Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Genping Zhao"'
Publikováno v:
Remote Sensing, Vol 16, Iss 17, p 3246 (2024)
Infrared and visible image fusion integrates complementary information from different modalities into a single image, providing sufficient imaging information for scene interpretation and downstream target recognition tasks. However, existing fusion
Externí odkaz:
https://doaj.org/article/1f1151fc3ae14a70b03d5765556edf80
Publikováno v:
Canadian Journal of Remote Sensing, Vol 49, Iss 1 (2023)
Gabor filter is widely used to extract spatial texture features of hyperspectral images (HSI) for HSI classification; however, a single Gabor filter cannot obtain the complete image features. In the paper, we propose an HSI classification method that
Externí odkaz:
https://doaj.org/article/5072d67cd437480594259f4cf3e7fa2f
Publikováno v:
Remote Sensing, Vol 14, Iss 19, p 4944 (2022)
The use of multi-mode remote sensing data for biomass prediction is of potential value to aid planting management and yield maximization. In this study, an advanced biomass estimation approach for sugarcane fields is proposed based on multi-source re
Externí odkaz:
https://doaj.org/article/3abd3a4e1d0349f9b1e82c2d207b0c2f
Publikováno v:
Canadian Journal of Remote Sensing, Vol 44, Iss 5, Pp 476-490 (2018)
In recent years, the spatial texture features obtained by filtering have become a hot research topic to improve hyperspectral image classification, but spatial correlation information is often lost in spatial texture information extraction. To solve
Externí odkaz:
https://doaj.org/article/1f2ad20d101c4264a1f4065730d5413b
Publikováno v:
Remote Sensing, Vol 14, Iss 1, p 87 (2021)
Unmanned air vehicle (UAV) based imaging has been an attractive technology to be used for wind turbine blades (WTBs) monitoring. In such applications, image motion blur is a challenging problem which means that motion deblurring is of great significa
Externí odkaz:
https://doaj.org/article/5be926ab806246cdbfd359b83247fbbc
Publikováno v:
Remote Sensing, Vol 13, Iss 20, p 4102 (2021)
Hyperspectral images (HSIs) often contain pixels with mixed spectra, which makes it difficult to accurately separate the background signal from the anomaly target signal. To mitigate this problem, we present a method that applies spectral unmixing an
Externí odkaz:
https://doaj.org/article/19af615c04394aceb1d0378b87a1a542
Publikováno v:
Remote Sensing, Vol 13, Iss 21, p 4312 (2021)
Point cloud classification plays a significant role in Light Detection and Ranging (LiDAR) applications. However, most available multi-scale feature learning networks for large-scale 3D LiDAR point cloud classification tasks are time-consuming. In th
Externí odkaz:
https://doaj.org/article/1e0a845ab8e6467bb45e5028053e439e
Publikováno v:
Information, Vol 12, Iss 10, p 406 (2021)
The datasets in the latest semantic segmentation model often need to be manually labeled for each pixel, which is time-consuming and requires much effort. General models are unable to make better predictions, for new categories of information that ha
Externí odkaz:
https://doaj.org/article/cc19a3f8f1d24790a0afa6d4597ca47b
Publikováno v:
Remote Sensing, Vol 13, Iss 19, p 3830 (2021)
Accurate estimation of the degree of regeneration in tropical dry forest (TDF) is critical for conservation policymaking and evaluation. Hyperspectral remote sensing and light detection and ranging (LiDAR) have been used to characterize the determini
Externí odkaz:
https://doaj.org/article/2f700d19c3254b02a8d69e28398767fd
Publikováno v:
European Journal of Remote Sensing, Vol 50, Iss 1, Pp 47-63 (2017)
It is of great interest in exploiting spectral-spatial information for hyperspectral image (HSI) classification at different spatial resolutions. This paper proposes a new spectral-spatial deep learning-based classification paradigm. First, pixel-bas
Externí odkaz:
https://doaj.org/article/faa638bda2604ab5b97e5c23b587836a