Pixel level fusion techniques for SAR and optical images: A review

Synthetic Aperture Radar (SAR) is an active remote sensing system which is utilized for producing high-resolution images. But due to backscattering of microwave signals, these images get contaminated with speckle noise. This paper proposes an improved local statistics filter for filtering the speckle noise from the SAR images. The proposed filter is a combination of mean and hybrid median filters, employing a novel 7x7 filtering template. The performance of the proposed filter is tested against the standard Hybrid Median filters for which the evaluated values show better performs in terms of PSNR (in dB) and SSI.

An Improved Local Statistics Filter for Denoising of SAR Images

The method of speckle reduction is widely used in synthetic aperture radar (SAR) imagery over the last three decades The SAR images are inherently speckled in nature Speckle noise is a granular pattern distribution, usually modeled as a multiplicative noise that affects the SAR images, as well as all coherent images Other SAR related problems are also discussed in this paper Therefore, despeckling approaches are needed to improve the quality of SAR images However, there is a trade-off between speckle reduction and the preservation of fine details in the despeckled SAR image The reduction of the speckle noise without losing the fine details of the SAR image is a diffucult task However, many despeckling methods have been discussed to reduce the speckle noise from the SAR images Each method has their own norms, advantages and disadvantages This article contains a review of some major work in the field of SAR image despeckling Often, scientists and scholars have faced the struggle to understand the pattern distribution of the speckle noise in SAR images Hence, a brief details about radar, SAR imaging, speckle noise in SAR images and the prevalent approaches of SAR image despeckling are reviewed here The advantages and disadvantages of SAR image despeckling approaches are also analysed and discussed

A Review on SAR Image and its Despeckling

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, a-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%.

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

SAR is a powerful tool for producing high resolution remote sensing images under all weather conditions. But, during the backscattering process, the obtained images are contaminated with a special type of noise, called speckle. Speckle noise appears like a granular pattern in the SAR images and results in degrading the image quality. This work uses contourlet transform to effectively represent the directional information and intrinsic geometrical structures of the SAR image along the smooth contours (edges). Here, the speckled image is decomposed into directional sub-bands using contourlet transform followed by application of adaptive thresholding and finally, they are reconstructed to produce the denoised image. The adaptive thresholding uses a function with increasing and decreasing exponential functions to suppress the speckle content from the SAR image. The finally produced images prove their superiority in terms of speckle suppression and detail preservation.

Despeckling of SAR images in contourlet domain using a new adaptive thresholding

SAR images despeckling is one of the most important research subjects in SAR images processing. Following discussion on the advantage and disadvantage of curvelet and wavelet transforms based SAR images despeckling, an algorithm which combined curvelet and wavelet transforms was proposed for despeckling of high resolution SAR images. Firstly, the curvelet and wavelet were used to denoise the original SAR image, then the related residual image between their results was acquired, which was then denoised through the curvelet transform. Finally the denoised residual image was added to the curvelet denoised image. Experimental results of TerraSAR-X high resolution images indicated that, compared with the Enhanced Lee filter, the Enhanced Frost filter, the wavelet transform based and curvelet transform based methods, the algorithm presented here had well despeckling effect, as well as fine edge-preserving ability.

A despeckling algorithm combining curvelet and wavelet transforms of high resolution SAR images

TerraSAR-X is a new generation of high-resolution (1 m) space borne Synthetic Aperture Radar (SAR) satellite, which makes ground targets detection (GTD), one of the hot problems in SAR image application fields, possible As for the mostly used CFAR algorithm for GTD, the statistical analysis of SAR image is an essential step in threshold calculation However, it has been proved that the traditionally used Rayleigh distribution is no longer suitable to modelling the statistical characteristics of 1 m resolution TerraSAR-X image with ground targets To model these heavy-tailed distributions, some other distributions, including Gamma, Weibull, lognormal and K distribution models have been applied According to the analysis of large numbers of experiment results, lognormal distribution model is selected to describe the statistical characteristics of the high resolution SAR images with ground targets Then genetic algorithm (GA) is adopted to efficiently estimate the model parameters as accurately as possible, which are subsequently used to calculate the global CFAR detection threshold The experiments indicate that, lognormal distribution model with GA based parameters computation are more suitable than Gamma, Weibull and K distribution models to describe the statistical characteristics of the high resolution SAR images, which is propitious to performance improvement of the global CFAR algorithm

Statistical Analysis of High Resolution TerraSAR-X Images for Ground Targets Detection

With TM remote sensing data and topographic data as the main data sources,coupled with other statistical information obtained in the upper reaches of Minjiang River,the authors tried to apply the analytic hierarchy process( AHP) to reframing the vulnerability characteristics resulting from complicated factors and establishing the comprehensive evaluation index system and the model with multiple objective elements. Vegetation index change rate,population density,terrain relief,slope and soil type were five factors serving as evaluation index factors in the AHP to determine the weight of each evaluation index,and superimposition analysis of the indicators was made on the Arc GIS platform. The grade map of comprehensive evaluation on ecological environment in the reaches of the Minjiang River could be obtained,which was classified into five degrees in the environmental vulnerability. The results achieved by the authors indicate that environmental vulnerability of the upper reaches of the Minjiang River is extremely intense,caused mainly by natural factors and human factors. The results of his study provide the quantitative and qualitative scientific basis for the construction of the environmental vulnerability pattern and the comprehensive evaluation of ecological environment along the upper reaches of the Minjiang River basin.

Evaluation of environmental vulnerability in the upper reaches of the Minjiang River

TerraSAR-X is the first commercially operated high resolution space-borne SAR in the world, which shows good potential in target detection fields. Statistical analysis and modeling are key steps in SAR images based automatic target detection systems. With four typical statistical measures, lognormal distribution is proved well to fit the histograms of TerraSAR-X images over land and ocean regions, and more suitable than Weibull, Gamma, K, G0 and α stable distributions to modeling statistics of such images. Additionally, the MLE based parameters estimation and simply analytical expression of detection threshold also indicates the effectiveness and efficiency of lognormal based CFAR for target detection.

Wu Junzheng

Papers

A despeckling algorithm combining curvelet and wavelet transforms of high resolution SAR images

Statistical Analysis of High Resolution TerraSAR-X Images for Ground Targets Detection

Evaluation of environmental vulnerability in the upper reaches of the Minjiang River

Statistical analysis and modeling of TerraSAR-X images for CFAR based target detection