In this paper, an unsupervised classification method is proposed for polarimetric synthetic aperture radar (PolSAR) images. The proposed method combines the Freeman decomposition and Cloude decomposition to improve the initial classification take advantage of the scattering mechanism. Decomposition coefficients of three scattering mechanism components are extracted through Freeman decomposition first; then the scattering entropy and angle are calculated by Cloude decomposition. Employing the aforementioned five parameters, the initial classification plane is redefined and divided based on the dominant scattering mechanism. Finally, a clustering algorithm is adopted to the iteration of the initial classification, which optimizes the boundaries between different terrains. The experiment results demonstrate the superiority and effectiveness of the proposed method.

To solve the problem of the unstable feature point extraction and the difficult feature matching in SAR image processing, we proposed a detection algorithm based on hybrid feature extraction. First, the repeated gray information is removed to reduce the complexity and import the real-time performance of the algorithm. Then MSER is utilized to extract regional extremes as candidates. And Harris-Laplace algorithm, which ensures uniform and stable feature-point-distribution, is employed to detect the feature points. Next, an improved SAR-SIFT algorithm is used for the feature matching. Finally, FSC and VFC are devised to refine the feature matching results. The dataset we used is the GaoFen-3 series image as it retains fine edge and structural texture information. The experiments have demonstrated the effectiveness of the proposed algorithm with insert performance metric here.

This paper proposed a novel river detection algorithm for SAR images with an improved version of OTSU algorithm. The width of the river can also be estimated using the detected results. The gray threshold, which is critical in the detection of the river, can be obtained adaptively by the proposed improved OTSU algorithm. Thus, it is more accurate than the conventional OTSU algorithm. In addition, DEM is employed in the river region extraction process to mitigate the effect of mountain shadows. The target refinement, the skeleton extraction and the endpoint removal algorithms are all devised to acquire the river centerline image. And the distance transformation algorithm is applied to generate the river distance map. Henceforth, one can estimate the river width by combining the previously acquired images. In order to create a more constant river centerline image, we employed a bridge removal procedure before the river skeletonization. The data used for the evaluation is from Sentinel 1A single-polarization data of the Yangtze River near Nanjing City, Jiangsu Province. The experiment results have shown that the proposed algorithm is more effective in the river extraction.

SAR image enhancement plays an important role in the process of SAR image processing and information interpretation. Super-resolution reconstruction is a widely adopted enhancement method. However, it is difficult to achieve a decent tradeoff between reconstruction effectiveness and the convergence speed for existing methods. To combat such problem, this paper proposed a novel solution to it, we started from the modeling of SAR image degradation principle, applying the adaptive line search strategy to the SAR image super-resolution reconstruction process, and redefined the step size selection in the reconstruction process, made it possible to achieve both reconstruction effectiveness and convergence speed. Compared with the existing empirical setting or iterative selection, the proposed method can reduce the number of iterations while guarantee the reconstruction results.

The polarimetric synthetic aperture radar (PolSAR) image filtering is an essential step before the quantitative inversion. However, the existing filtering algorithms often change the scattering features of the original PolSAR images. This leads to a performance decrease for the subsequent quantitative inversion. In order to combat such drawback, we adopted a novel filtering algorithm preceding the quantitative inversion. This algorithm adopted the idea of “classify first, filter later”. By utilizing the hybrid four-component polarization decomposition (HPD) method to pre-classify the pixels, and implementing non-local means lee (NLM-Lee) filtering only between the same ground object points, the scattering features are well-protected. The experiment results show that the proposed algorithm can not only effectively reduce the speckle and preserve the structural features of the image, but is far superior in preserving the scattering features of the images. Hence, when the PolSAR image filtering is carried out by the proposed algorithm, the performance of the quantitative inversion will be improved.

In this work, an effective 3-D reconstruction method for the transmission tower in mountain area, which combines the Persistent Scatterer Interferometry (PSI) and SAR Tomography (TomoSAR) based on generalized Orthogonal Matching Pursuit (gOMP) algorithm, is proposed. This paper is focused on reconstruction performance of gOMP algorithm on SAR signal and the 3-D reconstruction of transmission tower, using the results obtained in mountain area on simulated and real TerraSAR-X data. The vertical signal of slant distance from SAR images can be regarded as sparse signal, so the gOMP algorithm can be used to obtain extraction of elevation and deformation rate of transmission towers in mountain area. When only primary and secondary scattering points is present in each range-azimuth resolution cell, this method can be exploited to estimate the elevation and the deformation rate of the transmission tower. The results have demonstrated the advantage of this method.

SAR image enhancement is of great significance to SAR image processing. Aiming at the defects of traditional partial differential algorithms sensitive to noise, this paper proposes a SAR image enhancement method using both P-M nonlinear equations and coherent enhanced partial differential equations. Combining the physical model of the SAR image, the generalized diffusion process synthesizes the properties of both forward and backward diffusion, and has a good smooth enhancement effect at edges as well as smooth areas of the image. This hybrid model not only avoids noise enhancement, but also enhances image edges. Experiment results show that the proposed hybrid model has significantly improved the sharpness of enhanced SAR images with minimal distortion in image details, which is proven to be superior than applying only single one of the models.

