Results 11 to 20 of about 7,196 (229)

Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods

Remote Sensing, 2017
Decision tree classification is one of the most efficient methods for obtaining land use/land cover (LULC) information from remotely sensed imageries. However, traditional decision tree classification methods cannot effectively eliminate the influence of
Chao Yang, Guofeng Wu, Kai Ding, Tiezhu Shi, Qingquan Li, Jinliang Wang   +5 more
doaj   +2 more sources

Study on the Effect of Surface Roughness on the Spectral Unmixing of Mixed Pixels

Journal of Spectroscopy, 2020
In the spectrum measurement experiment, the roughness of the object surface is an essential factor that cannot be ignored. In this experiment, a group of mixed pixel samples with different mixing ratios were designed, and these samples were printed on ...
Haonan Zhang, Xingping Wen, Junlong Xu, Dayou Luo, Ping He   +4 more
doaj   +2 more sources

Hyperspectral Image Restoration via Spatial-Spectral Residual Total Variation Regularized Low-Rank Tensor Decomposition

Remote Sensing, 2022
To eliminate the mixed noise in hyperspectral images (HSIs), three-dimensional total variation (3DTV) regularization has been proven as an efficient tool. However, 3DTV regularization is prone to losing image details in restoration. To resolve this issue,
Xiangyang Kong, Yongqiang Zhao, Jonathan Cheung-Wai Chan, Jize Xue   +3 more
doaj   +2 more sources

Information Extraction from Satellite-Based Polarimetric SAR Data Using Simulated Annealing and SIRT Methods and GPU Processing

Energies, 2021
The main goal of this research was to propose a new method of polarimetric SAR data decomposition that will extract additional polarimetric information from the Synthetic Aperture Radar (SAR) images compared to other existing decomposition methods.
Stanisława Porzycka-Strzelczyk, Jacek Strzelczyk, Kamil Szostek, Maciej Dwornik, Andrzej Leśniak, Justyna Bała, Anna Franczyk   +6 more
doaj   +2 more sources

Weighted Nonlocal Low-Rank Tensor Decomposition Method for Sparse Unmixing of Hyperspectral Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
The low spatial resolution of hyperspectral images leads to the coexistence of multiple ground objects in a single pixel (called mixed pixels). A large number of mixed pixels in a hyperspectral image hinders the subsequent analysis and application of the
Le Sun, Feiyang Wu, Tianming Zhan, Wei Liu, Jin Wang, Byeungwoo Jeon   +5 more
doaj   +2 more sources

Spectral-Smoothness and Non-Local Self-Similarity Regularized Subspace Low-Rank Learning Method for Hyperspectral Mixed Denoising

Remote Sensing, 2021
During the imaging process, hyperspectral image (HSI) is inevitably affected by various noises, such as Gaussian noise, impulse noise, stripes or deadlines.
Wei Liu, Chengxun He, Le Sun
doaj   +2 more sources

Nighttime Single Image Dehazing Based on the Structural Patch Decomposition

IEEE Access, 2021
Images acquired from the outdoors are often degraded owing to bad weather conditions, such as haze, fog, and rain. Various algorithms for dehazing daytime images have been introduced to eliminate the effects of haze.
Soonyoung Hong, Minsub Kim, Haegeun Lee, Moon Gi Kang   +3 more
doaj   +2 more sources

Nearest Neighborhood Grayscale Operator for Hardware-Efficient Microscale Texture Extraction

EURASIP Journal on Advances in Signal Processing, 2007
First-stage feature computation and data rate reduction play a crucial role in an efficient visual information processing system. Hardware-based first stages usually win out where power consumption, dynamic range, and speed are the issue, but have ...
Andreas König, Christian Mayr
doaj   +3 more sources

Hyperspectral Image Denoising via $L_{0}$ Regularized Low-Rank Tucker Decomposition

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
This article studies the mixed noise removal problem for hyperspectral images (HSIs), which often suffer from Gaussian noise and sparse noise. Conventional denoising models mainly employ the $L_{1}$-norm-based regularizers to remove sparse noise and ...
Xin Tian, Kun Xie, Hanling Zhang
doaj   +2 more sources

Spatially and temporally continuous LAI datasets based on the mixed pixel decomposition method. [PDF]

Springerplus, 2016
Zhao J, Wang Y, Zhang H, Zhang Z, Guo X, Yu S, Du W.   +6 more
europepmc   +2 more sources