Discussion on Remote Dynamic Sensing Monitoring Method for Land in Regions under Rapid Economic Development

2012 ◽  
Vol 518-523 ◽  
pp. 5788-5792
Author(s):  
Zheng Dong Xie ◽  
Jian Zhang ◽  
Bu Zhuo Peng
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
High Resolution ◽  
Property Right ◽  
Remote Sensing Images ◽  
Monitoring Method ◽  
Nanjing City ◽  
Platform System ◽  
First Time

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.

scholarly journals Research on Cultivated Land Change Based on GIS and High Resolution Remote Sensing Images

2018 ◽  
Vol 228 ◽  
pp. 02013
Author(s):  
Haibo Yu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Automatic Monitoring ◽  
Research Area ◽  
Decision Fusion ◽  
Land Change ◽  
Fuzzy Decision ◽  
Remote Sensing Images ◽  
Monitoring Method ◽  
Gis Data

This paper study an automatic monitoring method for land change based on high resolution remote sensing images and GIS data, and we use three classification methods to classify and fuse the research area. Secondly, the paper calculates the corresponding map class components and compares them with their historical attributes; it can automatically monitor land use change. The experimental results show that the fuzzy decision fusion classification can significantly improve the classification effect, and it can accurately determine the change area accurately and automatically. However, there are some partial errors in the region.

scholarly journals Mapping Land Use from High Resolution Satellite Images by Exploiting the Spatial Arrangement of Land Cover Objects

2020 ◽  
Vol 12 (24) ◽  
pp. 4158
Author(s):  
Mengmeng Li ◽  
Alfred Stein
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Spatial Arrangement ◽  
Remote Sensing Images ◽  
Land Use Types ◽  
High Resolution Satellite Images

Spatial information regarding the arrangement of land cover objects plays an important role in distinguishing the land use types at land parcel or local neighborhood levels. This study investigates the use of graph convolutional networks (GCNs) in order to characterize spatial arrangement features for land use classification from high resolution remote sensing images, with particular interest in comparing land use classifications between different graph-based methods and between different remote sensing images. We examine three kinds of graph-based methods, i.e., feature engineering, graph kernels, and GCNs. Based upon the extracted arrangement features and features regarding the spatial composition of land cover objects, we formulated ten land use classifications. We tested those on two different remote sensing images, which were acquired from GaoFen-2 (with a spatial resolution of 0.8 m) and ZiYuan-3 (of 2.5 m) satellites in 2020 on Fuzhou City, China. Our results showed that land use classifications that are based on the arrangement features derived from GCNs achieved the highest classification accuracy than using graph kernels and handcrafted graph features for both images. We also found that the contribution to separating land use types by arrangement features varies between GaoFen-2 and ZiYuan-3 images, due to the difference in the spatial resolution. This study offers a set of approaches for effectively mapping land use types from (very) high resolution satellite images.

scholarly journals A Coarse-to-Fine Deep Learning Based Land Use Change Detection Method for High-Resolution Remote Sensing Images

2020 ◽  
Vol 12 (12) ◽  
pp. 1933 ◽  
Author(s):  
Mingchang Wang ◽  
Haiming Zhang ◽  
Weiwei Sun ◽  
Sheng Li ◽  
Fengyan Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Deep Learning ◽  
High Resolution ◽  
Land Use Change ◽  
Change Detection ◽  
Detection Method ◽  
Remote Sensing Images ◽  
High Quality ◽  
Coarse To Fine

In recent decades, high-resolution (HR) remote sensing images have shown considerable potential for providing detailed information for change detection. The traditional change detection methods based on HR remote sensing images mostly only detect a single land type or only the change range, and cannot simultaneously detect the change of all object types and pixel-level range changes in the area. To overcome this difficulty, we propose a new coarse-to-fine deep learning-based land-use change detection method. We independently created a new scene classification dataset called NS-55, and innovatively considered the adaptation relationship between the convolutional neural network (CNN) and the scene complexity by selecting the CNN that best fit the scene complexity. The CNN trained by NS-55 was used to detect the category of the scene, define the final category of the scene according to the majority voting method, and obtain the changed scene by comparison to obtain the so-called coarse change result. Then, we created a multi-scale threshold (MST) method, which is a new method for obtaining high-quality training samples. We used the high-quality samples selected by MST to train the deep belief network to obtain the pixel-level range change detection results. By mapping coarse scene changes to range changes, we could obtain fine multi-type land-use change detection results. Experiments were conducted on the Multi-temporal Scene Wuhan dataset and aerial images of a particular area of Dapeng New District, Shenzhen, where promising results were achieved by the proposed method. This demonstrates that the proposed method is practical, easy-to-implement, and the NS-55 dataset is physically justified. The proposed method has the potential to be applied in the large scale land use fine change detection problem and qualitative and quantitative research on land use/cover change based on HR remote sensing data.

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