Due to the minimum antenna area constraints of synthetic aperture radar (SAR), high-resolution and wide-swath (HRWS) imaging is difficult to be achieved using classical modes, such as scan, spotlight, and so on. Range ambiguity is one of the main technical challenges limiting HRWS imaging in spaceborne SAR. Inspired by the cocktail party effect, an advanced scheme based on blind source separation (BSS) is put forward to suppress the range ambiguity of spaceborne SAR in this article. In the scheme, multiple subantennas are used to collect multiple echo datasets, which are given different patterns. In this way, the echo signal of the desired region and that of the ambiguous region are weighted using different coefficients. During postprocessing, a detailed flow with the BSS algorithm is proposed to separate the desired echoes from those datasets. To verify the scheme, point target and scene simulations based on GF-3 are carried out. The results indicate that the range ambiguity can be reduced by more than 15 dB using the proposed scheme, the processing flow is effective, and the scheme has a good performance without increasing the complexity of the system design under the current SAR system conditions. The scheme has the potential to be adopted in future spaceborne SAR missions.

Realistic hyperspectral image (HSI) super-resolution (SR) techniques aim to generate a high-resolution (HR) HSI with higher spectral and spatial fidelity from its low-resolution (LR) counterpart. The generative adversarial network (GAN) has proven to be an effective deep learning framework for image SR. However, the optimization process of existing GAN-based models frequently suffers from the problem of mode collapse, leading to the limited capacity of spectral–spatial invariant reconstruction. This may cause the spectral–spatial distortion to the generated HSI, especially with a large upscaling factor. To alleviate the problem of mode collapse, this work has proposed a novel GAN model coupled with a latent encoder (LE-GAN), which can map the generated spectral–spatial features from the image space to the latent space and produce a coupling component to regularize the generated samples. Essentially, we treat an HSI as a high-dimensional manifold embedded in a latent space. Thus, the optimization of GAN models is converted to the problem of learning the distributions of HR HSI samples in the latent space, making the distributions of the generated SR HSIs closer to those of their original HR counterparts. We have conducted experimental evaluations on the model performance of SR and its capability in alleviating mode collapse. The proposed approach has been tested and validated based on two real HSI datasets with different sensors (i.e., AVIRIS and UHD-185) for various upscaling factors (i.e., $\times 2$ , $\times 4$ , and $\times 8$ ) and added noise levels (i.e., $\infty $ , 40, and 80 dB) and compared with the state-of-the-art SR models (i.e., hyperspectral coupled network (HyCoNet), low tensor-train rank (LTTR), band attention GAN (BAGAN), SR-GAN, and WGAN). Experimental results show that the proposed model outperforms the competitors on the SR quality, robustness, and alleviation of mode collapse. The proposed approach is able to capture spectral and spatial details and generate more faithful samples than its competitors. It has also been found that the proposed model is more robust to noise and less sensitive to the upscaling factor and has been proven to be effective in improving the convergence of the generator and the spectral–spatial fidelity of the SR HSIs.

Ships are important targets for marine surveillance in both military and civilian domains. Since the rise of deep learning, ship detection in synthetic aperture radar (SAR) images has achieved significant progress. However, the variability in ship size and resolution, especially the widespread presence of numerous small-sized ships, continues to pose challenges for effective ship detection in SAR images. To address the challenges posed by small ship targets, we propose an enhanced YOLO network to improve the detection accuracy of small targets. Firstly, we propose a Shuffle Re-parameterization (SR) module as a replacement for the C2f module in the original YOLOv8 network. The SR module employs re-parameterized convolution along with channel shuffle operations to improve feature extraction capabilities. Secondly, we employ the space-to-depth (SPD) module to perform down-sampling operations within the backbone network, thereby reducing the information loss associated with pooling operations. Thirdly, we incorporate a Hybrid Attention (HA) module into the neck network to enhance the feature representation of small ship targets while mitigating the interference caused by surrounding sea clutter and speckle noise. Finally, we add the shape-NWD loss to the regression loss, which emphasizes the shape and scale of the bounding box and mitigates the sensitivity of Intersection over Union (IoU) to positional deviations in small ship targets. Extensive experiments were carried out on three publicly available datasets—namely, LS-SSDD, HRSID, and iVision-MRSSD—to demonstrate the effectiveness and reliability of the proposed method. In the small ship dataset LS-SSDD, the proposed method exhibits a notable improvement in average precision at an IoU threshold of 0.5 (AP50), surpassing the baseline network by over 4%, and achieving an AP50 of 77.2%. In the HRSID and iVision-MRSSD datasets, AP50 reaches 91% and 95%, respectively. Additionally, the average precision for small targets (AP) exhibits an increase of approximately 2% across both datasets. Furthermore, the proposed method demonstrates outstanding performance in comparison experiments across all three datasets, outperforming existing state-of-the-art target detection methods. The experimental results offer compelling evidence supporting the superior performance and practical applicability of the proposed method in SAR small ship detection.

