@inproceedings{387fc2bd05f1441bb9684a35d49c6a3f,
title = "A Sub-Pixel Mapping Method Based on Logistic Regression and Pixel-Swapping Model",
abstract = "Mixed pixels are widely existed in remote sensing data. Using the proportion of different land-covers to improve the spatial resolution of hyperspectral images is a popular method in the field of remote sensing data processing. The proportion data and location of sub-pixels in geometrical shapes can be used as the training data to train the neural network. The trained model can be used to sub-pixel mapping for the real land image. This paper proposed a sub-pixel mapping method based on Logistic Regression and Pixel-Swapping Model (LRPSM). The artificial image and real land image taken by Landsat8 were used to be tested. Experiments showed that the accuracy of LRPSM outperformed PSM based on sub-pixel spatial attraction model and BPNN based on neural network model in sub-pixel mapping.",
keywords = "Logistic Regression, Pixel-Swapping Model, remote sensing image, sub-pixel mapping",
author = "Lijuan Su and Yue Xu and Yan Yuan and Jingyi Yang",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",
year = "2019",
month = jul,
doi = "10.1109/IGARSS.2019.8898178",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "572--575",
booktitle = "2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings",
address = "美国",
}