Log in

Relevant bibliographies by topics / Hyperspectral and multispectral data fusion / Journal articles

To see the other types of publications on this topic, follow the link: Hyperspectral and multispectral data fusion.

Journal articles on the topic 'Hyperspectral and multispectral data fusion'

Author: Grafiati

Published: 6 September 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Hyperspectral and multispectral data fusion.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Chakravortty, S., and P. Subramaniam. "Fusion of Hyperspectral and Multispectral Image Data for Enhancement of Spectral and Spatial Resolution." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 1099–103. http://dx.doi.org/10.5194/isprsarchives-xl-8-1099-2014.

Full text

Abstract:

Hyperspectral image enhancement has been a concern for the remote sensing society for detailed end member detection. Hyperspectral remote sensor collects images in hundreds of narrow, continuous spectral channels, whereas multispectral remote sensor collects images in relatively broader wavelength bands. However, the spatial resolution of the hyperspectral sensor image is comparatively lower than that of the multispectral. As a result, spectral signatures from different end members originate within a pixel, known as mixed pixels. This paper presents an approach for obtaining an image which has

APA, Harvard, Vancouver, ISO, and other styles

2

Mifdal, Jamila, Bartomeu Coll, Jacques Froment, and Joan Duran. "Variational Fusion of Hyperspectral Data by Non-Local Filtering." Mathematics 9, no. 11 (2021): 1265. http://dx.doi.org/10.3390/math9111265.

Full text

Abstract:

The fusion of multisensor data has attracted a lot of attention in computer vision, particularly among the remote sensing community. Hyperspectral image fusion consists in merging the spectral information of a hyperspectral image with the geometry of a multispectral one in order to infer an image with high spatial and spectral resolutions. In this paper, we propose a variational fusion model with a nonlocal regularization term that encodes patch-based filtering conditioned to the geometry of the multispectral data. We further incorporate a radiometric constraint that injects the high frequenci

APA, Harvard, Vancouver, ISO, and other styles

3

Gao, Jianhao, Jie Li, and Menghui Jiang. "Hyperspectral and Multispectral Image Fusion by Deep Neural Network in a Self-Supervised Manner." Remote Sensing 13, no. 16 (2021): 3226. http://dx.doi.org/10.3390/rs13163226.

Full text

Abstract:

Compared with multispectral sensors, hyperspectral sensors obtain images with high- spectral resolution at the cost of spatial resolution, which constrains the further and precise application of hyperspectral images. An intelligent idea to obtain high-resolution hyperspectral images is hyperspectral and multispectral image fusion. In recent years, many studies have found that deep learning-based fusion methods outperform the traditional fusion methods due to the strong non-linear fitting ability of convolution neural network. However, the function of deep learning-based methods heavily depends

APA, Harvard, Vancouver, ISO, and other styles

4

Li, Jiaxin, Ke Zheng, Jing Yao, Lianru Gao, and Danfeng Hong. "Deep Unsupervised Blind Hyperspectral and Multispectral Data Fusion." IEEE Geoscience and Remote Sensing Letters 19 (2022): 1–5. http://dx.doi.org/10.1109/lgrs.2022.3151779.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Nikolakopoulos, K., Ev Gioti, G. Skianis, and D. Vaiopoulos. "AMELIORATING THE SPATIAL RESOLUTION OF HYPERION HYPERSPECTRAL DATA. THE CASE OF ANTIPAROS ISLAND." Bulletin of the Geological Society of Greece 43, no. 3 (2017): 1627. http://dx.doi.org/10.12681/bgsg.11337.

Full text

Abstract:

In this study seven fusion techniques and more especially the Ehlers, Gram-Schmidt, High Pass Filter, Local Mean Matching (LMM), Local Mean and Variance Matching (LMVM), Pansharp and PCA, were used for the fusion of Hyperion hyperspectral data with ALI panchromatic data. The panchromatic data have a spatial resolution of 10m while the hyperspectral data have a spatial resolution of 30m. All the fusion techniques are designed for use with classical multispectral data. Thus, it is quite interesting to investigate the assessment of the common used fusion algorithms with the hyperspectral data. Th

APA, Harvard, Vancouver, ISO, and other styles

6

Chang, Chein-I., Meiping Song, Chunyan Yu, et al. "Editorial for Special Issue “Advances in Hyperspectral Data Exploitation”." Remote Sensing 14, no. 20 (2022): 5111. http://dx.doi.org/10.3390/rs14205111.

