U.S. flag

An official website of the United States government

Dot gov

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Https

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

PubAg

Main content area

You searched for: Subject "hyperspectral imagery" Remove constraint Subject: "hyperspectral imagery"
Start Over

Search

2,960  Search Results

« Previous | 1 - 20 of 2,960 |
Number of results to display per page

Search Results

1. A Novel Double Ensemble Algorithm for the Classification of Multi-Class Imbalanced Hyperspectral Data

Author:
Daying Quan; Wei Feng; Gabriel Dauphin; Xiaofeng Wang; Wenjiang Huang; Mengdao Xing
Source:
Remote Sensing 2022 v.14 no.15 pp. -
ISSN:
2072-4292
Subject:
hyperspectral imagery,   etc   ; algorithms; models;     Show all 3 Subjects
Abstract:
... The class imbalance problem has been reported to exist in remote sensing and hinders the classification performance of many machine learning algorithms. Several technologies, such as data sampling methods, feature selection-based methods, and ensemble-based methods, have been proposed to solve the class imbalance problem. However, these methods suffer from the loss of useful information or from ar ...
DOI:
10.3390/rs14153765

2. A Dual Multi-Head Contextual Attention Network for Hyperspectral Image Classification

Author:
Miaomiao Liang; Qinghua He; Xiangchun Yu; Huai Wang; Zhe Meng; Licheng Jiao
Source:
Remote Sensing 2022 v.14 no.13 pp. -
ISSN:
2072-4292
Subject:
hyperspectral imagery,   etc   ; image analysis; models;     Show all 3 Subjects
Abstract:
... To learn discriminative features, hyperspectral image (HSI), containing 3-D cube data, is a preferable means of capturing multi-head self-attention from both spatial and spectral domains if the burden in model optimization and computation is low. In this paper, we design a dual multi-head contextual self-attention (DMuCA) network for HSI classification with the fewest possible parameters and lower ...
DOI:
10.3390/rs14133091

3. Feature identification and extraction of urban built-up surfaces and materials in AVIRIS-NG hyperspectral imagery

Author:
Dwijendra Pandey; K. C. Tiwari
Source:
Geocarto international 2022 v.37 no.6 pp. 1722-1743
ISSN:
1752-0762
Subject:
hyperspectral imagery,   etc   ; urban areas; India;     Show all 3 Subjects
Abstract:
... Regular monitoring and precise mapping of urban environment is required by various applications. In this paper, a new method is proposed, in which different combinations of feature bands have been utilized for extraction of built-up surfaces, sub-surfaces and materials in different levels (Level-1, 2 and 3) using AVIRIS-NG hyperspectral imagery of Jodhpur, Rajasthan region of India. Features ident ...
DOI:
10.1080/10106049.2020.1797187

4. Hyperspectral image classification by integrating attention-based LSTM and hybrid spectral networks

Author:
Mohammed Ahmed AL-Kubaisi; Helmi Zulhaidi Mohd Shafri; Mohd Hasmadi Ismail; Mohd Johari Mohd Yusof; Shaiful Jahari Bin Hashim
Source:
International journal of remote sensing 2022 v.43 no.9 pp. 3450-3469
ISSN:
1366-5901
Subject:
hyperspectral imagery,   etc   ; hybrids; image analysis;     Show all 3 Subjects
Abstract:
... Though hyperspectral remote sensing images contain rich spatial and spectral information, they pose challenges in terms of feature extraction and mining. This paper describes the integration of a dimensionality reduction technique that employs spectral attention and Hybrid Spectral Networks (HybridSN) with spatial attention for hyperspectral image classification. The goal of this approach is to im ...
DOI:
10.1080/01431161.2022.2093621

5. ZY-1 02D Hyperspectral Imagery Super-Resolution via Endmember Matrix Constraint Unmixing

Author:
Xintong Zhang; Aiwu Zhang; Raechel Portelli; Xizhen Zhang; Hongliang Guan
Source:
Remote Sensing 2022 v.14 no.16 pp. -
ISSN:
2072-4292
Subject:
hyperspectral imagery,   etc   ; algorithms; multispectral imagery;     Show all 3 Subjects
Abstract:
... This paper proposes an endmember matrix constraint unmixing method for ZY-1 02D hyperspectral imagery (HSI) super-resolution reconstruction (SRR) to overcome the low resolution of ZY-1 02D HSI. The proposed method combines spectral unmixing and adds novel smoothing constraints to traditional non-negative matrix factorization to improve details and preserve the spectral information of traditional S ...
DOI:
10.3390/rs14164034

