Land-Use Scene Classification in High-Resolution Remote Sensing Images Using Improved Correlatons

2015 ◽  
Vol 12 (12) ◽  
pp. 2403-2407 ◽  
Author(s):  
Kunlun Qi ◽  
Huayi Wu ◽  
Chen Shen ◽  
Jianya Gong
2012 ◽  
Vol 518-523 ◽  
pp. 5788-5792
Author(s):  
Zheng Dong Xie ◽  
Jian Zhang ◽  
Bu Zhuo Peng

The paper was supported by The Second Land Investigation Item and took Nanjing city, Jiangsu Province as a case study. The research of the theory, technique and application for land use investigation was achieved by the high-resolution remote sensing images for application, designed a set of technique of land use investigation for land property right management. The database and platform system were established to carry out the dynamic management of land use. Based on the summarization of the correlative studies, The paper designed a set of technique of land investigation for land property right management and also designed the technical process, dealt with the remote sensing images, detected the changed information, classified the land, investigated the land property right and established the database to serve for the management of land property right. And it has been successfully used in Nanjing. It’s unique to use the high-resolution remote sensing images by QuichBird for the scale of 1:5000 in land use investigation in area cities which is also the first time in Nanjing City.


2018 ◽  
Vol 06 (11) ◽  
pp. 185-193
Author(s):  
Feng’an Zhao ◽  
Xiongmei Zhang ◽  
Xiaodong Mu ◽  
Zhaoxiang Yi ◽  
Zhou Yang

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Linyi Li ◽  
Tingbao Xu ◽  
Yun Chen

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.


2019 ◽  
Vol 13 (04) ◽  
pp. 1
Author(s):  
Xin Zhang ◽  
Yongcheng Wang ◽  
Ning Zhang ◽  
Dongdong Xu ◽  
Bo Chen ◽  
...  

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