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Journal articles on the topic 'Imagerie hyperspectrale large bande'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Krupiński, Michał, Anna Wawrzaszek, Wojciech Drzewiecki, Małgorzata Jenerowicz, and Sebastian Aleksandrowicz. "What Can Multifractal Analysis Tell Us about Hyperspectral Imagery?" Remote Sensing 12, no. 24 (2020): 4077. http://dx.doi.org/10.3390/rs12244077.

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Hyperspectral images provide complex information about the Earth’s surface due to their very high spectral resolution (hundreds of spectral bands per pixel). Effective processing of such a large amount of data requires dedicated analysis methods. Therefore, this research applies, for the first time, the degree of multifractality to the global description of all spectral bands of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. Subsets of four hyperspectral images, presenting four landscape types, are analysed. In particular, we verify whether multifractality can be detected in all

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Seydi, S. T., and M. Hasanlou. "LAND COVER CHANGE DETECTION BASED ON GENETICALLY FEATURE AELECTION AND IMAGE ALGEBRA USING HYPERION HYPERSPECTRAL IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 669–73. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-669-2015.

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The Earth has always been under the influence of population growth and human activities. This process causes the changes in land use. Thus, for optimal management of the use of resources, it is necessary to be aware of these changes. Satellite remote sensing has several advantages for monitoring land use/cover resources, especially for large geographic areas. Change detection and attribution of cultivation area over time present additional challenges for correctly analyzing remote sensing imagery. In this regards, for better identifying change in multi temporal images we use hyperspectral imag

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Qin, Jinchun, Hongrui Zhao, and Bing Liu. "Self-Supervised Denoising for Real Satellite Hyperspectral Imagery." Remote Sensing 14, no. 13 (2022): 3083. http://dx.doi.org/10.3390/rs14133083.

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Satellite hyperspectral remote sensing has gradually become an important means of Earth observation, but the existence of various types of noise seriously limits the application value of satellite hyperspectral images. With the continuous development of deep learning technology, breakthroughs have been made in improving hyperspectral image denoising algorithms based on supervised learning; however, these methods usually require a large number of clean/noisy training pairs, a target that is difficult to meet for real satellite hyperspectral imagery. In this paper, we propose a self-supervised l

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Jiang, Guo, Shuguang Zhou, Shichao Cui, et al. "Exploring the Potential of HySpex Hyperspectral Imagery for Extraction of Copper Content." Sensors 20, no. 21 (2020): 6325. http://dx.doi.org/10.3390/s20216325.

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Detritus geochemical information has been proven through research to be an effective prospecting method in mineral exploration. However, the traditional detritus metal content monitoring methods based on field sampling and laboratory chemical analysis are time-consuming and may not meet the requirements of large-scale metal content monitoring. In this study, we obtained 95 detritus samples and seven HySpex hyperspectral imagery scenes with a spatial resolution of 1 m from Karatag Gobi area, Xinjiang, China, and used partial least squares and wavebands selection methods to explore the usefulnes

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Ran, Qiong, Hong Zhong, Xu Sun, Degang Wang, and He Sun. "Large-Kernel Central Block Masked Convolution and Channel Attention-Based Reconstruction Network for Anomaly Detection of High-Resolution Hyperspectral Imagery." Remote Sensing 16, no. 22 (2024): 4125. http://dx.doi.org/10.3390/rs16224125.

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In recent years, the rapid advancement of drone technology has led to an increasing use of drones equipped with hyperspectral sensors for ground imaging. Hyperspectral data captured via drones offer significantly higher spatial resolution, but this also introduces more complex background details and larger target scales in high-resolution hyperspectral imagery (HRHSI), posing substantial challenges for hyperspectral anomaly detection (HAD). Mainstream reconstruction-based deep learning methods predominantly emphasize spatial local information in hyperspectral images (HSIs), relying on small sp

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Ye, Bei, Shufang Tian, Qiuming Cheng, and Yunzhao Ge. "Application of Lithological Mapping Based on Advanced Hyperspectral Imager (AHSI) Imagery Onboard Gaofen-5 (GF-5) Satellite." Remote Sensing 12, no. 23 (2020): 3990. http://dx.doi.org/10.3390/rs12233990.

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The Advanced Hyperspectral Imager (AHSI), carried by the Gaofen-5 (GF-5) satellite, is the first hyperspectral sensor that simultaneously offers broad coverage and a broad spectrum. Meanwhile, deep-learning-based approaches are emerging to manage the growing volume of data produced by satellites. However, the application potential of GF-5 AHSI imagery in lithological mapping using deep-learning-based methods is currently unknown. This paper assessed GF-5 AHSI imagery for lithological mapping in comparison with Shortwave Infrared Airborne Spectrographic Imager (SASI) data. A multi-scale 3D deep

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Misbah, Khalil, Ahmed Laamrani, Paul Voroney, Keltoum Khechba, Raffaele Casa, and Abdelghani Chehbouni. "Ensemble Band Selection for Quantification of Soil Total Nitrogen Levels from Hyperspectral Imagery." Remote Sensing 16, no. 14 (2024): 2549. http://dx.doi.org/10.3390/rs16142549.

