A SUB-PIXEL MAPPING METHOD BASED ON LOGISTIC REGRESSION AND PIXEL-SWAPPING MODEL

被引：0
|
作者：
Su, Lijuan [1 ]
Xu, Yue [1 ]
Yuan, Yan [1 ]
Yang, Jingyi [1 ]
机构：
[1] Beihang Univ, Sch Instrumentat & Optoelect Engn, Beijing, Peoples R China
来源：
2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) | 2019年
基金：
中国国家自然科学基金;
关键词：
remote sensing image; sub-pixel mapping; Logistic Regression; Pixel-Swapping Model; NEURAL-NETWORK; LAND-COVER;
D O I：
10.1109/igarss.2019.8898178
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
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.
引用
收藏
页码：572 / 575
页数：4
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