Full text

Abstract:

Hyperspectral imaging (HSI) has emerged as a promising, advanced technology in remote sensing and has demonstrated great potential in the exploitation of a wide variety of data. In particular, its capability has expanded from unmixing data samples and detecting targets at the subpixel scale to finding endmembers, which generally cannot be resolved by multispectral imaging. Accordingly, a wealth of new HSI research has been conducted and reported in the literature in recent years. The aim of this Special Issue “Advances in Hyperspectral Data Exploitation“ is to provide a forum for scholars and

APA, Harvard, Vancouver, ISO, and other styles

7

Hervieu, Alexandre, Arnaud Le Bris, and Clément Mallet. "FUSION OF HYPERSPECTRAL AND VHR MULTISPECTRAL IMAGE CLASSIFICATIONS IN URBAN α–AREAS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 6, 2016): 457–64. http://dx.doi.org/10.5194/isprs-annals-iii-3-457-2016.

Full text

Abstract:

An energetical approach is proposed for classification decision fusion in urban areas using multispectral and hyperspectral imagery at distinct spatial resolutions. Hyperspectral data provides a great ability to discriminate land-cover classes while multispectral data, usually at higher spatial resolution, makes possible a more accurate spatial delineation of the classes. Hence, the aim here is to achieve the most accurate classification maps by taking advantage of both data sources at the decision level: spectral properties of the hyperspectral data and the geometrical resolution of multispec

APA, Harvard, Vancouver, ISO, and other styles

8

Peng, Mingyuan, Guoyuan Li, Xiaoqing Zhou, et al. "A Registration-Error-Resistant Swath Reconstruction Method of ZY1-02D Satellite Hyperspectral Data Using SRE-ResNet." Remote Sensing 14, no. 22 (2022): 5890. http://dx.doi.org/10.3390/rs14225890.

Full text

Abstract:

ZY1-02D is a Chinese hyperspectral satellite, which is equipped with a visible near-infrared multispectral camera and a hyperspectral camera. Its data are widely used in soil quality assessment, mineral mapping, water quality assessment, etc. However, due to the limitations of CCD design, the swath of hyperspectral data is relatively smaller than multispectral data. In addition, stripe noise and collages exist in hyperspectral data. With the contamination brought by clouds appearing in the scene, the availability is further affected. In order to solve these problems, this article used a swath

APA, Harvard, Vancouver, ISO, and other styles

9

Guilloteau, Claire, Thomas Oberlin, Olivier Berné, Émilie Habart, and Nicolas Dobigeon. "Simulated JWST Data Sets for Multispectral and Hyperspectral Image Fusion." Astronomical Journal 160, no. 1 (2020): 28. http://dx.doi.org/10.3847/1538-3881/ab9301.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Yokoya, Naoto, Takehisa Yairi, and Akira Iwasaki. "Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion." IEEE Transactions on Geoscience and Remote Sensing 50, no. 2 (2012): 528–37. http://dx.doi.org/10.1109/tgrs.2011.2161320.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Chang, Chein-I., Meiping Song, Junping Zhang, and Chao-Cheng Wu. "Editorial for Special Issue “Hyperspectral Imaging and Applications”." Remote Sensing 11, no. 17 (2019): 2012. http://dx.doi.org/10.3390/rs11172012.

Full text

Abstract:

Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging. The aim of this Special Issue “Hyperspectral Imaging and Applications” is to publish new ideas and technologies to facilitate the utility of hyperspectral imaging in data exploitation and to further explore its potential in different applications. This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories, Data Unmixing,

APA, Harvard, Vancouver, ISO, and other styles

12

Liu, Hui, Liangfeng Deng, Yibo Dou, Xiwu Zhong, and Yurong Qian. "Pansharpening Model of Transferable Remote Sensing Images Based on Feature Fusion and Attention Modules." Sensors 23, no. 6 (2023): 3275. http://dx.doi.org/10.3390/s23063275.

Full text

Abstract:

The purpose of the panchromatic sharpening of remote sensing images is to generate high-resolution multispectral images through software technology without increasing economic expenditure. The specific method is to fuse the spatial information of a high-resolution panchromatic image and the spectral information of a low-resolution multispectral image. This work proposes a novel model for generating high-quality multispectral images. This model uses the feature domain of the convolution neural network to fuse multispectral and panchromatic images so that the fused images can generate new featur

APA, Harvard, Vancouver, ISO, and other styles

13

Vargas, Edwin, Kevin Arias, Fernando Rojas, and Henry Arguello. "Fusion of Hyperspectral and Multispectral Images Based on a Centralized Non-local Sparsity Model of Abundance Maps." Tecnura 24, no. 66 (2020): 62–75. http://dx.doi.org/10.14483/22487638.16904.