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Total nitrogen (TN) is a critical nutrient for plant growth, and its monitoring in agricultural soil is vital for farm managers. Traditional methods of estimating soil TN levels involve laborious and costly chemical analyses, especially when applied to large areas with multiple sampling points. Remote sensing offers a promising alternative for identifying, tracking, and mapping soil TN levels at various scales, including the field, landscape, and regional levels. Spaceborne hyperspectral sensing has shown effectiveness in reflecting soil TN levels. This study evaluates the efficiency of spectr

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Vangi, Elia, Giovanni D’Amico, Saverio Francini, et al. "The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination." Sensors 21, no. 4 (2021): 1182. http://dx.doi.org/10.3390/s21041182.

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Different forest types based on different tree species composition may have similar spectral signatures if observed with traditional multispectral satellite sensors. Hyperspectral imagery, with a more continuous representation of their spectral behavior may instead be used for their classification. The new hyperspectral Precursore IperSpettrale della Missione Applicativa (PRISMA) sensor, developed by the Italian Space Agency, is able to capture images in a continuum of 240 spectral bands ranging between 400 and 2500 nm, with a spectral resolution smaller than 12 nm. The new sensor can be emplo

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Olsoy, P. J., S. N. Barrett, B. C. Robb, et al. "SCALING UP SAGEBRUSH CHEMISTRY WITH NEAR-INFRARED SPECTROSCOPY AND UAS-ACQUIRED HYPERSPECTRAL IMAGERY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-3-2021 (August 10, 2021): 127–32. http://dx.doi.org/10.5194/isprs-archives-xliv-m-3-2021-127-2021.

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Abstract. Sagebrush ecosystems (Artemisia spp.) face many threats including large wildfires and conversion to invasive annuals, and thus are the focus of intense restoration efforts across the western United States. Specific attention has been given to restoration of sagebrush systems for threatened herbivores, such as Greater Sage-Grouse (Centrocercus urophasianus) and pygmy rabbits (Brachylagus idahoensis), reliant on sagebrush as forage. Despite this, plant chemistry (e.g., crude protein, monoterpenes and phenolics) is rarely considered during reseeding efforts or when deciding which areas

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Shaik, Riyaaz Uddien, Shoba Periasamy, and Weiping Zeng. "Potential Assessment of PRISMA Hyperspectral Imagery for Remote Sensing Applications." Remote Sensing 15, no. 5 (2023): 1378. http://dx.doi.org/10.3390/rs15051378.

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Hyperspectral imagery plays a vital role in precision agriculture, forestry, environment, and geological applications. Over the past decade, extensive research has been carried out in the field of hyperspectral remote sensing. First introduced by the Italian Space Agency ASI in 2019, space-borne PRISMA hyperspectral imagery (PHSI) is taking the hyperspectral remote sensing research community into the next era due to its unprecedented spectral resolution of ≤12 nm. Given these abundant free data and high spatial resolution, it is crucial to provide remote sensing researchers with information ab

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Chen, Nian, Kezhong Lu, and Hao Zhou. "A Search Method for Optimal Band Combination of Hyperspectral Imagery Based on Two Layers Selection Strategy." Computational Intelligence and Neuroscience 2021 (June 22, 2021): 1–14. http://dx.doi.org/10.1155/2021/5592323.

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A band selection method based on two layers selection (TLS) strategy, which forms an optimal subset from all-bands set to reconstitute the original hyperspectral imagery (HSI) and aims to cost a fewer bands for better performances, is proposed in this paper. As its name implies, TLS picks out the bands with low correlation and a large amount of information into the target set to reach dimensionality reduction for HSI via two phases. Specifically, the fast density peaks clustering (FDPC) algorithm is used to select the most representative node in each cluster to build a candidate set at first.

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Deng, Chubo, Yi Cen, and Lifu Zhang. "Learning-Based Hyperspectral Imagery Compression through Generative Neural Networks." Remote Sensing 12, no. 21 (2020): 3657. http://dx.doi.org/10.3390/rs12213657.

