scholarly journals Keypoint Description Using Statistical Descriptor with Similarity-Invariant Regions

2022 ◽  
Vol 42 (1) ◽  
pp. 407-421
Author(s):  
Ibrahim El rube' ◽  
Sameer Alsharif
Keyword(s):  
Invariant Regions ◽  
Similarity Invariant

Similarity-invariant signatures for partially occluded planar shapes

1992 ◽  
Vol 7 (3) ◽  
pp. 271-285 ◽  
Author(s):  
Alfred M. Bruckstein ◽  
Nir Katzir ◽  
Michael Lindenbaum ◽  
Moshe Porat
Keyword(s):  
Invariant Signatures ◽  
Planar Shapes ◽  
Similarity Invariant ◽  
Partially Occluded

scholarly journals Hyperspectral and Multispectral Remote Sensing Image Fusion Based on Endmember Spatial Information

2020 ◽  
Vol 12 (6) ◽  
pp. 1009
Author(s):  
Xiaoxiao Feng ◽  
Luxiao He ◽  
Qimin Cheng ◽  
Xiaoyi Long ◽  
Yuxin Yuan
Keyword(s):  
Image Fusion ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
Spatial Information ◽  
Spectral Unmixing ◽  
High Spectral Resolution ◽  
Remote Sensing Image Fusion ◽  
Fusion Methods ◽  
Invariant Regions ◽  
The Difference

Hyperspectral (HS) images usually have high spectral resolution and low spatial resolution (LSR). However, multispectral (MS) images have high spatial resolution (HSR) and low spectral resolution. HS–MS image fusion technology can combine both advantages, which is beneficial for accurate feature classification. Nevertheless, heterogeneous sensors always have temporal differences between LSR-HS and HSR-MS images in the real cases, which means that the classical fusion methods cannot get effective results. For this problem, we present a fusion method via spectral unmixing and image mask. Considering the difference between the two images, we firstly extracted the endmembers and their corresponding positions from the invariant regions of LSR-HS images. Then we can get the endmembers of HSR-MS images based on the theory that HSR-MS images and LSR-HS images are the spectral and spatial degradation from HSR-HS images, respectively. The fusion image is obtained by two result matrices. Series experimental results on simulated and real datasets substantiated the effectiveness of our method both quantitatively and visually.

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