6. HyperLoopNet: Hyperspectral image classification using multiscale self-looping convolutional networks

Author:
Shivam Pande; Biplab Banerjee
Source:
ISPRS journal of photogrammetry and remote sensing 2022 v.183 pp. 422-438
ISSN:
0924-2716
Subject:
hyperspectral imagery,   etc   ; image analysis; photogrammetry;     Show all 3 Subjects
Abstract:
... Hyperspectral image (HSI) classification using convolutional neural networks (CNNs) has always been a hot topic in the field of remote sensing. This is owing to the high level feature extraction offered by CNNs that enables efficient encoding of the features at several stages. However, the drawback with CNNs is that for exceptional performance, they need a deeper and wider architecture along with ...
DOI:
10.1016/j.isprsjprs.2021.11.021

7. Unsupervised Generative Adversarial Network with Background Enhancement and Irredundant Pooling for Hyperspectral Anomaly Detection

Author:
Zhongwei Li; Shunxiao Shi; Leiquan Wang; Mingming Xu; Luyao Li
Source:
Remote Sensing 2022 v.14 no.5 pp. -
ISSN:
2072-4292
Subject:
hyperspectral imagery,   etc   ; data collection; models;     Show all 3 Subjects
Abstract:
... Lately, generative adversarial networks (GAN)-based methods have drawn extensive attention and achieved a promising performance in the field of hyperspectral anomaly detection (HAD) owing to GAN’s powerful data generation capability. However, without considering the background spatial features, most of these methods can not obtain a GAN with a strong background generation ability. Besides, they fa ...
DOI:
10.3390/rs14051265

8. Diverse-Region Hyperspectral Image Classification via Superpixelwise Graph Convolution Technique

Author:
Yan Huang; Xiao Zhou; Bobo Xi; Jiaojiao Li; Jian Kang; Shiyang Tang; Zhanye Chen; Wei Hong
Source:
Remote Sensing 2022 v.14 no.12 pp. -
ISSN:
2072-4292
Subject:
hyperspectral imagery,   etc   ; data collection; image analysis;     Show all 3 Subjects
Abstract:
... In this paper, a diverse-region hyperspectral image classification (DRHy) method is proposed by considering both irregularly local pixels and globally contextual connections between pixels. Specifically, the proposed method is operated on non-Euclidean graphs, which are constructed by superpixel segmentation methods for diverse regions to cluster irregularly local-region pixels. In addition, the d ...
DOI:
10.3390/rs14122907

9. Evaluation of CNN model by comparing with convolutional autoencoder and deep neural network for crop classification on hyperspectral imagery

Author:
Kavita Bhosle; Vijaya Musande
Source:
Geocarto international 2022 v.37 no.3 pp. 813-827
ISSN:
1752-0762
Subject:
hyperspectral imagery,   etc   ; data collection; neural networks;     Show all 3 Subjects
Abstract:
... Identification of crops is an important topic in the agricultural domain. Hyperspectral remote sensing data are very useful for crop feature extraction and classification. Remote sensing data is an unstructured data and Convolutional Neural Network (CNN) can work on unstructured data efficiently. This paper presents an evaluation of CNN for crop classification using the Indian Pines standard datas ...
DOI:
10.1080/10106049.2020.1740950

10. Hyperspectral anomaly detection based on score-guided cooperative autoencoders

Author:
Minkai Hou; Tao Wang; Yanzhao Su; Yanping Cai; Yiting Wang
Source:
International journal of remote sensing 2022 v.43 no.12 pp. 4436-4453
ISSN:
1366-5901
Subject:
hyperspectral imagery,   etc   ; algorithms; data collection; models;     Show all 4 Subjects
Abstract:
... Hyperspectral anomaly detection (HAD) is an important application of hyperspectral technology and has received extensive research attention. The lack of available prior spectral information limits deep-learning-based HAD. During the model training process, background and anomaly data are input into the network indiscriminately, and the model tends to overfit, which results in a better representati ...
DOI:
10.1080/01431161.2022.2112110

