Novel fusion method for SAR and optical images based on non-subsampled shearlet transform

Chu, Tianyong; Tan, Yumin; Liu, Qiang; Bai, Bingxin

doi:10.1080/01431161.2020.1723175

Cited by 27 publications

(12 citation statements)

References 28 publications

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“…Then a local energy fusion strategy was used to fuse the texture components of the SAR image and the optical image to obtain the fused texture components. After the processing of spectral and texture components was completed, the contourlet method [33] was adopted to fuse the prepared features. In this paper, we consider the feature information and correlation of images, and get more macroscopic feature level information compared with pixel level fusion.…”

Section: Proposed Frameworkmentioning

confidence: 99%

A Novel Feature-Level Fusion Framework Using Optical and SAR Remote Sensing Images for Land Use/Land Cover (LULC) Classification in Cloudy Mountainous Area

et al. 2020

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Remote sensing data plays an important role in classifying land use/land cover (LULC) information from various sensors having different spectral, spatial and temporal resolutions. The fusion of an optical image and a synthetic aperture radar (SAR) image is significant for the study of LULC change and simulation in cloudy mountain areas. This paper proposes a novel feature-level fusion framework, in which the Landsat operational land imager (OLI) images with different cloud covers, and a fully polarized Advanced Land Observing Satellite-2 (ALOS-2) image are selected to conduct LULC classification experiments. We take the karst mountain in Chongqing as a study area, following which the features of the spectrum, texture, and space of the optical and SAR images are extracted, respectively, supplemented by the normalized difference vegetation index (NDVI), elevation, slope and other relevant information. Furthermore, the fused feature image is subjected to object-oriented multi-scale segmentation, subsequently, an improved support vector machine (SVM) model is used to conduct the experiment. The results showed that the proposed framework has the advantages of multi-source data feature fusion, high classification performance and can be applied in mountain areas. The overall accuracy (OA) was more than 85%, with the Kappa coefficient values of 0.845. In terms of forest, gardenland, water, and artificial surfaces, the precision of fusion image was higher compared to single data source. In addition, ALOS-2 data have a comparative advantage in the extraction of shrubland, water, and artificial surfaces. This work aims to provide a reference for selecting the suitable data and methods for LULC classification in cloudy mountain areas. When in cloudy mountain areas, the fusion features of images should be preferred, during the period of low cloudiness, the Landsat OLI data should be selected, when no optical remote sensing data are available, and the fully polarized ALOS-2 data are an appropriate substitute.

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Section: Proposed Frameworkmentioning

confidence: 99%

A Novel Feature-Level Fusion Framework Using Optical and SAR Remote Sensing Images for Land Use/Land Cover (LULC) Classification in Cloudy Mountainous Area

et al. 2020

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“…Considering the complementarity between different types of sensor data, researchers have started to fuse multi-type sensor data to solve traditional remote sensing problems, such as land cover analysis [10][11][12][13], change detection [14][15][16], image classification [17], and image fusion [18,19]. As a critical problem in the field of photogrammetry and remote sensing, topographic mapping is mainly completed using optical satellite images [20].…”

Section: Introductionmentioning

confidence: 99%

Exploiting High Geopositioning Accuracy of SAR Data to Obtain Accurate Geometric Orientation of Optical Satellite Images

et al. 2021

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Accurate geopositioning of optical satellite imagery is a fundamental step for many photogrammetric applications. Considering the imaging principle and data processing manner, SAR satellites can achieve high geopositioning accuracy. Therefore, SAR data can be a reliable source for providing control information in the orientation of optical satellite images. This paper proposes a practical solution for an accurate orientation of optical satellite images using SAR reference images to take advantage of the merits of SAR data. Firstly, we propose an accurate and robust multimodal image matching method to match the SAR and optical satellite images. This approach includes the development of a new structural-based multimodal applicable feature descriptor that employs angle-weighted oriented gradients (AWOGs) and the utilization of a three-dimensional phase correlation similarity measure. Secondly, we put forward a general optical satellite imagery orientation framework based on multiple SAR reference images, which uses the matches of the SAR and optical satellite images as virtual control points. A large number of experiments not only demonstrate the superiority of the proposed matching method compared to the state-of-the-art methods but also prove the effectiveness of the proposed orientation framework. In particular, the matching performance is improved by about 17% compared with the latest multimodal image matching method, namely, CFOG, and the geopositioning accuracy of optical satellite images is improved, from more than 200 to around 8 m.

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“…The algorithm performance is poor especially when the source image has complex details and continuous curves. NSCT has multi-scale direction anisotropy and shift invariance, which can effectively remove Gibbs effects [29]. However, NSCT has a complicated structure and a high computation cost.…”

Section: Introductionmentioning

confidence: 99%

An Infrared and Visible Image Fusion Algorithm Based on LSWT-NSST

Jiang

2020

IEEE Access

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Regarding the problems of image distortion, edge blurring, Gibbs phenomena in the traditional wavelet transform algorithm and the loss of subtle features in the Non-Subsampled Shearlet Transform (NSST), and considering the physical characteristics of infrared and visible images, an infrared and visible image fusion algorithm based on the Lifting Stationary Wavelet Transform (LSWT) and Non-Subsampled Shearlet Transform is proposed in this paper. First, since LSWT can quickly calculate and has all advantages of traditional WT, it is utilized to decompose infrared and visible images to obtain lowfrequency coefficients and multi-scale and multi-directional high-frequency coefficients, respectively. Second, NSST multi-scale decomposition is used to extract the target features and detailed features of the image from the high and low-frequency sub-bands to obtain new high and low-frequency sub-bands. Third, according to the physical characteristics that low and high-frequency coefficients represent, different fusion rules are designed. Discrete Cosine Transform (DCT) and Local Spatial Frequency (LSF) are introduced in the low-frequency sub-band, and LSF adaptive weighted fusion rules are used in the DCT domain. The fusion strategy improves the regional contrast in the high-frequency sub-band with the spectral characteristics of human vision. Finally, the Inverse Lifting Stationary Wavelet Transform (ILSWT) is used to reconstruct the fusion coefficients to obtain the final fused images. To verify the advantages of the proposed algorithm in this paper, the classic and advanced 9 IR and VI fusion algorithms are selected for subjective and objective comparison. In the objective evaluation, a comprehensive ranking index is designed based on 9 classical indicators. Simulation experiments with 10 IR and VI fusion algorithms prove that the proposed algorithm has better performance and flexibility. The results show that the proposed algorithm in this paper fuses the images with clear edges, prominent targets, and good visual perception, and it outperforms state-of-the-art image fusion algorithms.

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Novel fusion method for SAR and optical images based on non-subsampled shearlet transform

Cited by 27 publications

References 28 publications

A Novel Feature-Level Fusion Framework Using Optical and SAR Remote Sensing Images for Land Use/Land Cover (LULC) Classification in Cloudy Mountainous Area

A Novel Feature-Level Fusion Framework Using Optical and SAR Remote Sensing Images for Land Use/Land Cover (LULC) Classification in Cloudy Mountainous Area

Exploiting High Geopositioning Accuracy of SAR Data to Obtain Accurate Geometric Orientation of Optical Satellite Images

An Infrared and Visible Image Fusion Algorithm Based on LSWT-NSST

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