Gurbantunggut Desert Haloxylon Forest NDVI Time Effect - Based on Phenological Changes and the Image of MODIS

2012 ◽  
Vol 433-440 ◽  
pp. 5409-5414
Author(s):  
Tie Cheng Huang ◽  
Shu Jiang Chen ◽  
Min Hou ◽  
Ge Li Shi ◽  
Zhen Xia Shi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information Extraction ◽  
Remote Sensing Image ◽  
Time Effect ◽  
Gurbantunggut Desert ◽  
Information Analysis ◽  
Time Effects ◽  
Image Information ◽  
Phenological Changes ◽  
Gurbantünggüt Desert

Remote sensing image information extraction research is one of the key problems of remote sensing research, it is also one of the hot and difficult points in remote sensing research., In this paper,NDVI images for the study of synthesis of haloxylon ammdendron forest 16 days of MODIS is taken as research object, aimed at improving the precision of information extraction, focuses on the NDVI time effects on phenology of haloxylon ammdendron woodland response, tell woodland diagnosis point of information, using diagnostic points of haloxylon forest information analysis and extraction of results and field visits, mutatis mutandis, the results showed information extraction accuracy has improved.

scholarly journals Mathematical models for information classification and recognition of multi-target optical remote sensing images

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 951-960
Author(s):  
Haiqing Zhang ◽  
Jun Han
Keyword(s):  
Remote Sensing ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Remote Sensing Image ◽  
Optical Remote Sensing ◽  
Specific Class ◽  
Three Dimensional Model ◽  
Image Information ◽  
Information Classification ◽  
Optical Remote Sensing Image

Abstract Traditionally, three-dimensional model is used to classify and recognize multi-target optical remote sensing image information, which can only identify a specific class of targets, and has certain limitations. A mathematical model of multi-target optical remote sensing image information classification and recognition is designed, and a local adaptive threshold segmentation algorithm is used to segment multi-target optical remote sensing image to reduce the gray level between images and improve the accuracy of feature extraction. Remote sensing image information is multi-feature, and multi-target optical remote sensing image information is identified by chaotic time series analysis method. The experimental results show that the proposed model can effectively classify and recognize multi-target optical remote sensing image information. The average recognition rate is more than 95%, the maximum robustness is 0.45, the recognition speed is 98%, and the maximum time-consuming average is only 14.30 s. It has high recognition rate, robustness, and recognition efficiency.

scholarly journals U-net Network for Building Information Extraction of Remote-Sensing Imagery

2018 ◽  
Vol 14 (12) ◽  
pp. 179
Author(s):  
Jingtan Li ◽  
Maolin Xu ◽  
Hongling Xiu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Information Extraction ◽  
Image Data ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Building Information ◽  
The Face

With the resolution of remote sensing images is getting higher and higher, high-resolution remote sensing images are widely used in many areas. Among them, image information extraction is one of the basic applications of remote sensing images. In the face of massive high-resolution remote sensing image data, the traditional method of target recognition is difficult to cope with. Therefore, this paper proposes a remote sensing image extraction based on U-net network. Firstly, the U-net semantic segmentation network is used to train the training set, and the validation set is used to verify the training set at the same time, and finally the test set is used for testing. The experimental results show that U-net can be applied to the extraction of buildings.

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