Full text

Abstract:

   Objective: Hyperspectral (HS) imaging systems are commonly used in a diverse range of applications that involve detection and classification tasks. However, the low spatial resolution of hyperspectral images may limit the performance of the involved tasks in such applications. In the last years, fusing the information of an HS image with high spatial resolution multispectral (MS) or panchromatic (PAN) images has been widely studied to enhance the spatial resolution. Image fusion has been formulated as an inverse problem whose solution is an HS image which assumed to be spa

APA, Harvard, Vancouver, ISO, and other styles

14

Yokoya, Naoto, Claas Grohnfeldt, and Jocelyn Chanussot. "Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature." IEEE Geoscience and Remote Sensing Magazine 5, no. 2 (2017): 29–56. http://dx.doi.org/10.1109/mgrs.2016.2637824.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Benhalouche, Fatima Zohra, Moussa Sofiane Karoui, Yannick Deville, and Abdelaziz Ouamri. "Hyperspectral and multispectral data fusion based on linear-quadratic nonnegative matrix factorization." Journal of Applied Remote Sensing 11, no. 2 (2017): 025008. http://dx.doi.org/10.1117/1.jrs.11.025008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Ren, Kai, Weiwei Sun, Xiangchao Meng, Gang Yang, and Qian Du. "Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?" Remote Sensing 12, no. 5 (2020): 882. http://dx.doi.org/10.3390/rs12050882.

Full text

Abstract:

The China GaoFen-5 (GF-5) satellite sensor, which was launched in 2018, collects hyperspectral data with 330 spectral bands, a 30 m spatial resolution, and 60 km swath width. Its competitive advantages compared to other on-orbit or planned sensors are its number of bands, spectral resolution, and swath width. Unfortunately, its applications may be undermined by its relatively low spatial resolution. Therefore, the data fusion of GF-5 with high spatial resolution multispectral data is required to further enhance its spatial resolution while preserving its spectral fidelity. This paper conducted

APA, Harvard, Vancouver, ISO, and other styles

17

Lin, Hong, Jian Long, Yuanxi Peng, and Tong Zhou. "Hyperspectral Multispectral Image Fusion via Fast Matrix Truncated Singular Value Decomposition." Remote Sensing 15, no. 1 (2022): 207. http://dx.doi.org/10.3390/rs15010207.

Full text

Abstract:

Recently, methods for obtaining a high spatial resolution hyperspectral image (HR-HSI) by fusing a low spatial resolution hyperspectral image (LR-HSI) and high spatial resolution multispectral image (HR-MSI) have become increasingly popular. However, most fusion methods require knowing the point spread function (PSF) or the spectral response function (SRF) in advance, which are uncertain and thus limit the practicability of these fusion methods. To solve this problem, we propose a fast fusion method based on the matrix truncated singular value decomposition (FTMSVD) without using the SRF, in w

APA, Harvard, Vancouver, ISO, and other styles

18

Zare, Marzieh, Mohammad Sadegh Helfroush, Kamran Kazemi, and Paul Scheunders. "Hyperspectral and Multispectral Image Fusion Using Coupled Non-Negative Tucker Tensor Decomposition." Remote Sensing 13, no. 15 (2021): 2930. http://dx.doi.org/10.3390/rs13152930.

Full text

Abstract:

Fusing a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI), aiming to produce a super-resolution hyperspectral image, has recently attracted increasing research interest. In this paper, a novel approach based on coupled non-negative tensor decomposition is proposed. The proposed method performs a tucker tensor factorization of a low resolution hyperspectral image and a high resolution multispectral image under the constraint of non-negative tensor decomposition (NTD). The conventional matrix factorization methods essentially lose spatio-s

APA, Harvard, Vancouver, ISO, and other styles

19

Lu, Han, Danyu Qiao, Yongxin Li, Shuang Wu, and Lei Deng. "Fusion of China ZY-1 02D Hyperspectral Data and Multispectral Data: Which Methods Should Be Used?" Remote Sensing 13, no. 12 (2021): 2354. http://dx.doi.org/10.3390/rs13122354.

Full text

Abstract:

ZY-1 02D is China’s first civil hyperspectral (HS) operational satellite, developed independently and successfully launched in 2019. It can collect HS data with a spatial resolution of 30 m, 166 spectral bands, a spectral range of 400~2500 nm, and a swath width of 60 km. Its competitive advantages over other on-orbit or planned satellites are its high spectral resolution and large swath width. Unfortunately, the relatively low spatial resolution may limit its applications. As a result, fusing ZY-1 02D HS data with high-spatial-resolution multispectral (MS) data is required to improve spatial r

APA, Harvard, Vancouver, ISO, and other styles

20

Hall, Emma C., and Mark J. Lara. "Multisensor UAS mapping of Plant Species and Plant Functional Types in Midwestern Grasslands." Remote Sensing 14, no. 14 (2022): 3453. http://dx.doi.org/10.3390/rs14143453.