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Hyperspectral images (HSIs), which obtain abundant spectral information for narrow spectral bands (no wider than 10 nm), have greatly improved our ability to qualitatively and quantitatively sense the Earth. Since HSIs are collected by high-resolution instruments over a very large number of wavelengths, the data generated by such sensors is enormous, and the amount of data continues to grow, HSI compression technique will play more crucial role in this trend. The classical method for HSI compression is through compression and reconstruction methods such as three-dimensional wavelet-based techn

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Gao, Bo-Cai, and Rong-Rong Li. "Correction of Sunglint Effects in High Spatial Resolution Hyperspectral Imagery Using SWIR or NIR Bands and Taking Account of Spectral Variation of Refractive Index of Water." Advances in Environmental and Engineering Research 02, no. 03 (2021): 1. http://dx.doi.org/10.21926/aeer.2103017.

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Passive high spatial resolution hyperspectral and multispectral imaging systems in the solar spectral region from aircraft and satellite platforms are being increasingly used for remote sensing of coastal waters and inland lakes. However, the remotely sensed data are often contaminated by the specular reflection of solar radiation at the air/water interface. Thus, the effects of sunglint need to be corrected. The purpose of sunglint correction is to remove the undesired specular reflected portion of radiance that does not carry any information about the water body and its bottom surface proper

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Jia, Wen, Yong Pang, Riccardo Tortini, Daniel Schläpfer, Zengyuan Li, and Jean-Louis Roujean. "A Kernel-Driven BRDF Approach to Correct Airborne Hyperspectral Imagery over Forested Areas with Rugged Topography." Remote Sensing 12, no. 3 (2020): 432. http://dx.doi.org/10.3390/rs12030432.

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Airborne hyper-spectral imaging has been proven to be an efficient means to provide new insights for the retrieval of biophysical variables. However, quantitative estimates of unbiased information derived from airborne hyperspectral measurements primarily require a correction of the anisotropic scattering properties of the land surface depicted by the bidirectional reflectance distribution function (BRDF). Hitherto, angular BRDF correction methods rarely combined viewing-illumination geometry and topographic information to achieve a comprehensive understanding and quantification of the BRDF ef

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López, Josué, Deni Torres, Stewart Santos, and Clement Atzberger. "Spectral Imagery Tensor Decomposition for Semantic Segmentation of Remote Sensing Data through Fully Convolutional Networks." Remote Sensing 12, no. 3 (2020): 517. http://dx.doi.org/10.3390/rs12030517.

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This work aims at addressing two issues simultaneously: data compression at input space and semantic segmentation. Semantic segmentation of remotely sensed multi- or hyperspectral images through deep learning (DL) artificial neural networks (ANN) delivers as output the corresponding matrix of pixels classified elementwise, achieving competitive performance metrics. With technological progress, current remote sensing (RS) sensors have more spectral bands and higher spatial resolution than before, which means a greater number of pixels in the same area. Nevertheless, the more spectral bands and

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16

Gaffney, Rowan, David J. Augustine, Sean P. Kearney, and Lauren M. Porensky. "Using Hyperspectral Imagery to Characterize Rangeland Vegetation Composition at Process-Relevant Scales." Remote Sensing 13, no. 22 (2021): 4603. http://dx.doi.org/10.3390/rs13224603.

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Rangelands are composed of patchy, highly dynamic herbaceous plant communities that are difficult to quantify across broad spatial extents at resolutions relevant to their characteristic spatial scales. Furthermore, differentiation of these plant communities using remotely sensed observations is complicated by their similar spectral absorption profiles. To better quantify the impacts of land management and weather variability on rangeland vegetation change, we analyzed high resolution hyperspectral data produced by the National Ecological Observatory Network (NEON) at a 6500-ha experimental st

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17

Raya-Sereno, María D., J. Ivan Ortiz-Monasterio, María Alonso-Ayuso, et al. "High-Resolution Airborne Hyperspectral Imagery for Assessing Yield, Biomass, Grain N Concentration, and N Output in Spring Wheat." Remote Sensing 13, no. 7 (2021): 1373. http://dx.doi.org/10.3390/rs13071373.

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Remote sensing allows fast assessment of crop monitoring over large areas; however, questions regarding uncertainty in crop parameter prediction and application to nitrogen (N) fertilization remain open. The objective of this study was to optimize of remote sensing spectral information for its application to grain yield (GY), biomass, grain N concentration (GNC), and N output assessment, and decision making on spring wheat fertilization. Spring wheat (Triticum turgidum L.) field experiments testing two tillage treatments, two irrigation levels and six N treatments were conducted in Northwest M

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18

Jeevanand, N., P. A. Verma, and S. Saran. "FUSION OF HYPERSPECTRAL AND MULTISPECTRAL IMAGERY WITH REGRESSION KRIGING AND THE LULU OPERATORS; A COMPARISON." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 583–88. http://dx.doi.org/10.5194/isprs-archives-xlii-5-583-2018.

