A New Classification Method of High Spatial Resolution Remote Sensing Image

2008 ◽  
Author(s):  
Ren Guangbo ◽  
Zheng Rong-er ◽  
Ren Guangbo ◽  
Zhang Jie ◽  
Ma Yi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Image ◽  
Classification Method ◽  
New Classification

scholarly journals SUPERPIXEL CLASSIFICATION OF HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGE BASED ON MULTI-SCALE CNN AND SCALE PARAMETER ESTIMATION

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 681-685
Author(s):  
Y. Chen ◽  
D. Ming
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Classification Accuracy ◽  
High Spatial Resolution ◽  
Scale Parameter ◽  
Remote Sensing Image ◽  
Classification Method ◽  
Multi Scale ◽  
Digital Orthophoto

<p><strong>Abstract.</strong> In recent years, considerable attention has been paid to integrate convolutional neural network (CNN) with land cover classification of high spatial resolution remote sensing image. Per-pixel classification method based on CNN (Per-pixel CNN) achieved higher accuracy with the help of high-level features, however, this method still has limitations. Even though per-superpixel classification method based on CNN (Per-superpixel CNN) overcome the limitations of per-pixel CNN, classification accuracy of complex urban is easily influenced by scale effect. To solve this issue, superpixel classification method combining multi-scale CNN (Per-superpixel MCNN) method is proposed. Besides, this paper proposes a novel spatial statistics based method to estimate applicable scale parameter of per-superpixel CNN. Experiments using proposed method were performed on Digital Orthophoto Quarer Quad (DOQQ) images in urban and suburban area. Classification results show that per-superpixel MCNN can effectively avoid misclassification in complex urban area compared with per-superpixel classification method combining single-scale CNN (Per-superpixel SCNN). Series of classification results also show that using the pre-estimated scale parameter can guarantee high classification accuracy, thus arbitrary nature of scale estimation can be avoided to some extent.</p>

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.

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