A Multiscale and Multidepth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening

By Qiangqiang Yuan, Yancong Wei, Xiangchao Meng, Huanfeng Shen, Liangpei Zhang. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017

580 citations recorded by the source corpus.

Pan-sharpening is a fundamental and significant task in the field of remote sensing imagery processing, in which high-resolution spatial details from panchromatic images are employed to enhance the spatial resolution of multispectral (MS) images. As the transformation from low spatial resolution MS image to high-resolution MS image is complex and highly nonlinear, inspired by the powerful representation for nonlinear relationships of deep neural networks, we introduce multiscale feature extraction and residual learning into the basic convolutional neural network (CNN) architecture and propose the multiscale and multidepth CNN for the pan-sharpening of remote sensing imagery. Both the quantitative assessment results and the visual assessment confirm that the proposed network yields high-resolution MS images that are superior to the images produced by the compared state-of-the-art methods.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/74aa7ae25eebc11eed50db673283e286f0e21dad

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Citations in the corpus

Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review (2022, methodology): “Later, numerous methods inspired by this pioneer work are presented, in which residual learning and dense connection are commonly used (Wei et al., 2017; Yang et al., 2017; Scarpa et al., 2018; Yuan et al., 2018; Peng et al., 2020; Fu et al., 2020; Lei et al., 2021).”
A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods (2021, methodology): “Different training strategies have also been investigated by means of different target loss functions, such as the mean squared error (MSE) [91], 92, [96]– [98], the mean absolute error [95], [99] or the erreur relative globale adimensionnelle de synthése (ERGAS) [100], also using adaptive tuning sc”