Due to the capability of fast multiparametric quantitative imaging, magnetic resonance fingerprinting (MRF) is becoming a promising quantitative magnetic resonance imaging approach. However, the artifacts caused by the highly undersampled data acquisition lead to inaccurate estimation of the tissue parameter maps. Based on the assumption that the 3-D MRF data can be modeled as a piecewise smooth signal, with the discontinuities localized to the zero sets of a bandlimited function, we exploit the low-rank property of the structured Toeplitz matrix constructed from the Fourier measurements. In addition, we adopt the subspace projection scheme to improve the accuracy of parameter estimation. In order to efficiently solve the regularized problem, we propose an iterative two-stage algorithm, which alternately updates the ${k}$ -space data and projects the space-time matrix into the dictionary space. Numerical experiments demonstrate that the proposed algorithm shows significant improvement in MRF time-series images reconstruction and can provide more accurate parameter maps over the state-of-the-art algorithms.

The active recognition of interesting targets has been a vital issue for remote sensing. In this paper, a novel multi-source fusion method for ship target detection and recognition is proposed. By introducing synthetic aperture radar (SAR) sensor images, the proposed method solves the problem of precision degradation in optical remote sensing image target detection and recognition caused by the limit of illumination and weather conditions. The proposed method obtains port slice images containing ship targets by fusing optical data with SAR data. On this basis, spectral residual saliency and region growth method are used to detect ship targets in optical image, while SAR data are introduced to improve the accuracy of ship detection based on joint shape analysis and multi-feature classification. Finally, feature point matching, contour extraction and brightness saliency are used to detect the ship parts, and the ship target types are identified according to the voting results of part information. The proposed ship detection method obtained 91.43% recognition accuracy. The results showed that this paper provides an effective and efficient ship target detection and recognition method based on multi-source remote sensing images fusion.

Deep learning models utilizing spatial features have demonstrated outstanding performance in remote sensing change detection. However, current deep learning methods relying on spatial features still exhibit limitations in accurately detecting changed edge regions and insignificant areas. To overcome this limitation, we propose the multiscale spatial-frequency feature fusion network (MSFNet) for change detection. The network aims to enhance the detection capability of change edge and insignificant regions. In the encoding stage, we introduce a frequency difference feature extraction module to enhance the frequency difference features by extracting high-frequency information that represents changes and low-frequency information that represents invariance at each feature scale. This approach effectively mitigates the issue of incomplete change information caused by relying solely on a single spatial feature dimension for feature extraction. In the decoding stage, a spatial frequency difference fusion module is introduced to map the extracted frequency difference information back to the spatial domain. This module enhances the network's semantic modeling capability for change regions while mitigating the degradation of edge detection accuracy resulting from information loss during feature extraction. The experimental results demonstrate that MSFNet has remarkable performance on both the LEVIR-CD and WHU-CD datasets, with an F1 score of 91.66 and an IoU of 84.41 on the LEVIR-CD dataset, and an F1 score of 90.87 and an IoU of 82.80 on the WHU-CD dataset.

To obtain more building surface information with fewer images, an unmanned aerial vehicle (UAV) path planning method utilizing an opposition-based learning artificial bee colony (OABC) algorithm is proposed. To evaluate the obtained information, a target information entropy ratio model based on observation angles is proposed, considering the observation angle constraints under two conditions: whether there is an obstacle around the target or not. To efficiently find the optimal observation angles, half of the population that is lower-quality generates bit points through opposition-based learning. The algorithm searches for better individuals near the bit points when generating new solutions. Furthermore, to prevent individuals from observing targets repeatedly from similar angles, the concept of individual abandonment probability is proposed. The algorithm can adaptively abandon similar solutions based on the relative position between the individual and the population. To verify the effectiveness of the proposed method, information acquisition experiments were conducted on real residential buildings, and the results of 3D reconstruction were analyzed. The experiment results show that while model accuracy is comparable to that of the comparison method, the number of images obtained is reduced to one-fourth of the comparison method. The operation time is significantly reduced, and 3D reconstruction efficiency is remarkably improved.

Most heterogeneous change detection methods based on transfer learning may not yield satisfactory results due to the lack of comprehensive utilization of the global and local characteristics of the image. In this paper, we propose an unsupervised heterogeneous change detection method based on iterative joint global-local translation (IJGLT). The two heterogeneous images are firstly segmented into superpixels with the same boundary. Unlike the transfer learning methods for classification tasks that directly reduce the distance of the distribution of all data, we construct a translation function to decrease the difference between unchanged superpixels while increasing the difference of changed superpixels. During the translation process, the overall distribution is constrained using pseudo change labels, and the local manifold preserving is utilized to maintain the spatial structure and local adjacency relationship of superpixels. By comparing the translated superpixels of the heterogeneous images, the change map is obtained and then refined with pseudo change labels taking an iterative approach until convergence. Using the public and self-made optical-SAR heterogeneous datasets with different resolutions, the experimental results demonstrate that IJGLT is superior to several typical comparison methods with a maximum overall accuracy of 0.98 and a kappa coefficient of 0.9.

Inshore ship detection becomes challenging in high-resolution optical remote sensing image (RSI) because inshore ships are often incomplete and deformed due to the poor imaging condition and shadow of ship superstructure, and there are various interferences in harbor. A contour refinement and the improved generalized Hough transform (GHT)-based inshore ship detection scheme is proposed for RSI with complex harbor scenes. First, the suspected region of ships (SRS) is located in the entire RSI according to the line segments of ship body and docks. The contours in each SRS are then refined to repair the damaged ship head contour (SHC) using the convex set characteristics of ship head and subsequently reduce non-SHC by curvature filtering. In each refined SRS, equal frequency quantification instead of equal width quantification for R-Table construction and Gini coefficient-based decision criterion combining the number and distribution of votes are proposed to improve GHT (i.e., EG-GHT) and to extract SHCs as candidate targets. The false candidates are removed according to pixel proportion described by the structured binarization feature. Applying the border scoring strategy, the best candidates with the largest score among all the overlapped bounding boxes are selected as the final detection targets. Using the public RSIs with various cases, including turbid water, cloud occlusion, ships moored together, and ships with the different sizes, experimental results demonstrate the proposed scheme outperforms state-of-the-art contour-based methods and deep learning-based methods in terms of precision-recall rate and average precision, respectively.