Results 91 to 100 of about 1,803 (195)

Testing the Impact of Pansharpening Using PRISMA Hyperspectral Data: A Case Study Classifying Urban Trees in Naples, Italy

Remote Sensing
Urban trees support vital ecological functions and help with the mitigation of and adaption to climate change. Yet, their monitoring and management require significant public resources. remote sensing could facilitate these tasks.
Miriam Perretta, Gabriele Delogu, Cassandra Funsten, Alessio Patriarca, Eros Caputi, Lorenzo Boccia   +5 more
doaj   +1 more source

Hyperspectral Pansharpening: Review and Future Perspectives

In this paper, a representative set of state-of-the-art methods for hyperspectral pansharpening, comprising both model- and deep learning-based ones, are reviewed and compared on four datasets from the PRISMA mission.
Scarpa G., Guarino G., Plaza A., Vivone G., Chanussot J., Ciotola M.   +5 more
core   +1 more source

Performance Evaluation of Pansharpening Methods on GPU for RASAT Images

, 2015
Turkey is among the countries which could develop earth observation satellites. RASAT and Gokturk-2 satellites are still operational and continuously acquire images of the Earth. Their images are processed before sharing with end users. Pansharpening, at
Acikgoz, Ibrahim Serdar, KUTBAY, UĞURHAN, HARDALAÇ, FIRAT, Teke, Mustafa   +3 more
core   +1 more source

Pansharpening of remote sensing images using dominant pixels

Pansharpening refers to the synthesis of super-resolution multispectral images (i.e., pansharpened images, PIs) designed to overcome the technical constraints of Earth Observation Satellites.
Civicioglu, Pinar, BEŞDOK, ERKAN
core   +1 more source

A novel pansharpening: when multiscale adaptive attention meets dual enhanced networks and low-rank tensor decomposition

International Journal of Digital Earth
Deep learning (DL) has achieved advanced results in pansharpening remote sensing images. However, existing DL-based methods still have several drawbacks, such as incomplete feature extraction and insufficient information fusion.
Hengyou Wang, Jie Zhang, Tingting Zhang, Zheng Zhang, Lianzhi Huo   +4 more
doaj   +1 more source

Pansharpening largely preserves the normalized difference vegetation index: a multi-sensor comparison with PRISMA, Landsat 9, and field spectroscopy

Frontiers in Remote Sensing
Pansharpening is widely used to increase the spatial resolution of satellite imagery, yet its effects on vegetation indices derived from hyperspectral sensors remain poorly quantified.
Jan Procházka, Jan Komárek, David Moravec, Jiří Rous, Tomáš Klouček   +4 more
doaj   +1 more source

A Progressive Spectral Correction and Spatial Compensation Network for Pansharpening

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Pansharpening aims to produce a high resolution multispectral image by fusing a panchromatic image with a low-resolution multispectral image. Current pansharpening methods often overlook the significant modality differences between source images and lack
Rixian Liu, Hangyuan Lu, Biwei Chi, Yong Yang, Shuying Huang   +4 more
doaj   +1 more source

A spectral preserved model based on spectral contribution and dependence with detail injection for pansharpening. [PDF]

Sci Rep, 2023
Wu L, Jiang X, Peng J, Wu G, Xiong X.
europepmc   +1 more source

Pansharpening Model of Transferable Remote Sensing Images Based on Feature Fusion and Attention Modules. [PDF]

Sensors (Basel), 2023
Liu H, Deng L, Dou Y, Zhong X, Qian Y.
europepmc   +1 more source

DSConv: Dynamic Splitting Convolution for Pansharpening

CoRR
The content of the paper is not yet fully developed, and the proposed approach requires further optimization. Additionally, the experimental results are incomplete and need to be supplemented.
Liu, Xuanyu, An, Bonan
openaire   +3 more sources