Full text

Abstract:

Uncrewed aerial systems (UASs) have emerged as powerful ecological observation platforms capable of filling critical spatial and spectral observation gaps in plant physiological and phenological traits that have been difficult to measure from space-borne sensors. Despite recent technological advances, the high cost of drone-borne sensors limits the widespread application of UAS technology across scientific disciplines. Here, we evaluate the tradeoffs between off-the-shelf and sophisticated drone-borne sensors for mapping plant species and plant functional types (PFTs) within a diverse grasslan

APA, Harvard, Vancouver, ISO, and other styles

21

Weinmann, M., and M. Weinmann. "FUSION OF HYPERSPECTRAL, MULTISPECTRAL, COLOR AND 3D POINT CLOUD INFORMATION FOR THE SEMANTIC INTERPRETATION OF URBAN ENVIRONMENTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1899–906. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1899-2019.

Full text

Abstract:

<p><strong>Abstract.</strong> In this paper, we address the semantic interpretation of urban environments on the basis of multi-modal data in the form of RGB color imagery, hyperspectral data and LiDAR data acquired from aerial sensor platforms. We extract radiometric features based on the given RGB color imagery and the given hyperspectral data, and we also consider different transformations to potentially better data representations. For the RGB color imagery, these are achieved via color invariants, normalization procedures or specific assumptions about the scene. For the

APA, Harvard, Vancouver, ISO, and other styles

22

Anshakov, G. P., A. V. Raschupkin, and Y. N. Zhuravel. "Hyperspectral and multispectral resurs-p data fusion for increase of their informational content." Computer Optics 39, no. 1 (2015): 77–82. http://dx.doi.org/10.18287/0134-2452-2015-39-1-77-82.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Zhou, Xinyu, Ye Zhang, Junping Zhang, and Shaoqi Shi. "Alternating Direction Iterative Nonnegative Matrix Factorization Unmixing for Multispectral and Hyperspectral Data Fusion." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 (2020): 5223–32. http://dx.doi.org/10.1109/jstars.2020.3020586.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Guo, Fen Fen, Jian Rong Fan, Wen Qian Zang, Fei Liu, and Huai Zhen Zhang. "Research on Fusion Approach for Hyperspectral Image and Multispectral Image of HJ-1A." Advanced Materials Research 356-360 (October 2011): 2897–903. http://dx.doi.org/10.4028/www.scientific.net/amr.356-360.2897.

Full text

Abstract:

The vacancy of hyperspectral image (HSI) in China is made up by HJ-1A satellite, which makes more study and application possible. But comparing with other HSI, low spatial resolution turns into a big limiting obstacle for application. In order to improve the HSI quality and make full use of the existing RS data, this paper proposed a fusion approach basing on 3D wavelet transform (3D WT) to fusing HJ-1A HSI and Multispectral image (MSI) using their 3D structure. Contrasting with the principal component transform (PCA) and Gram-Schmidt fusion approach, which are mature at present, 3D WT fusion

APA, Harvard, Vancouver, ISO, and other styles

25

Karimov, B., G. Karimova, and N. Amankulova. "Land Cover Classification Improvements by Remote Sensing Data Fusion." Bulletin of Science and Practice, no. 2 (February 15, 2023): 66–74. http://dx.doi.org/10.33619/2414-2948/87/07.

Full text

Abstract:

Computer processing and analysis of satellite data is an urgent task of the science of remote sensing of the earth. Such processing can range from adjusting the contrast and brightness of the images of an amateur photographer to a group of scientists using neural network classification to determine the types of minerals in a hyperspectral satellite image. This article implements a method of satellite data fusion, which improves the digital image interpretation and image quality for further analysis. For fusion, a multispectral image with a resolution of 30 m Landsat 5 with 6 channels was taken

APA, Harvard, Vancouver, ISO, and other styles

26

Hu, Jingliang, Rong Liu, Danfeng Hong, et al. "MDAS: a new multimodal benchmark dataset for remote sensing." Earth System Science Data 15, no. 1 (2023): 113–31. http://dx.doi.org/10.5194/essd-15-113-2023.

Full text

Abstract:

Abstract. In Earth observation, multimodal data fusion is an intuitive strategy to break the limitation of individual data. Complementary physical contents of data sources allow comprehensive and precise information retrieval. With current satellite missions, such as ESA Copernicus programme, various data will be accessible at an affordable cost. Future applications will have many options for data sources. Such a privilege can be beneficial only if algorithms are ready to work with various data sources. However, current data fusion studies mostly focus on the fusion of two data sources. There

APA, Harvard, Vancouver, ISO, and other styles

27

Mohammed Noori, Abbas, Sumaya Falih Hasan, Qayssar Mahmood Ajaj, Mustafa Ridha Mezaal, Helmi Z. M. Shafri, and Muntadher Aidi Shareef. "Fusion of Airborne Hyperspectral and WorldView2 Multispectral Images for Detailed Urban Land Cover Classification A case Study of Kuala Lumpur, Malaysia." International Journal of Engineering & Technology 7, no. 4.37 (2018): 202. http://dx.doi.org/10.14419/ijet.v7i4.37.24102.