Ship detection in synthetic aperture radar (SAR) images holds significant importance for both military and civilian applications, including maritime traffic supervision, marine search and rescue operations, and emergency response initiatives. Although extensive research has been conducted in this field, the interference of speckle noise in SAR images and the potential discontinuity of target contours continue to pose challenges for the accurate detection of multi-directional ships in complex scenes. To address these issues, we propose a novel ship detection method for SAR images that leverages edge deformable convolution combined with point set representation. By integrating edge deformable convolution with backbone networks, we learn the correlations between discontinuous target blocks in SAR images. This process effectively suppresses speckle noise while capturing the overall offset characteristics of targets. On this basis, a multi-directional ship detection module utilizing radial basis function (RBF) point set representation is developed. By constructing a point set transformation function, we establish efficient geometric alignment between the point set and the predicted rotated box, and we impose constraints on the penalty term associated with point set transformation to ensure accurate mapping between point set features and directed prediction boxes. This methodology enables the precise detection of multi-directional ship targets even in dense scenes. The experimental results derived from two publicly available datasets, RSDD-SAR and SSDD, demonstrate that our proposed method achieves state-of-the-art performance when benchmarked against other advanced detection models.

To achieve high-resolution and wide-swath (HRWS) imaging, a space-time waveform-encoding (STWE) spaceborne synthetic aperture radar (SAR) system is adopted. In rugged terrain, the beam-pointing mismatch problem will appear when the traditional digital beamforming (DBF) technique is used to separate the received echoes. This problem leads to decreasing the received echo’s gain, deteriorating the quality of the image product and making the interpretation of SAR image difficult. To address this problem, an advanced echo separation scheme for STWE spaceborne SAR based on the DBF and blind source separation (BSS) is put forward in this paper. In the scheme, the echoes are transmitted within the adjacent pulse repetition intervals to simulate multiple beams, and the scattered echoes are received by the sixteen-channel antennas in elevation simultaneously. In post-processing, a detailed flow is adopted. In the method, the DBF is firstly performed on received echoes. Due to the error caused by terrain undulation, the degree of echo separation is not enough. Then, the BSS is performed on the multiple echoes obtained after the DBF processing. Finally, the highly separated echo signal can be obtained. In this scheme, there is no need to perform the direction of arrival (DOA) estimation before the DBF processing, which saves valuable computing resources. In addition, to verify the proposed scheme, point target and distributed target simulations based on the 16-channel data of an elevation X-band DBF-SAR system are carried out. The results of point targets indicate that the residual echo caused by rough terrain can be reduced by more than 14 dB using the proposed scheme. The proposed scheme can be directly implemented into existing SAR systems; thus, it does not increase the complexity of the system design. The scheme has the potential to be applied to future spaceborne SAR missions.

Spectral-spatial-based deep learning models have recently proven to be effective in hyper-spectral image (HSI) classification for various earth monitoring applications such as land cover classification and agricultural monitoring. However, due to the nature of ``black-box'' model representation, how to explain and interpret the learning process and the model decision, especially for vegetation classification, remains an open challenge. This study proposes a novel interpretable deep learning model--a biologically interpretable two-stage deep neural network (BIT-DNN), by incorporating the prior-knowledge (i.e., biophysical and biochemical attributes and their hierarchical structures of target entities)-based spectral-spatial feature transformation into the proposed framework, capable of achieving both high accuracy and interpretability on HSI-based classification tasks. The proposed model introduces a two-stage feature learning process: in the first stage, an enhanced interpretable feature block extracts the low-level spectral features associated with the biophysical and biochemical attributes of target entities; and in the second stage, an interpretable capsule block extracts and encapsulates the high-level joint spectral-spatial features representing the hierarchical structure of biophysical and biochemical attributes of these target entities, which provides the model an improved performance on classification and intrinsic interpretability with reduced computational complexity. We have tested and evaluated the model using four real HSI data sets for four separate tasks (i.e., plant species classification, land cover classification, urban scene recognition, and crop disease recognition tasks). The proposed model has been compared with five state-of-the-art deep learning models. The results demonstrate that the proposed model has competitive advantages in terms of both classification accuracy and model interpretability, especially for vegetation classification.