Accurate detection of the changed areas and effective speckle suppression are the main difficulties in synthetic aperture radar (SAR) image change detection (CD). The available feature extraction techniques for CD always ignore the spatial context correlation and are not robust to speckle noise. To overcome these drawbacks, we present a novel feature extraction technique that takes full advantage of sparse representation (SR) and nonlocal similarity of SAR images. First, each pixel in the difference image is represented by a feature vector, which is extracted using the sparse coding with a constructed robust discriminative dictionary. Next, a group of related feature vectors for each pixel can be generated according to the nonlocal similarity of SAR image. Finally, the discriminative change feature is obtained by means of the pooling, which can extract significant change information from the feature group. This method not only suppresses the speckle noise effectively but also improves the discrimination of the extracted features. The experimental results verify the superior performance of the proposed method on several real SAR image data sets and simulated image pairs.

Synthetic aperture radar (SAR) image change detection has good application prospects in the domain of remote sensing image processing. However, the accuracy of change detection is negatively affected by the inherent speckle noise in SAR images. To solve this problem, a method based on sparse representation (SR) and a capsule network is proposed. Firstly, sparse features of the difference image (DI) are extracted by the SR method. Secondly, a lightweight capsule network (L-CapsNet) is constructed, which is used to mine the spatial relationship between features. The network classifies the changed and unchanged pixels. Finally, the change map (CM) is generated. The proposed method can obtain more robust features while reducing the influence of speckle noise. Experiments are performed on four SAR data sets to compare the proposed method with related methods. The results confirm the superior performance of the proposed method.

Synthetic aperture radar (SAR) image change detection has been widely applied in a variety of fields as one of the research hotspots in remote sensing image processing. To increase the accuracy of SAR image change detection, an algorithm based on saliency detection and an attention capsule network is proposed. First, the difference image (DI) is processed using the saliency detection method. The DI’s most significant regions are extracted. Considering the saliency detection characteristics, we select training samples only from the DI’s most salient regions. The regions in the background are omitted. This results in a significant reduction in the number of training samples. Second, a capsule network based on an attention mechanism is constructed. The spatial attention model is capable of extracting the salient characteristics. Capsule networks enable precise classification. Finally, a final change map is obtained using capsule network to classify images. To compare the proposed method with the related methods, experiments are carried out on four real SAR datasets. The results show that the proposed method is effective in improving the exactitude of change detection.

Accurate spectrum prediction in design of terahertz (THz) devices remains challenging, especially for THz fibers. In this article, we propose an approach based on artificial intelligence (AI) assisted finite element method (FEM) to achieve accurate spectrum prediction for the design of THz fiber operating in 6G communication window. The antiresonant THz fiber has been selected to verify the effectiveness of this method. Initially, the principle and physical modeling of antiresonant THz fiber are analyzed. The spectra of THz fibers with different structural parameters are designed and predicted by FEM simulation. Then, the THz fibers are fabricated by 3D printing technology and the spectra are measured by a THz time domain spectroscopy system. The spectral dataset obtained by both forms are prepared for the modeling of AI-assisted FEM methods. Different AI algorithms are induced in FEM to predict experimental spectrum, including elman neural network (Elman), support vector machines (SVM), and general regression neutral network (GRNN). The prediction performance obtained by different methods are compared and analyzed comprehensively to confirm the effectiveness of proposed methods. The AI-assisted FEM methods show great improvement of prediction accuracy in the design of THz fibers. It provides accurate data support for THz device modeling assisted by machine learning.

In this study, a novel feature learning method for synthetic aperture radar (SAR) image automatic target recognition is presented. It is based on spatial pyramid matching (SPM), which represents an image by concatenating the pooling feature vectors that are obtained from different resolution sub-regions. This method exploits the dependability of obtaining the weighted pooling features generated from SPM sub-regions. The dependability is determined by the residuals obtained from sparse representation. This method aims at enhancing the weights of the pooling features generated in the sub-regions located in the target and suppressing the weights of the background. The feature representation for SAR image target recognition is discriminative and robust to speckle noise and background clutter. Experiments performed on the Moving and Stationary Target Acquisition and Recognition public dataset prove the advantageous performance of the presented algorithm over several state-of-the-art methods.