11. Transferable network with Siamese architecture for anomaly detection in hyperspectral images

Author:
Weiqiang Rao; Ying Qu; Lianru Gao; Xu Sun; Yuanfeng Wu; Bing Zhang
Source:
International journal of applied earth observation and geoinformation 2022 v.106 pp. 102669
ISSN:
1569-8432
Subject:
hyperspectral imagery,   etc   ; data collection; spatial data;     Show all 3 Subjects
Abstract:
... The purpose of hyperspectral anomaly detection is to distinguish abnormal objects from the surrounding background. In actual scenes, however, the complexity of ground objects, the high-dimensionality of data and the non-linear correlation of bands have high requirements for the generalizability, feature extraction ability and nonlinear expression ability of anomaly detection algorithms. In order t ...
DOI:
10.1016/j.jag.2021.102669

12. Affinity Propagation Based on Structural Similarity Index and Local Outlier Factor for Hyperspectral Image Clustering

Author:
Haimiao Ge; Liguo Wang; Haizhu Pan; Yuexia Zhu; Xiaoyu Zhao; Moqi Liu
Source:
Remote Sensing 2022 v.14 no.5 pp. -
ISSN:
2072-4292
Subject:
hyperspectral imagery,   etc   ; algorithms; data collection; wavelet;     Show all 4 Subjects
Abstract:
... In hyperspectral remote sensing, the clustering technique is an important issue of concern. Affinity propagation is a widely used clustering algorithm. However, the complex structure of the hyperspectral image (HSI) dataset presents challenge for the application of affinity propagation. In this paper, an improved version of affinity propagation based on complex wavelet structural similarity index ...
DOI:
10.3390/rs14051195

13. Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification

Author:
Jing Bai; Jiawei Lu; Zhu Xiao; Zheng Chen; Licheng Jiao
Source:
Remote Sensing 2022 v.14 no.14 pp. -
ISSN:
2072-4292
Subject:
hyperspectral imagery,   etc   ; data collection; image analysis;     Show all 3 Subjects
Abstract:
... 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 samples. Existing methods in few-shot problems usually require another dataset in order to improve the classification accuracy. However, the cross-domain problem exists in these method ...
DOI:
10.3390/rs14143426

14. Supervised data-driven approach for hyperspectral band selection using quantization

Author:
Arati Paul; Nabendu Chaki
Source:
Geocarto international 2022 v.37 no.8 pp. 2312-2322
ISSN:
1752-0762
Subject:
hyperspectral imagery,   etc   ; data collection; reflectance; variance;     Show all 4 Subjects
Abstract:
... Hyperspectral image (HSI), acquired in narrow and contiguous bands, contains redundant information in neighbouring bands. In order to overcome the processing overhead of this data like Hughes phenomenon, dimension of HSI is reduced either by using feature extraction or selection technique. Most of the existing dimensionality reduction methods depend on the user input to provide the reduced set of ...
DOI:
10.1080/10106049.2020.1822929

15. A probabilistic neighbourhood pooling-based attention network for hyperspectral image classification

Author:
Yuanlin Wang; Tiecheng Song; Yurui Xie; Swalpa Kumar Roy
Source:
Remote sensing letters 2022 v.13 no.1 pp. 65-75
ISSN:
2150-7058
Subject:
hyperspectral imagery,   etc   ; image analysis; spatial data;     Show all 3 Subjects
Abstract:
... Attention mechanisms are recently deployed in deep learning models for hyperspectral image (HSI) classification. Conventional spectral attentions typically use global pooling to aggregate spatial information, without sufficiently considering the spatial dependencies of the central pixel to be classified and its neighbours. Moreover, the limited training samples with high-dimensional spectral infor ...
DOI:
10.1080/2150704X.2021.1992034