Full text

Abstract:

Detecting the features of urban areas in detail requires very high spatial and spectral resolution in images. Hyperspectral sensors usually offer high spectral resolution images with a low spatial resolution. By contrast, multispectral sensors produce high spatial resolution images with a poor spectral resolution. Therefore, numerous fusion algorithms and techniques have been proposed in recent years to obtain high-quality images with improved spatial and spectral resolutions by sensibly combining the data acquired for the same scene. This work aims to exploit the extracted information from im

APA, Harvard, Vancouver, ISO, and other styles

28

Ma, Fei, Feixia Yang, and Yanwei Wang. "Low-Rank Tensor Decomposition With Smooth and Sparse Regularization for Hyperspectral and Multispectral Data Fusion." IEEE Access 8 (2020): 129842–56. http://dx.doi.org/10.1109/access.2020.3009263.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Lin, Chia-Hsiang, Fei Ma, Chong-Yung Chi, and Chih-Hsiang Hsieh. "A Convex Optimization-Based Coupled Nonnegative Matrix Factorization Algorithm for Hyperspectral and Multispectral Data Fusion." IEEE Transactions on Geoscience and Remote Sensing 56, no. 3 (2018): 1652–67. http://dx.doi.org/10.1109/tgrs.2017.2766080.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Lu, Xiaochen, Dezheng Yang, Fengde Jia, and Yifeng Zhao. "Coupled Convolutional Neural Network-Based Detail Injection Method for Hyperspectral and Multispectral Image Fusion." Applied Sciences 11, no. 1 (2020): 288. http://dx.doi.org/10.3390/app11010288.

Full text

Abstract:

In this paper, a detail-injection method based on a coupled convolutional neural network (CNN) is proposed for hyperspectral (HS) and multispectral (MS) image fusion with the goal of enhancing the spatial resolution of HS images. Owing to the excellent performance in spectral fidelity of the detail-injection model and the image spatial–spectral feature exploration ability of CNN, the proposed method utilizes a couple of CNN networks as the feature extraction method and learns details from the HS and MS images individually. By appending an additional convolutional layer, both the extracted feat

APA, Harvard, Vancouver, ISO, and other styles

31

Cessna, Janice, Michael G. Alonzo, Adrianna C. Foster, and Bruce D. Cook. "Mapping Boreal Forest Spruce Beetle Health Status at the Individual Crown Scale Using Fused Spectral and Structural Data." Forests 12, no. 9 (2021): 1145. http://dx.doi.org/10.3390/f12091145.

Full text

Abstract:

The frequency and severity of spruce bark beetle outbreaks are increasing in boreal forests leading to widespread tree mortality and fuel conditions promoting extreme wildfire. Detection of beetle infestation is a forest health monitoring (FHM) priority but is hampered by the challenges of detecting early stage (“green”) attack from the air. There is indication that green stage might be detected from vertical gradients of spectral data or from shortwave infrared information distributed within a single crown. To evaluate the efficacy of discriminating “non-infested”, “green”, and “dead” health

APA, Harvard, Vancouver, ISO, and other styles

32

Huang, Leping, Zhongwen Hu, Xin Luo, Qian Zhang, Jingzhe Wang, and Guofeng Wu. "Stepwise Fusion of Hyperspectral, Multispectral and Panchromatic Images with Spectral Grouping Strategy: A Comparative Study Using GF5 and GF1 Images." Remote Sensing 14, no. 4 (2022): 1021. http://dx.doi.org/10.3390/rs14041021.

Full text

Abstract:

Since hyperspectral satellite images (HSIs) usually hold low spatial resolution, improving the spatial resolution of hyperspectral imaging (HSI) is an effective solution to explore its potential for remote sensing applications, such as land cover mapping over urban and coastal areas. The fusion of HSIs with high spatial resolution multispectral images (MSIs) and panchromatic (PAN) images could be a solution. To address the challenging work of fusing HSIs, MSIs and PAN images, a novel easy-to-implement stepwise fusion approach was proposed in this study. The fusion of HSIs and MSIs was decompos

APA, Harvard, Vancouver, ISO, and other styles

33

Fan, Shuxiang, Changying Li, Wenqian Huang, and Liping Chen. "Data Fusion of Two Hyperspectral Imaging Systems with Complementary Spectral Sensing Ranges for Blueberry Bruising Detection." Sensors 18, no. 12 (2018): 4463. http://dx.doi.org/10.3390/s18124463.

Full text

Abstract:

Currently, the detection of blueberry internal bruising focuses mostly on single hyperspectral imaging (HSI) systems. Attempts to fuse different HSI systems with complementary spectral ranges are still lacking. A push broom based HSI system and a liquid crystal tunable filter (LCTF) based HSI system with different sensing ranges and detectors were investigated to jointly detect blueberry internal bruising in the lab. The mean reflectance spectrum of each berry sample was extracted from the data obtained by two HSI systems respectively. The spectral data from the two spectroscopic techniques we

APA, Harvard, Vancouver, ISO, and other styles

34

Tong, Zhonggui, Yuxia Li, Jinglin Zhang, Lei He, and Yushu Gong. "MSFANet: Multiscale Fusion Attention Network for Road Segmentation of Multispectral Remote Sensing Data." Remote Sensing 15, no. 8 (2023): 1978. http://dx.doi.org/10.3390/rs15081978.

