A MULTI-TASK ARCHITECTURE FOR REMOTE SENSING BY JOINT SCENE CLASSIFICATION AND IMAGE QUALITY ASSESSMENT

被引：2

作者：

Zhang, Cong ^{[1
,2
]}

Wang, Qi ^{[1
,2
]}

Li, Xuelong ^{[1
,2
]}

机构：

[1] Northwestern Polytech Univ, Sch Comp Sci, Xian 1710072, Shaanxi, Peoples R China

[2] Northwestern Polytech Univ, Ctr OpT IMagery Anal & Learning OPTIMAL, Xian 1710072, Shaanxi, Peoples R China

来源：

2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) | 2019年

基金：

国家重点研发计划; 中国国家自然科学基金;

关键词：

Remote sensing; scene classification; image quality assessment; image super-resolution; multi-task learning; deep learning; CONVOLUTIONAL NETWORK;

D O I：

10.1109/igarss.2019.8898659

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

In this work, we propose a compact multi-task architecture based on deep learning for remote sensing scene classification and image quality assessment (IQA) simultaneously. The model can be trained in an end-to-end manner, and the robustness of classification is improved in our method. More importantly, by exploiting IQA and super-resolution, the accurate classification results can be obtained even if the images are distorted or with low quality. To the best of our knowledge, it is the first successful attempt to associate IQA with scene classification in a unified multi-task architecture. Our method is evaluated on the expanded UC Merced Land-Use dataset after data augmentation. In comparison with some other methods, the experimental results show that the proposed structure makes a great improvement on both classification and IQA.

引用

页码：10051 / 10054

页数：4

共 13 条

[11] iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning [J].