@inproceedings{fedd7b7971934ba5ba4e0648b5b349ba,
title = "Multi-scale-and-depth convolutional neural network for remote sensed imagery pan-sharpening",
abstract = "Pan-sharpening is a fundamental and significant task in the field of remote sensed imagery fusion, which demands fusion of panchromatic and multi-spectral images with the rich information accurately preserved in both spatial and spectral domains. In this paper, to overcome the drawbacks of traditional pan-sharpening methodologies, we employed the advanced concept of deep learning to propose a Multi-Scale-and-Depth Convolutional Neural Network (MSDCNN) as an end-to-end pan-sharpening model. By the results of a large number of quantitative and visual assessments, the qualities of images fused by the proposed network have been confirmed superior to compared state-of-the-art methods.",
keywords = "Convolutional neural network, Deep learning, Pan-sharpening, Remote Sensing, Residual Learning",
author = "Yancong Wei and Qiangqiang Yuan and Xiangchao Meng and Huanfeng Shen and Liangpei Zhang and Michael Ng",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 ; Conference date: 23-07-2017 Through 28-07-2017",
year = "2017",
month = dec,
day = "1",
doi = "10.1109/IGARSS.2017.8127731",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "IEEE",
pages = "3413--3416",
booktitle = "2017 IEEE International Geoscience and Remote Sensing Symposium",
address = "United States",
}