In this paper, an improved change detection method (ICDM) is proposed to obtain the soil surface moisture (SSM) with a resolution of 20 meters, which greatly improves the resolution of SMAP soil moisture products. First, NDWI was calculated using a time series data of Sentinel-2 and then converted to vegetation water content (mveg). With the help of the water cloud model, the influence of vegetation on the backscattering coefficient is eliminated. Then, in order to make the change detection method (CDM) more reasonable, a method for removing influence of roughness change on backscattering coefficient is proposed. It uses the difference between co-polarization and cross-polarization ( σ0vv- σ0vh) for roughness normalization. SSM was obtained by combining the soil relative moisture retrieved from the time series data of Sentinel-1 with the maximum and minimum water content of the study area within one year obtained from the SMAP data. The results showed that the MRE of SSM extracted by ICDM was 8.34%, which was less than 17.49% of CDM.

In this paper, a landslide detection method using Small Baseline Subset Differential Interferometry (SBAS-DInSAR) combined with gray level co-occurrence matrix (GLCM) that combines the surface deformation rate with the texture feature information of the ground object to make full use of the ground object information. Compared with the traditional technique using only texture features, taking Qinglong County, a mountainous region in Guizhou Province in the southwestern region as the research area, can improve the accuracy of landslide detection. The final experimental results demonstrate the effectiveness of the proposed method, which can be used for landslide warning, the general survey of landslide bodies, and provide auxiliary information support for the detailed investigation.

Detecting the change of texture information is very important for landslide detection. In polarimetric SAR imagery, the landslides not only have typical polarimetric features but also have rich texture features. In this study, the texture feature extraction technique based on gray scale co-occurrence matrix was applied to process two SLC Sentinel-1A C-band SAR images and two COSMO-SkyMed X-band SAR images acquired before and after landslip for the landslide detection leading to the Jomda landslide event in 2018, Tibet of China and the Qinglong landslide in 2018, Guizhou of China. Several important characteristic parameters of the gray scale co-occurrence matrix were studied, and the optimal sliding window size and the optimal step size for landslide detection were obtained through rigorous analysis and comparison. The experimental results show that the analysis of the complex texture information can effectively detect the landslide in the mountain area with high accuracy.

In this paper, to address the problem in the registration of synthetic aperture radar (SAR), the speeded up robust features (SURF) approach based on multi-level FAST and improved random sampling consistency is proposed. This approach is used to address the high mismatch rate in SAR image registration, and it is based on the characteristics of SAR images. We construct a new corner detection by combining with the hierarchical theory of the system, which is referred to as multi-level FAST (MFAST). The contribution of MFAST algorithm mainly lies in the efficiency of SURF algorithm, which determines the main direction and feature descriptor. And improving the random sampling consistency (RANSAC) to remove mismatched feature points and ameliorate the registration effectiveness. The proposed algorithm is suitable for multi-sensor images with large gray differences and significant edge features. The experimental results show that the registration efficiency of the proposed algorithm is more than three times of the SURF algorithm, and the registration accuracy also is better than traditional SURF algorithm, which can reach 0.39 pixels.

Primarily the article briefly introduces the basic concept of the flat earth removal and baseline estimation, and mainly introduces the method of flat earth removal and baseline estimation based on orbit parameters using Radarsat-2 image, in detail represents the processing of flat earth removal based on orbit parameters. By removing the flat earth phase of the interferogram and analyzing the result of flat earth removal, the accuracy of generated digital elevation model and surface deformation, it shows that the method is effective, thanks to the highly precise orbit parameters. Meanwhile, the baseline estimation can be operated, which improves the efficiency.

A monitoring method of methane emission from rice paddies is developed by using quad-polarization radar datasets of ground-based scatterometer and space-borne RADARSAT-2. The backscattering coefficients in eight different rice growth periods are measured by using a ground-based scatterometer system, and the rice biomass, leaf-area index (LAI), water depth and CH4flux are collected by randomly sampling. Meanwhile, four scenes of RADARSAT-2 quad-polarization synthetic aperture radar (SAR) images covering the same study area are acquired. An experience rice parameter inversion model and a methane emissions model are established according to the measured data. The Models are applied to RADARSAT-2 image after pretreatment and classification to retrieve the rice biomass and the CH4flux at four dates, and come to the total methane emission, eventually. The methane emission per hectare is about 418.88 kg; it shows that methane emissions remain at a low level in the Chengdu Plain.

This paper presents a technique for the detection of damaged road in spaceborne synthetic radar (SAR) images. Roads in SAR image can be modeled as line structures, and are extracted from image by Duda detector, and the roads are accurately detected by removing line structures which aren't road information. We use a change detection algorithm based on Edgeworth approach and Kullback-Leibler divergence to obtain change information of images. Finally, we combined information of road and change detection result for detecting damaged road sections. This technique is applied on RadarSat-2 images that have a resolution of about 3m. The experimental results show that our method can detect mainly damaged road sections.