Full text

Abstract:

With the development of deep learning and remote sensing technologies in recent years, many semantic segmentation methods based on convolutional neural networks (CNNs) have been applied to road extraction. However, previous deep learning-based road extraction methods primarily used RGB imagery as an input and did not take advantage of the spectral information contained in hyperspectral imagery. These methods can produce discontinuous outputs caused by objects with similar spectral signatures to roads. In addition, the images obtained from different Earth remote sensing sensors may have differe

APA, Harvard, Vancouver, ISO, and other styles

35

Guo, Siyu, Xi’ai Chen, Huidi Jia, Zhi Han, Zhigang Duan, and Yandong Tang. "Fusing Hyperspectral and Multispectral Images via Low-Rank Hankel Tensor Representation." Remote Sensing 14, no. 18 (2022): 4470. http://dx.doi.org/10.3390/rs14184470.

Full text

Abstract:

Hyperspectral images (HSIs) have high spectral resolution and low spatial resolution. HSI super-resolution (SR) can enhance the spatial information of the scene. Current SR methods have generally focused on the direct utilization of image structure priors, which are often modeled in global or local lower-order image space. The spatial and spectral hidden priors, which are accessible from higher-order space, cannot be taken advantage of when using these methods. To solve this problem, we propose a higher-order Hankel space-based hyperspectral image-multispectral image (HSI-MSI) fusion method in

APA, Harvard, Vancouver, ISO, and other styles

36

Zhang, Yi, Yizhe Yang, Qinwei Zhang, et al. "Toward Multi-Stage Phenotyping of Soybean with Multimodal UAV Sensor Data: A Comparison of Machine Learning Approaches for Leaf Area Index Estimation." Remote Sensing 15, no. 1 (2022): 7. http://dx.doi.org/10.3390/rs15010007.

Full text

Abstract:

Leaf Area Index (LAI) is an important parameter which can be used for crop growth monitoring and yield estimation. Many studies have been carried out to estimate LAI with remote sensing data obtained by sensors mounted on Unmanned Aerial Vehicles (UAVs) in major crops; however, most of the studies used only a single type of sensor, and the comparative study of different sensors and sensor combinations in the model construction of LAI was rarely reported, especially in soybean. In this study, three types of sensors, i.e., hyperspectral, multispectral, and LiDAR, were used to collect remote sens

APA, Harvard, Vancouver, ISO, and other styles

37

Ahmad, Uzair, Abozar Nasirahmadi, Oliver Hensel, and Stefano Marino. "Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring." Agronomy 12, no. 3 (2022): 555. http://dx.doi.org/10.3390/agronomy12030555.

Full text

Abstract:

Digital farming approach merges new technologies and sensor data to optimize the quality of crop monitoring in agriculture. The successful fusion of technology and data is highly dependent on the parameter collection, the modeling adoption, and the technology integration being accurately implemented according to the specified needs of the farm. This fusion technique has not yet been widely adopted due to several challenges; however, our study here reviews current methods and applications for fusing technologies and data. First, the study highlights different sensors that can be merged with oth

APA, Harvard, Vancouver, ISO, and other styles

38

Ling, Jianmei, Lu Li, and Haiyan Wang. "Improved Fusion of Spatial Information into Hyperspectral Classification through the Aggregation of Constrained Segment Trees: Segment Forest." Remote Sensing 13, no. 23 (2021): 4816. http://dx.doi.org/10.3390/rs13234816.

Full text

Abstract:

Compared with traditional optical and multispectral remote sensing images, hyperspectral images have hundreds of bands that can provide the possibility of fine classification of the earth’s surface. At the same time, a hyperspectral image is an image that coexists with the spatial and spectral. It has become a hot research topic to combine the spatial spectrum information of the image to classify hyperspectral features. Based on the idea of spatial–spectral classification, this paper proposes a novel hyperspectral image classification method based on a segment forest (SF). Firstly, the first p

APA, Harvard, Vancouver, ISO, and other styles

39

Jiang, Yufeng, Li Zhang, Min Yan, et al. "High-Resolution Mangrove Forests Classification with Machine Learning Using Worldview and UAV Hyperspectral Data." Remote Sensing 13, no. 8 (2021): 1529. http://dx.doi.org/10.3390/rs13081529.

