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Bibliografías temáticas / High spatial and spectral remote sensing

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Artículos de revistas

Literatura académica sobre el tema "High spatial and spectral remote sensing"

Autor: Grafiati

Publicado: 11 de marzo de 2023

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Artículos de revistas sobre el tema "High spatial and spectral remote sensing"

1

Rocchini, Duccio. "Ecological Remote Sensing: A Challenging Section on Ecological Theory and Remote Sensing." Remote Sensing 13, no. 5 (2021): 848. http://dx.doi.org/10.3390/rs13050848.

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2

Han, Yanling, Cong Wei, Ruyan Zhou, Zhonghua Hong, Yun Zhang, and Shuhu Yang. "Combining 3D-CNN and Squeeze-and-Excitation Networks for Remote Sensing Sea Ice Image Classification." Mathematical Problems in Engineering 2020 (April 7, 2020): 1–15. http://dx.doi.org/10.1155/2020/8065396.

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Sea ice is one of the most prominent marine disasters in high latitudes. Remote sensing technology provides an effective means for sea ice detection. Remote sensing sea ice images contain rich spectral and spatial information. However, most traditional methods only focus on spectral information or spatial information, and do not excavate the feature of spectral and spatial simultaneously in remote sensing sea ice images classification. At the same time, the complex correlation characteristics among spectra and small sample problem in sea ice classification also limit the improvement of sea ice

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3

Wei, Lifei, Ming Yu, Yajing Liang, et al. "Precise Crop Classification Using Spectral-Spatial-Location Fusion Based on Conditional Random Fields for UAV-Borne Hyperspectral Remote Sensing Imagery." Remote Sensing 11, no. 17 (2019): 2011. http://dx.doi.org/10.3390/rs11172011.

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The precise classification of crop types is an important basis of agricultural monitoring and crop protection. With the rapid development of unmanned aerial vehicle (UAV) technology, UAV-borne hyperspectral remote sensing imagery with high spatial resolution has become the ideal data source for the precise classification of crops. For precise classification of crops with a wide variety of classes and varied spectra, the traditional spectral-based classification method has difficulty in mining large-scale spatial information and maintaining the detailed features of the classes. Therefore, a pre

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4

Duan, Meimei, and Lijuan Duan. "High Spatial Resolution Remote Sensing Data Classification Method Based on Spectrum Sharing." Scientific Programming 2021 (December 20, 2021): 1–12. http://dx.doi.org/10.1155/2021/4356957.

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Existing remote sensing data classification methods cannot achieve the sharing of remote sensing image spectrum, leading to poor fusion and classification of remote sensing data. Therefore, a high spatial resolution remote sensing data classification method based on spectrum sharing is proposed. A page frame recovery algorithm (PFRA) is introduced to allocate the wireless spectrum resources in low-frequency band, and a dynamic spectrum sharing mechanism is designed between the primary and secondary users of remote sensing images. Based on this, D-S evidence theory is used to fuse high spatial

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5

Peng, Mingyuan, Lifu Zhang, Xuejian Sun, Yi Cen, and Xiaoyang Zhao. "A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset." Remote Sensing 12, no. 23 (2020): 3888. http://dx.doi.org/10.3390/rs12233888.

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With the growing development of remote sensors, huge volumes of remote sensing data are being utilized in related applications, bringing new challenges to the efficiency and capability of processing huge datasets. Spatiotemporal remote sensing data fusion can restore high spatial and high temporal resolution remote sensing data from multiple remote sensing datasets. However, the current methods require long computing times and are of low efficiency, especially the newly proposed deep learning-based methods. Here, we propose a fast three-dimensional convolutional neural network-based spatiotemp

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6

Imanian, A., M. H. Tangestani, and A. Asadi. "INVESTIGATION OF SPECTRAL CHARACTERISTICS OF CARBONATE ROCKS – A CASE STUDY ON POSHT MOLEH MOUNT IN IRAN." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 553–57. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-553-2019.

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Abstract. Recent developments in the image processing approaches and the availability of multi and/or hyper spectral remote sensing data with high spectral, spatial and temporal resolutions have made remote sensing technique of great interest in investigations of geological sciences. One of the biggest advantage of the application of remote sensing in geology is recognizing the type of unknown rocks and minerals. In this study, an investigation on spectral features of carbonate rocks (i.e. calcite, dolomite, and dolomitized calcite) were done in terms of main absorptions, the reasons of those

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7

Xu, Qingsong, Xin Yuan, Chaojun Ouyang, and Yue Zeng. "Attention-Based Pyramid Network for Segmentation and Classification of High-Resolution and Hyperspectral Remote Sensing Images." Remote Sensing 12, no. 21 (2020): 3501. http://dx.doi.org/10.3390/rs12213501.

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Unlike conventional natural (RGB) images, the inherent large scale and complex structures of remote sensing images pose major challenges such as spatial object distribution diversity and spectral information extraction when existing models are directly applied for image classification. In this study, we develop an attention-based pyramid network for segmentation and classification of remote sensing datasets. Attention mechanisms are used to develop the following modules: (i) a novel and robust attention-based multi-scale fusion method effectively fuses useful spatial or spectral information at

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8

NanLan, Wang, and Zeng Xiaoyong. "Hyperspectral Data Classification Algorithm considering Spatial Texture Features." Mobile Information Systems 2022 (March 22, 2022): 1–11. http://dx.doi.org/10.1155/2022/9915809.

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As a cutting-edge technology, hyperspectral remote sensing has been widely applied in many fields, including agricultural production, mineral identification, target detection, disaster warning, military reconnaissance, and urban planning. The collected hyperspectral data have high spectral resolution and spatial resolution and are characterized by a large amount of information, redundancy, and high dimension. At the same time, there is a strong correlation between the bands. Therefore, hyperspectral data not only provides rich information but also brings great challenges for subsequent process

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9

Zhao, Rui, and Shihong Du. "Spectral-Spatial Residual Network for Fusing Hyperspectral and Panchromatic Remote Sensing Images." Remote Sensing 14, no. 3 (2022): 800. http://dx.doi.org/10.3390/rs14030800.

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Fusing hyperspectral and panchromatic remote sensing images can obtain the images with high resolution in both spectral and spatial domains. In addition, it can complement the deficiency of high-resolution hyperspectral and panchromatic remote sensing images. In this paper, a spectral–spatial residual network (SSRN) model is established for the intelligent fusion of hyperspectral and panchromatic remote sensing images. Firstly, the spectral–spatial deep feature branches are built to extract the representative spectral and spatial deep features, respectively. Secondly, an enhanced multi-scale r

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10

Shi, Xue, Yu Wang, Yu Li, and Shiqing Dou. "Remote Sensing Image Segmentation Based on Hierarchical Student’s-t Mixture Model and Spatial Constrains with Adaptive Smoothing." Remote Sensing 15, no. 3 (2023): 828. http://dx.doi.org/10.3390/rs15030828.

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Image segmentation is an important task in image processing and analysis but due to the same ground object having different spectra and different ground objects having similar spectra, segmentation, particularly on high-resolution remote sensing images, can be significantly challenging. Since the spectral distribution of high-resolution remote sensing images can have complex characteristics (e.g., asymmetric or heavy-tailed), an innovative image segmentation algorithm is proposed based on the hierarchical Student’s-t mixture model (HSMM) and spatial constraints with adaptive smoothing. Conside

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