Papers Hyperspectral Image Segmentation
“Hyperspectral Image Segmentation” 태그가 달린 논문 19편 · 필터 해제
Hyperspectral image segmentation with a machine learning model trained using quantum annealer
Training of machine learning models consumes large amounts of energy. Since the energy consumption becomes a major problem in the development and implementation of artificial intelligence systems there exists a need to i…
Hyperspectral Image SegmentationImage SegmentationSemantic SegmentationMDN: Mamba-Driven Dualstream Network For Medical Hyperspectral Image Segmentation
Medical Hyperspectral Imaging (MHSI) offers potential for computational pathology and precision medicine. However, existing CNN and Transformer struggle to balance segmentation accuracy and speed due to high spatial-spec…
Hyperspectral Image SegmentationImage SegmentationMambaSemantic SegmentationOn-chip Hyperspectral Image Segmentation with Fully Convolutional Networks for Scene Understanding in Autonomous Driving
Most of current computer vision-based advanced driver assistance systems (ADAS) perform detection and tracking of objects quite successfully under regular conditions. However, under adverse weather and changing lighting …
Autonomous DrivingHyperspectral Image SegmentationImage SegmentationInformation Retrieval+2Hyperspectral Imaging-Based Perception in Autonomous Driving Scenarios: Benchmarking Baseline Semantic Segmentation Models
Hyperspectral Imaging (HSI) is known for its advantages over traditional RGB imaging in remote sensing, agriculture, and medicine. Recently, it has gained attention for enhancing Advanced Driving Assistance Systems (ADAS…
Autonomous DrivingHyperspectral Image SegmentationSemantic SegmentationHS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving Scenarios
Semantic segmentation is an essential step for many vision applications in order to understand a scene and the objects within. Recent progress in hyperspectral imaging technology enables the application in driving scenar…
Autonomous DrivingHyperspectral Image SegmentationHyperspectral Semantic SegmentationSegmentation+1Hyperspectral Dataset and Deep Learning methods for Waste from Electric and Electronic Equipment Identification (WEEE)
Hyperspectral imaging, a rapidly evolving field, has witnessed the ascendancy of deep learning techniques, supplanting classical feature extraction and classification methods in various applications. However, many resear…
Hyperspectral Image SegmentationImage SegmentationSegmentationSemantic SegmentationSpectralZoom: Efficient Segmentation with an Adaptive Hyperspectral Camera
Hyperspectral image segmentation is crucial for many fields such as agriculture, remote sensing, biomedical imaging, battlefield sensing and astronomy. However, the challenge of hyper and multi spectral imaging is its la…
AstronomyHyperspectral Image SegmentationImage SegmentationSegmentation+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+1Hyperspectral Image Segmentation: A Preliminary Study on the Oral and Dental Spectral Image Database (ODSI-DB)
Visual discrimination of clinical tissue types remains challenging, with traditional RGB imaging providing limited contrast for such tasks. Hyperspectral imaging (HSI) is a promising technology providing rich spectral in…
Hyperspectral Image SegmentationImage SegmentationSemantic SegmentationExploring 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+3Hyperspectral Image Segmentation based on Graph Processing over Multilayer Networks
Hyperspectral imaging is an important sensing technology with broad applications and impact in areas including environmental science, weather, and geo/space exploration. One important task of hyperspectral image (HSI) pr…
ClusteringHyperspectral Image SegmentationImage SegmentationSegmentation+2Sparse Subspace Clustering Friendly Deep Dictionary Learning for Hyperspectral Image Classification
Subspace clustering techniques have shown promise in hyperspectral image segmentation. The fundamental assumption in subspace clustering is that the samples belonging to different clusters/segments lie in separable subsp…
ClusteringDictionary LearningHyperspectral Image Classificationhyperspectral image clustering+6Image Processing via Multilayer Graph Spectra
Graph signal processing (GSP) has become an important tool in image processing because of its ability to reveal underlying data structures. Many real-life multimedia datasets, however, exhibit heterogeneous structures ac…
Edge DetectionHyperspectral Image SegmentationImage CompressionImage Segmentation+1Validating Hyperspectral Image Segmentation
Hyperspectral satellite imaging attracts enormous research attention in the remote sensing community, hence automated approaches for precise segmentation of such imagery are being rapidly developed. In this letter, we sh…
Hyperspectral Image SegmentationImage SegmentationSegmentationSemantic SegmentationDeep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations
Herein, we present a system for hyperspectral image segmentation that utilizes multiple class--based denoising autoencoders which are efficiently trained. Moreover, we present a novel hyperspectral data augmentation meth…
Data AugmentationDenoisingHyperspectral Image ClassificationHyperspectral Image Segmentation+4Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation
Image segmentation is considered to be one of the critical tasks in hyperspectral remote sensing image processing. Recently, convolutional neural network (CNN) has established itself as a powerful model in segmentation a…
Hyperspectral Image SegmentationImage SegmentationSegmentationSemantic SegmentationOn distances, paths and connections for hyperspectral image segmentation
The present paper introduces the $\eta$ and {\eta} connections in order to add regional information on $\lambda$-flat zones, which only take into account a local information. A top-down approach is considered. First $\la…
Hyperspectral Image SegmentationImage SegmentationSegmentationSemantic SegmentationSegSALSA-STR: A convex formulation to supervised hyperspectral image segmentation using hidden fields and structure tensor regularization
We present a supervised hyperspectral image segmentation algorithm based on a convex formulation of a marginal maximum a posteriori segmentation with hidden fields and structure tensor regularization: Segmentation via th…
Hyperspectral Image SegmentationImage SegmentationSegmentationSemantic Segmentation