Full text

Abstract:

Mangrove forests, as important ecological and economic resources, have suffered a loss in the area due to natural and human activities. Monitoring the distribution of and obtaining accurate information on mangrove species is necessary for ameliorating the damage and protecting and restoring mangrove forests. In this study, we compared the performance of UAV Rikola hyperspectral images, WorldView-2 (WV-2) satellite-based multispectral images, and a fusion of data from both in the classification of mangrove species. We first used recursive feature elimination‒random forest (RFE-RF) to select the

APA, Harvard, Vancouver, ISO, and other styles

40

Ouerghemmi, W., A. Le Bris, N. Chehata, and C. Mallet. "A TWO-STEP DECISION FUSION STRATEGY: APPLICATION TO HYPERSPECTRAL AND MULTISPECTRAL IMAGES FOR URBAN CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 167–74. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-167-2017.

Full text

Abstract:

Very high spatial resolution multispectral images and lower spatial resolution hyperspectral images are complementary sources for urban object classification. The first enables a fine delineation of objects, while the second can better discriminate classes and consider richer land cover semantics. This paper presents a decision fusion scheme taking advantage of both sources classification maps, to produce a better classification map. The proposed method aims at dealing with both semantic and spatial uncertainties and consists in two steps. First, class membership maps are merged at pixel level

APA, Harvard, Vancouver, ISO, and other styles

41

Brezini, Salah Eddine, and Yannick Deville. "Hyperspectral and Multispectral Image Fusion with Automated Extraction of Image-Based Endmember Bundles and Sparsity-Based Unmixing to Deal with Spectral Variability." Sensors 23, no. 4 (2023): 2341. http://dx.doi.org/10.3390/s23042341.

Full text

Abstract:

The aim of fusing hyperspectral and multispectral images is to overcome the limitation of remote sensing hyperspectral sensors by improving their spatial resolutions. This process, also known as hypersharpening, generates an unobserved high-spatial-resolution hyperspectral image. To this end, several hypersharpening methods have been developed, however most of them do not consider the spectral variability phenomenon; therefore, neglecting this phenomenon may cause errors, which leads to reducing the spatial and spectral quality of the sharpened products. Recently, new approaches have been prop

APA, Harvard, Vancouver, ISO, and other styles

42

Degerickx, Jeroen, Martin Hermy, and Ben Somers. "Mapping Functional Urban Green Types Using High Resolution Remote Sensing Data." Sustainability 12, no. 5 (2020): 2144. http://dx.doi.org/10.3390/su12052144.

Full text

Abstract:

Urban green spaces are known to provide ample benefits to human society and hence play a vital role in safeguarding the quality of life in our cities. In order to optimize the design and management of green spaces with regard to the provisioning of these ecosystem services, there is a clear need for uniform and spatially explicit datasets on the existing urban green infrastructure. Current mapping approaches, however, largely focus on large land use units (e.g., park, garden), or broad land cover classes (e.g., tree, grass), not providing sufficient thematic detail to model urban ecosystem ser

APA, Harvard, Vancouver, ISO, and other styles

43

Wang, Xueliang, and Honge Ren. "DBMF: A Novel Method for Tree Species Fusion Classification Based on Multi-Source Images." Forests 13, no. 1 (2021): 33. http://dx.doi.org/10.3390/f13010033.

Full text

Abstract:

Multi-source data remote sensing provides innovative technical support for tree species recognition. Tree species recognition is relatively poor despite noteworthy advancements in image fusion methods because the features from multi-source data for each pixel in the same region cannot be deeply exploited. In the present paper, a novel deep learning approach for hyperspectral imagery is proposed to improve accuracy for the classification of tree species. The proposed method, named the double branch multi-source fusion (DBMF) method, could more deeply determine the relationship between multi-sou

APA, Harvard, Vancouver, ISO, and other styles

44

Han, Yanling, Pengxia Cui, Yun Zhang, Ruyan Zhou, Shuhu Yang, and Jing Wang. "Remote Sensing Sea Ice Image Classification Based on Multilevel Feature Fusion and Residual Network." Mathematical Problems in Engineering 2021 (September 20, 2021): 1–10. http://dx.doi.org/10.1155/2021/9928351.

Full text

Abstract:

Sea ice disasters are already one of the most serious marine disasters in the Bohai Sea region of our country, which have seriously affected the coastal economic development and residents’ lives. Sea ice classification is an important part of sea ice detection. Hyperspectral imagery and multispectral imagery contain rich spectral information and spatial information and provide important data support for sea ice classification. At present, most sea ice classification methods mainly focus on shallow learning based on spectral features, and the good performance of the deep learning method in remo

APA, Harvard, Vancouver, ISO, and other styles

45

Mallikharjuna Rao, K., B. Srinivasa Rao, B. Sai Chandana, and J. Harikiran. "Dimensionality reduction and hierarchical clustering in framework for hyperspectral image segmentation." Bulletin of Electrical Engineering and Informatics 8, no. 3 (2019): 1081–87. http://dx.doi.org/10.11591/eei.v8i3.1451.

