Abstract
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.
Highlights
MethodE problem of wireless spectrum sharing of remote sensing image in low-frequency band is to solve channel assignment matrix AM×N. e sum of the perturbation elements in each channel is taken as the perturbation coefficient of the channel [10] and expressed as follows: SS
None of the above studies can realize the spectrum sharing of remote sensing images, resulting in the poor effect of remote sensing data fusion and classification
Erefore, a high spatial resolution remote sensing data classification method based on spectrum sharing is proposed
Summary
E problem of wireless spectrum sharing of remote sensing image in low-frequency band is to solve channel assignment matrix AM×N. e sum of the perturbation elements in each channel is taken as the perturbation coefficient of the channel [10] and expressed as follows: SS. Classification Method of High Spatial Resolution Remote Sensing Data Based on Spectrum Sharing. Classification is one of the main objectives of high spatial resolution remote sensing monitoring, which is a method of dividing each pixel or region into a certain type of terrain based on features collected by airborne LiDAR and hyperspectral technology [15]. Evidence theory can combine different kinds of evidences, which are related to each other, so as to fuse different kinds of evidences and get the final conclusion. e fusion rules are as follows: assuming that m1(A) and (A ∈ 2U) are the basic probability functions under different evidence U, the following combination rules of evidence theory may be applied: Start
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