Abstract
Most bamboo forests grow in humid climates in low-latitude tropical or subtropical monsoon areas, and they are generally located in hilly areas. Bamboo trunks are very straight and smooth, which means that bamboo forests have low structural diversity. These features are beneficial to synthetic aperture radar (SAR) microwave penetration and they provide special information in SAR imagery. However, some factors (e.g., foreshortening) can compromise the interpretation of SAR imagery. The fusion of SAR and optical imagery is considered an effective method with which to obtain information on ground objects. However, most relevant research has been based on two types of remote sensing image. This paper proposes a new fusion scheme, which combines three types of image simultaneously, based on two fusion methods: bidimensional empirical mode decomposition (BEMD) and the Gram-Schmidt transform. The fusion of panchromatic and multispectral images based on the Gram-Schmidt transform can enhance spatial resolution while retaining multispectral information. BEMD is an adaptive decomposition method that has been applied widely in the analysis of nonlinear signals and to the nonstable signal of SAR. The fusion of SAR imagery with fused panchromatic and multispectral imagery using BEMD is based on the frequency information of the images. It was established that the proposed fusion scheme is an effective remote sensing image interpretation method, and that the value of entropy and the spatial frequency of the fused images were improved in comparison with other techniques such as the discrete wavelet, à-trous, and non-subsampled contourlet transform methods. Compared with the original image, information entropy of the fusion image based on BEMD improves about 0.13–0.38. Compared with the other three methods it improves about 0.06–0.12. The average gradient of BEMD is 4%–6% greater than for other methods. BEMD maintains spatial frequency 3.2–4.0 higher than other methods. The experimental results showed the proposed fusion scheme could improve the accuracy of bamboo forest classification. Accuracy increased by 12.1%, and inaccuracy was reduced by 11.0%.
Highlights
A synthetic aperture radar (SAR) sensor is a type of active sensor that makes earth observation possible, regardless of weather conditions
Results and Discussion to verify the effectiveness of bidimensional empirical mode decomposition (BEMD) in image fusion we selected a set of data comprising SAR and panchromatic images
We can see that the information entropy based on BEMD is 7.87 and 7.78 (Tables 1 and 2) which is the highest; the average slope and spatial frequency of the fusion image were 40.01, 71.41 and 27.16, 48.57 which shows the advantage of BEMD in dealing with nonlinear non-stationary signals
Summary
A synthetic aperture radar (SAR) sensor is a type of active sensor that makes earth observation possible, regardless of weather conditions. Because of the significant differences between the imaging mechanisms of SAR and optical sensors, the properties of SAR, multispectral, and panchromatic images are different for the same surface features of the same target. The multiscale decomposition method (MSD) has been recognized as useful for image fusion Based on this finding, various MSD-based fusion methods have been proposed for the fusion of SAR and panchromatic images [7,8,9]. The bidimensional empirical mode decomposition (BEMD) method has been used since 2003 [15] It is an extension of the EMD technique that has been applied to image fusion [16]. We applied the BEMD method to SAR, panchromatic, and multispectral images. Achieving the fusion of SAR, multispectral, and panchromatic imagery simultaneously was the objective of this study
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