Full text

Abstract:

The hyperspectral data contains hundreds of narrows bands representing the same scene on earth, with each pixel has a continuous reflectance spectrum. The first attempts to analysehyperspectral images were based on techniques that were developed for multispectral images by randomly selecting few spectral channels, usually less than seven. This random selection of bands degrades the performance of segmentation algorithm on hyperspectraldatain terms of accuracies. In this paper, a new framework is designed for the analysis of hyperspectral image by taking the information from all the data channe

APA, Harvard, Vancouver, ISO, and other styles

46

Marques Junior, Ademir, Eniuce Menezes de Souza, Marianne Müller, et al. "Improving Spatial Resolution of Multispectral Rock Outcrop Images Using RGB Data and Artificial Neural Networks." Sensors 20, no. 12 (2020): 3559. http://dx.doi.org/10.3390/s20123559.

Full text

Abstract:

Spectral information provided by multispectral and hyperspectral sensors has a great impact on remote sensing studies, easing the identification of carbonate outcrops that contribute to a better understanding of petroleum reservoirs. Sensors aboard satellites like Landsat series, which have data freely available usually lack the spatial resolution that suborbital sensors have. Many techniques have been developed to improve spatial resolution through data fusion. However, most of them have serious limitations regarding application and scale. Recently Super-Resolution (SR) convolution neural net

APA, Harvard, Vancouver, ISO, and other styles

47

Zheng, Qiong, Wenjiang Huang, Qing Xia, et al. "Remote Sensing Monitoring of Rice Diseases and Pests from Different Data Sources: A Review." Agronomy 13, no. 7 (2023): 1851. http://dx.doi.org/10.3390/agronomy13071851.

Full text

Abstract:

Rice is an important food crop in China, and diseases and pests are the main factors threatening its safety, ecology, and efficient production. The development of remote sensing technology provides an important means for non-destructive and rapid monitoring of diseases and pests that threaten rice crops. This paper aims to provide insights into current and future trends in remote sensing for rice crop monitoring. First, we expound the mechanism of remote sensing monitoring of rice diseases and pests and introduce the applications of different commonly data sources (hyperspectral data, multispe

APA, Harvard, Vancouver, ISO, and other styles

48

Zhao, Jing, Fangjiang Pan, Xiao Xiao, et al. "Summer Maize Growth Estimation Based on Near-Surface Multi-Source Data." Agronomy 13, no. 2 (2023): 532. http://dx.doi.org/10.3390/agronomy13020532.

Full text

Abstract:

Rapid and accurate crop chlorophyll content estimation and the leaf area index (LAI) are both crucial for guiding field management and improving crop yields. This paper proposes an accurate monitoring method for LAI and soil plant analytical development (SPAD) values (which are closely related to leaf chlorophyll content; we use the SPAD instead of chlorophyll relative content) based on the fusion of ground–air multi-source data. Firstly, in 2020 and 2021, we collected unmanned aerial vehicle (UAV) multispectral data, ground hyperspectral data, UAV visible-light data, and environmental cumulat

APA, Harvard, Vancouver, ISO, and other styles

49

Gao, Yunhao, Xiukai Song, Wei Li, et al. "Fusion Classification of HSI and MSI Using a Spatial-Spectral Vision Transformer for Wetland Biodiversity Estimation." Remote Sensing 14, no. 4 (2022): 850. http://dx.doi.org/10.3390/rs14040850.

Full text

Abstract:

The rapid development of remote sensing technology provides wealthy data for earth observation. Land-cover mapping indirectly achieves biodiversity estimation at a coarse scale. Therefore, accurate land-cover mapping is the precondition of biodiversity estimation. However, the environment of the wetlands is complex, and the vegetation is mixed and patchy, so the land-cover recognition based on remote sensing is full of challenges. This paper constructs a systematic framework for multisource remote sensing image processing. Firstly, the hyperspectral image (HSI) and multispectral image (MSI) ar

APA, Harvard, Vancouver, ISO, and other styles

50

Qi, Guanghui, Chunyan Chang, Wei Yang, Peng Gao, and Gengxing Zhao. "Soil Salinity Inversion in Coastal Corn Planting Areas by the Satellite-UAV-Ground Integration Approach." Remote Sensing 13, no. 16 (2021): 3100. http://dx.doi.org/10.3390/rs13163100.

Full text

Abstract:

Soil salinization is a significant factor affecting corn growth in coastal areas. How to use multi-source remote sensing data to achieve the target of rapid, efficient and accurate soil salinity monitoring in a large area is worth further study. In this research, using Kenli District of the Yellow River Delta as study area, the inversion of soil salinity in a corn planting area was carried out based on the integration of ground imaging hyperspectral, unmanned aerial vehicles (UAV) multispectral and Sentinel-2A satellite multispectral images. The UAV and ground images were fused, and the partia

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!