoring, medical imaging, and land use/land cover (LULC) classification. Due to the significant amount of spect…
ClassificationDimensionality Reductionfeature selectionHyperspectral image analysis+1Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning with Distance Covariance Representation for Hyperspectral Image Classification
For the abundant spectral and spatial information recorded in hyperspectral images (HSIs), fully exploring spectral-spatial relationships has attracted widespread attention in hyperspectral image classification (HSIC) co…
Hyperspectral image analysisHyperspectral Image ClassificationHyperspectral Image Segmentationimage-classification+1Measuring complex refractive index through deeplearning-enabled optical reflectometry
Optical spectroscopy is indispensable for research and development in nanoscience and nanotechnology, microelectronics, energy, and advanced manufacturing. Advanced optical spectroscopy tools often require both specifica…
Hyperspectral image analysisExploring the Relationship between Center and Neighborhoods: Central Vector oriented Self-Similarity Network for Hyperspectral Image Classification
To mine the spectral-spatial information of target pixel in hyperspectral image classification (HSIC), convolutional neural network (CNN)-based models widely adopt patch-based input pattern, where a patch represents its …
Hyperspectral image analysisHyperspectral Image ClassificationHyperspectral Image Segmentationimage-classificationA distribution-dependent Mumford-Shah model for unsupervised hyperspectral image segmentation
Hyperspectral images provide a rich representation of the underlying spectrum for each pixel, allowing for a pixel-wise classification/segmentation into different classes. As the acquisition of labeled training data is v…
DenoisingDimensionality ReductionHyperspectral image analysisHyperspectral Image Segmentation+3