16. A Hybrid-Order Spectral-Spatial Feature Network for Hyperspectral Image Classification

Author:
Dongxu Liu; Guangliang Han; Peixun Liu; Yirui Wang; Hang Yang; Dianbing Chen; Qingqing Li; Jiajia Wu
Source:
Remote Sensing 2022 v.14 no.15 pp. -
ISSN:
2072-4292
Subject:
hyperspectral imagery,   etc   ; distillation; image analysis; statistics;     Show all 4 Subjects
Abstract:
... Convolutional neural networks are widely applied in hyperspectral image (HSI) classification and show excellent performance. However, there are two challenges: the first is that fine features are generally lost in the process of depth transfer; the second is that most existing studies usually restore to first-order features, whereas they rarely consider second-order representations. To tackle the ...
DOI:
10.3390/rs14153555

17. Hyperspectral Image Classification Promotion Using Clustering Inspired Active Learning

Author:
Chen Ding; Mengmeng Zheng; Feixiong Chen; Yuankun Zhang; Xusi Zhuang; Enquan Fan; Dushi Wen; Lei Zhang; Wei Wei; Yanning Zhang
Source:
Remote Sensing 2022 v.14 no.3 pp. -
ISSN:
2072-4292
Subject:
hyperspectral imagery,   etc   ; data collection; image analysis;     Show all 3 Subjects
Abstract:
... Deep neural networks (DNNs) have promoted much of the recent progress in hyperspectral image (HSI) classification, which depends on extensive labeled samples and deep network structure and has achieved surprisingly good generalization capacity. However, due to the expensive labeling cost, the labeled samples are scarce in most practice cases, which causes these DNN-based methods to be prone to ove ...
DOI:
10.3390/rs14030596

18. Improving Intramuscular Fat Assessment in Pork by Synergy Between Spectral and Spatial Features in Hyperspectral Image

Author:
Christopher T. Kucha; Li Liu; Michael Ngadi; Claude Gariépy
Source:
Food analytical methods 2022 v.15 no.1 pp. 212-226
ISSN:
1936-9751
Subject:
hyperspectral imagery,   etc   ; intramuscular fat; pork; prediction;     Show all 4 Subjects
Abstract:
... Meat is a complex matrix of structural features exhibiting physical and chemical variations. The duality of the spatial and spectral information in the hyperspectral image of meat provides complementary information, and a synergistic fusion of the information will allow for the development of a rapid and non-invasive system based on hyperspectral imaging for assessment of a chemical component in m ...
DOI:
10.1007/s12161-021-02113-1

19. Sedimentary structure discrimination with hyperspectral imaging in sediment cores

Author:
Kévin Jacq; William Rapuc; Alexandre Benoit; Didier Coquin; Bernard Fanget; Yves Perrette; Pierre Sabatier; Bruno Wilhelm; Maxime Debret; Fabien Arnaud
Source:
Science of the total environment 2022 v.817 pp. 152018
ISSN:
0048-9697
Subject:
hyperspectral imagery,   etc   ; environment; lakes; prediction; sediments;     Show all 5 Subjects
Abstract:
... Hyperspectral imaging (HSI) is a non-destructive, high-resolution imaging technique that is currently under significant development for analyzing geological areas with remote devices or natural samples in a laboratory. In both cases, the hyperspectral image provides several sedimentary structures that must be separated to temporally and spatially describe the sample. Sediment sequences are compose ...
DOI:
10.1016/j.scitotenv.2021.152018

20. Spatial-spectral classification of hyperspectral images based on multiple fractal-based features

Author:
Behnam Asghari Beirami; Mehdi Mokhtarzade
Source:
Geocarto international 2022 v.37 no.1 pp. 231-245
ISSN:
1752-0762
Subject:
hyperspectral imagery,   etc   ; Aesculus; geometry; spatial data;     Show all 4 Subjects
Abstract:
... Hyperspectral images are efficient tools for discriminating different types of earth's surface materials. Spectral features traditionally perform classification of hyperspectral images, but different studies have proved the efficiency of spatial features as complementary information in increasing the classification accuracy. The fractal geometry can be regarded as a potent tool for spatial data mo ...
DOI:
10.1080/10106049.2020.1713232