High-resolution (HR) synthetic aperture radar (SAR) images play an important role in people’s daily life and military applications. However, due to the interference of speckle noise, the texture details of the SAR images become relatively blurred. The fine texture details can be reconstructed by increasing the resolution of the SAR images. Generative adversarial networks achieve high performance in image super-resolution (SR) reconstruction, but the existing generative adversarial networks only pay attention to the discrimination of HR images without that of the low-resolution (LR) images. If the reconstructed HR image is sufficiently realistic, the LR image obtained from downsampled super-resolved images should also be the same as the original LR image. To take advantage of the LR image, an SAR image SR reconstruction algorithm based on cross-resolution discrimination (CRD) using teacher–student network is proposed. First, the teacher discriminator network (TD-Net) discriminates the HR images, which enriches the reconstructed HR images with more high-frequency texture details. Second, the student discriminator network (SD-Net) discriminates the LR images, which enables the reconstructed HR images to be accurately downsampled to the original LR image. Finally, the TD-Net guides the training of the SD-Net by transmitting distillation knowledge to the SD-Net, which further improves the discriminative performance of the SD-Net. Experiments on the SAR image dataset demonstrate that the performance of the proposed CRD algorithm is better than other algorithms when both the objective evaluations and subjective effects are considered.
The mechanism of speckle noise in synthetic aperture radar (SAR) images and its characteristics are analyzed. Combining the advantages of the traditional bilateral filter (BF) and alpha-trimmed median filter, a truncated-statistics-based bilateral filter (TS-BF) in SAR imagery is proposed. The despeckling method is based on the BF methodology, where the similarities of gray levels and spatial location of the neighboring pixels are exploited. However, traditional BF is not effective to reduce the strong speckle, which is often presented as impulse noise. The proposed TS-BF filtering method designs an adaptive truncation method to properly select the samples in the local reference window, where the mean and standard deviation of all the samples are estimated, and the background types of the current pixel-for-filtering are categorized. Finally, the samples of the local reference window are truncated with different levels according to different background types, and BF is applied using the truncated samples. TS-BF can effectively preserve the edge and texture information of the image while smoothing the speckle noise; it has a great application value. The experimental results show the effectiveness of the proposed algorithm through subjective and objective analyses.
An improved bilateral filter with adaptive parameters estimation in space domain and polarimetric domain for polarimetric synthetic aperture radar (PolSAR) image despeckling, named PolSAR adaptive bilateral filtering (PABF), is proposed. On one hand, PABF sets the spatial parameter adaptively according to the local coefficient of variation. On the other hand, the polarimetric parameter is adjusted adaptively on the basis of the noise variance estimated from the convolution between the intensity image and Laplacian template. The experiments performed on simulated and real PolSAR data show that PABF effectively suppresses speckles while maintaining important details of images.
The problem of change detection in bitemporal synthetic aperture radar (SAR) images is studied. Motivated by utilizing nondense neighborhoods around pixels to detect the change level, a pointwise change detection approach is developed by employing a bilaterally weighted graph model and an irregular Markov random field (I-MRF). First, keypoints with local maximum intensity are extracted from one of the bitemporal images to describe the textural information of the images. Then, two bilaterally weighted graphs with the same topology are constructed for the bitemporal images using the keypoints, respectively. They utilize both the spatial structural and intensity information to provide good performance for feature-based change detection. Next, a change measure function is designed to evaluate the similarity between the graphs, and then the nondense difference image (NDI) is generated. Finally, an I-MRF with a generalized neighborhood system is proposed to classify the discrete keypoints on the NDI. Experiments on real SAR images show that the proposed NDI improves separability between changed and unchanged areas, and I-MRF provides high accuracy and strong noise immunity for change detection tasks with noise-contaminated SAR images. On the whole, the proposed approach is a good candidate for SAR image change detection.
In the Bohai Sea, sea ice drifting is hardly tracked due to the highly sea motion. The long satellite repeat cycles in the polar region are not suitable to the ice drift tracking in the Bohai Sea. The unique characteristics of the Geostationary Ocean Color Imager (GOCI) allow the tracking of sea ice drift on a daily basis with the use of 1-hour time intervals images (eight images per day). The optical flow method is applied to track the sea ice drift in the Bohai Sea. Experiments have shown that the sea ice vectors from the optical flow method are agreement well with the manually selected reference data.
Independent component analysis (ICA) provides an efficient approach to characterizing higher-order statistical
relationships in texture images. For the classification of textures based on ICA, a fundamental problem lies on the
selection of ICA features which are desired to maximize the separability between classes. In this paper, the efficiency of
various ICA features for texture classification is investigated, which involves ICA coefficients and their various statistics
with a focus on the higher-order statistics to take into account the non-Gaussian property of ICA coefficients. By
evaluating the ICA features on the classification of twenty-five classes of Brodatz texture images, it has been shown that
the higher-order statistics of ICA coefficients offer efficient discrimination of textures and the combination of variance,
skewness and kurtosis is a better alternative to the previously reported ICA features. By comparing the performance of
ICA features to their principal component analysis (PCA) counterparts, it is further revealed that the advantage of ICA
for texture classification can be obtained by using the higher-order statistics of ICA coefficients.
KEYWORDS: Radiography, Defect detection, Ions, Computer science, Human vision and color perception, Spherical lenses, Digital image processing, Image processing, Analytical research, Computing systems
This article discussed the computerized automatic evaluation of weld radiographs. The radiation projection method (RPM) was introduced, and a modified calculation method based on RPM was proposed. The actual experimental results demonstrated that this new method improved the ability of RPM to suppress the interference of noise.
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