High spatial resolution remote sensing image segmentation based on the multiclassification model and the binary classification model

2021 ◽  
Author(s):  
Xiaoxiong Zheng ◽  
Tao Chen
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Binary Classification ◽  
Remote Sensing Image ◽  
Classification Model

Review on High Spatial Resolution Remote Sensing Image Segmentation Evaluation

2018 ◽  
Vol 84 (10) ◽  
pp. 629-646 ◽  
Author(s):  
Yangyang Chen ◽  
Dongping Ming ◽  
Lu Zhao ◽  
Beiru Lv ◽  
Keqi Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Image ◽  
Segmentation Evaluation ◽  
Image Segmentation Evaluation

scholarly journals High Spatial Resolution Remote Sensing Data Classification Method Based on Spectrum Sharing

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Meimei Duan ◽  
Lijuan Duan
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Spectrum Sharing ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Data Classification ◽  
Remote Sensing Image ◽  
Classification Model ◽  
Classification Method ◽  
Sensing Data

Existing remote sensing data classification methods cannot achieve the sharing of remote sensing image spectrum, leading to poor fusion and classification of remote sensing data. Therefore, a high spatial resolution remote sensing data classification method based on spectrum sharing is proposed. A page frame recovery algorithm (PFRA) is introduced to allocate the wireless spectrum resources in low-frequency band, and a dynamic spectrum sharing mechanism is designed between the primary and secondary users of remote sensing images. Based on this, D-S evidence theory is used to fuse high spatial resolution remote sensing data and correct the pixel brightness of the fused multispectral image. The initial data are normalized, the feature of spectral image is extracted, the convolution neural network classification model is constructed, and the remote sensing image is segmented. Experimental results show that the proposed method takes shorter time and has higher accuracy for high spatial resolution image segmentation. High spatial resolution remote sensing data classification is more efficient, and the accuracy of data classification and remote sensing image fusion are more ideal.

High Spatial Resolution Remote Sensing Image Classification Based on Deep Learning

2016 ◽  
Vol 36 (4) ◽  
pp. 0428001 ◽  
Author(s):  
刘大伟 Liu Dawei ◽  
韩玲 Han Ling ◽  
韩晓勇 Han Xiaoyong
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Image ◽  
Remote Sensing Image Classification
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