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<p><strong>Abstract.</strong> In this digital world, there is a large requirement of high resolution satellite image. Images at a low resolution may contain relevant information that has to be integrated with the high resolution image to obtain the required information. This is being fulfilled by image fusion. Image fusion is merging of different resolution images into a single image. The output image contains more information, as the information is integrated from both the images Image fusion was conducted with two different algorithms: regression kriging and the LULU operat

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Olivetti, Diogo, Henrique Roig, Jean-Michel Martinez, et al. "Low-Cost Unmanned Aerial Multispectral Imagery for Siltation Monitoring in Reservoirs." Remote Sensing 12, no. 11 (2020): 1855. http://dx.doi.org/10.3390/rs12111855.

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The recent and continuous development of unmanned aerial vehicles (UAV) and small cameras with different spectral resolutions and imaging systems promotes new remote sensing platforms that can supply ultra-high spatial and temporal resolution, filling the gap between ground-based surveys and orbital sensors. This work aimed to monitor siltation in two large rural and urban reservoirs by recording water color variations within a savanna biome in the central region of Brazil using a low cost and very light unmanned platform. Airborne surveys were conducted using a Parrot Sequoia camera (~0.15 kg

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Bergmüller, Kai O., and Mark C. Vanderwel. "Predicting Tree Mortality Using Spectral Indices Derived from Multispectral UAV Imagery." Remote Sensing 14, no. 9 (2022): 2195. http://dx.doi.org/10.3390/rs14092195.

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Past research has shown that remotely sensed spectral information can be used to predict tree health and vitality. Recent developments in unmanned aerial vehicles (UAVs) have now made it possible to derive such information at the tree and stand scale from high-resolution imagery. We used visible and multispectral bands from UAV imagery to calculate a set of spectral indices for 52,845 individual tree crowns within 38 forest stands in western Canada. We then used those indices to predict the mortality of these canopy trees over the following year. We evaluated whether including multispectral in

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Akrem, Sellami, Riadh Farah Imed, and Solaiman Basel. "An Adaptive Dimensionality Reduction Approach for Hyperspectral Imagery Semantic Interpretation." January 3, 2017. https://doi.org/10.5281/zenodo.1128851.

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With the development of HyperSpectral Imagery (HSI) technology, the spectral resolution of HSI became denser, which resulted in large number of spectral bands, high correlation between neighboring, and high data redundancy. However, the semantic interpretation is a challenging task for HSI analysis due to the high dimensionality and the high correlation of the different spectral bands. In fact, this work presents a dimensionality reduction approach that allows to overcome the different issues improving the semantic interpretation of HSI. Therefore, in order to preserve the spatial information,

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Akrem, Sellami, and Riadh Farah Imed. "A Spatial Hypergraph Based Semi-Supervised Band Selection Method for Hyperspectral Imagery Semantic Interpretation." September 1, 2016. https://doi.org/10.5281/zenodo.1127070.

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Hyperspectral imagery (HSI) typically provides a wealth of information captured in a wide range of the electromagnetic spectrum for each pixel in the image. Hence, a pixel in HSI is a high-dimensional vector of intensities with a large spectral range and a high spectral resolution. Therefore, the semantic interpretation is a challenging task of HSI analysis. We focused in this paper on object classification as HSI semantic interpretation. However, HSI classification still faces some issues, among which are the following: The spatial variability of spectral signatures, the high number of spectr

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Pazhanivelan, S., A. T. Shyam, K. P. Ragunath, N. S. Sudarmanian, S. Satheesh, and S. Sakthivel. "Advanced mapping of mango orchards in Chittoor district through object-based image analysis approach using Sentinel-2 imagery." Plant Science Today, May 10, 2025. https://doi.org/10.14719/pst.6889.

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Mango is crucial in India's agricultural economy, particularly in the Chittoor District of Andhra Pradesh. As mango cultivation transitions from subsistence farming to large-scale commercial operations, accurate mapping and monitoring of mango plantations are essential for sustainable agricultural management. This study explored the application of Object-Based Image Analysis (OBIA) using high-resolution Sentinel-2 satellite imagery for mapping mango plantations in Chittoor District. OBIA, a more advanced approach than traditional pixel- based methods, integrates spectral, spatial and contextua

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Naicker, Rowan, Onisimo Mutanga, Kabir Peerbhay, and Omosalewa Odebiri. "Estimating high-density aboveground biomass within a complex tropical grassland using Worldview-3 imagery." Environmental Monitoring and Assessment 196, no. 4 (2024). http://dx.doi.org/10.1007/s10661-024-12476-7.

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AbstractA large percentage of native grassland ecosystems have been severely degraded as a result of urbanization and intensive commercial agriculture. Extensive nitrogen-based fertilization regimes are widely used to rehabilitate and boost productivity in these grasslands. As a result, modern management frameworks rely heavily on detailed and accurate information on vegetation condition to monitor the success of these interventions. However, in high-density environments, biomass signal saturation has hampered detailed monitoring of rangeland condition. This issue stems from traditional broad-

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