Multisource Remote Sensing Imagery Fusion Scheme Based on Bidimensional Empirical Mode Decomposition (BEMD) and Its Application to the Extraction of Bamboo Forest

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%.

A High-Dynamic-Range Optical Remote Sensing Imaging Method for Digital TDI CMOS

“…In order to develop the adaptive enhancement and fusion algorithm according to the specific characteristics of the source images, it is an essential condition to have an objective and accurate image quality evaluation method [20]. Non-reference evaluation indicators usually include contrast, entropy, average gradient, and others [21,22].…”

“…Non-reference evaluation indicators usually include contrast, entropy, average gradient, and others [21,22].…”

“…The larger the entropy is, the more abundant information will be contained in the image, and the better the visual effect will be [21]. Assuming that the probability of gray values x i is P(x i ), the entropy of the image is defined as follows:…”

Application of the Hilbert–Huang transform for recognition of active gully erosion sites in the Loess Plateau of China

“…This method can be used to acquire the different scales of the original signal intrinsic characteristics and completely eliminate the linear and stationary constraints. Until now, the HHT has been widely used in biomedicine (Zahra, Kanwal, Ur, Ehsan, & McDonald‐Maier, ), fault diagnosis and localization (Lei, He, & Zi, ; Peng, Tse, & Chu, ), oceanography (Song, Bai, Dong, & Song, ), earthquake engineering (Wang, Zhang, Yu, & Zhang, ), paleoclimatology (Liu et al, ; Qian, Wu, Fu, & Wang, ), image processing (Liu, Li, Gong, et al, ; Nunes & Deléchelle, ), and some other fields. The key process in HHT is the empirical model decomposition (EMD), and this process has a so‐called “model mixing” problem, which is defined as “a single intrinsic mode function (IMF) either consisting of widely disparate scale signals or a signal of a similar scale residing in different IMF components” (Huang & Wu, ).…”

Fusion of SAR and Multispectral Images Using Random Forest Regression for Change Detection

“…Especially, in the case of fusion between SAR and MS images, it is assumed that the SAR image is the PAN and performs fusion with the MS image, which is a good alternative to enhance the presentation and demonstration of SAR images by integrating the color information of MS images [10,11]. However, due to significant differences between the imaging mechanisms of SAR and optical images, when SAR and MS images are fused by using conventional image fusion methods, the gray value differences between the intensity image and the SAR image become obvious [11,12]. After the image fusion, this difference causes substantial color distortion in the fused images [11].…”