Papers Classification Of Hyperspectral Images
“Classification Of Hyperspectral Images” 태그가 달린 논문 27편 · 필터 해제
Spectral-Spatial Self-Supervised Learning for Few-Shot Hyperspectral Image Classification
Few-shot classification of hyperspectral images (HSI) faces the challenge of scarce labeled samples. Self-Supervised learning (SSL) and Few-Shot Learning (FSL) offer promising avenues to address this issue. However, exis…
Classification Of Hyperspectral ImagesDiversityFew-Shot LearningHyperspectral Image Classification+3Spectral-Spatial Transformer with Active Transfer Learning for Hyperspectral Image Classification
The classification of hyperspectral images (HSI) is a challenging task due to the high spectral dimensionality and limited labeled data typically available for training. In this study, we propose a novel multi-stage acti…
Active LearningClassification Of Hyperspectral ImagesDiversityHyperspectral Image Classification+3Hyperspectral Imaging-Based Grain Quality Assessment With Limited Labelled Data
Recently hyperspectral imaging (HSI)-based grain quality assessment has gained research attention. However, unlike other imaging modalities, HSI data lacks sufficient labelled samples required to effectively train deep c…
Classification Of Hyperspectral ImagesFew-Shot LearningRandomized Principal Component Analysis for Hyperspectral Image Classification
The high-dimensional feature space of the hyperspectral imagery poses major challenges to the processing and analysis of the hyperspectral data sets. In such a case, dimensionality reduction is necessary to decrease the …
ClassificationClassification Of Hyperspectral ImagesDimensionality ReductionHyperspectral Image Classification+2Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations
Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the…
Classification Of Hyperspectral ImagesMedical DiagnosisA comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images
Hyperspectral imaging (HSI) is a non-destructive and contactless technology that provides valuable information about the structure and composition of an object. It can capture detailed information about the chemical and …
Classification Of Hyperspectral ImagesA new filter for dimensionality reduction and classification of hyperspectral images using GLCM features and mutual information
Dimensionality reduction is an important preprocessing step of the hyperspectral images classification (HSI), it is inevitable task. Some methods use feature selection or extraction algorithms based on spectral and spati…
ClassificationClassification Of Hyperspectral ImagesDimensionality Reductionfeature selectionHybridization of filter and wrapper approaches for the dimensionality reduction and classification of hyperspectral images
The high dimensionality of hyperspectral images often imposes a heavy computational burden for image processing. Therefore, dimensionality reduction is often an essential step in order to remove the irrelevant, noisy and…
Classification Of Hyperspectral ImagesDimensionality ReductionA Novel Filter Approach for Band Selection and Classification of Hyperspectral Remotely Sensed Images Using Normalized Mutual Information and Support Vector Machines
Band selection is a great challenging task in the classification of hyperspectral remotely sensed images HSI. This is resulting from its high spectral resolution, the many class outputs and the limited number of training…
ClassificationClassification Of Hyperspectral ImagesDimensionality ReductionA novel filter based on three variables mutual information for dimensionality reduction and classification of hyperspectral images
The high dimensionality of hyperspectral images (HSI) that contains more than hundred bands (images) for the same region called Ground Truth Map, often imposes a heavy computational burden for image processing and compli…
ClassificationClassification Of Hyperspectral ImagesDimensionality ReductionA novel information gain-based approach for classification and dimensionality reduction of hyperspectral images
Recently, the hyperspectral sensors have improved our ability to monitor the earth surface with high spectral resolution. However, the high dimensionality of spectral data brings challenges for the image processing. Cons…
ClassificationClassification Of Hyperspectral ImagesDimensionality ReductionA Novel Approach for Dimensionality Reduction and Classification of Hyperspectral Images based on Normalized Synergy
During the last decade, hyperspectral images have attracted increasing interest from researchers worldwide. They provide more detailed information about an observed area and allow an accurate target detection and precise…
Classification Of Hyperspectral ImagesDimensionality ReductionNew wrapper method based on normalized mutual information for dimension reduction and classification of hyperspectral images
Feature selection is one of the most important problems in hyperspectral images classification. It consists to choose the most informative bands from the entire set of input datasets and discard the noisy, redundant and …
ClassificationClassification Of Hyperspectral ImagesDimensionality Reductionfeature selectionAn Algorithm and Heuristic based on Normalized Mutual Information for Dimensionality Reduction and Classification of Hyperspectral images
In the feature classification domain, the choice of data affects widely the results. The Hyperspectral image (HSI), is a set of more than a hundred bidirectional measures (called bands), of the same region (called ground…
ClassificationClassification Of Hyperspectral ImagesDimensionality Reductionfeature selectionGenerative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification
Nowadays, HSI classification can reach a high classification accuracy when given sufficient labeled samples as training set. However, the performances of existing methods decrease sharply when trained on few labeled samp…
ClassificationClassification Of Hyperspectral ImagesFew-Shot Image ClassificationFew-Shot Learning+4Classification of Hyperspectral Images by Using Spectral Data and Fully Connected Neural Network
It is observed that high classification performance is achieved for one- and two-dimensional signals by using deep learning methods. In this context, most researchers have tried to classify hyperspectral images by using …
ClassificationClassification Of Hyperspectral ImagesExtreme Learning Machine-Based Heterogeneous Domain Adaptation for Classification of Hyperspectral Images
An extreme learning machine (ELM)-based heterogeneous domain adaptation (HDA) algorithm is proposed for the classification of remote sensing images. In the adaptive ELM network, one hidden layer is used for the source…
ClassificationClassification Of Hyperspectral ImagesDomain Adaptationdomain classification+1A Supervised Geometry-Aware Mapping Approach for Classification of Hyperspectral Images
The lack of proper class discrimination among the Hyperspectral (HS) data points poses a potential challenge in HS classification. To address this issue, this paper proposes an optimal geometry-aware transformation for e…
ClassificationClassification Of Hyperspectral ImagesDimensionality ReductionGeneral ClassificationGMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data
The amount of training data that is required to train a classifier scales with the dimensionality of the feature data. In hyperspectral remote sensing, feature data can potentially become very high dimensional. However, …
Classification Of Hyperspectral ImagesGeneral ClassificationMulti-class ClassificationGraph Scaling Cut with L1-Norm for Classification of Hyperspectral Images
In this paper, we propose an L1 normalized graph based dimensionality reduction method for Hyperspectral images, called as L1-Scaling Cut (L1-SC). The underlying idea of this method is to generate the optimal projection …
Classification Of Hyperspectral ImagesDimensionality ReductionGeneral Classification