Suleman Mazhar

In remote sensing image pansharpening, the fundamental objective is to generate high-resolution multispectral (HRMS) image that preserves spectral integrity while enhancing spatial resolution. However, contemporary approaches lack the ability to perceive the quality of input images, thereby failing to preserve desired performance in complex real-world scenarios characterized by panchromatic (PAN) image degradation (e.g., noise contamination or sensor limitations). To address this problem, we present a novel degradation-aware Taylor expansion approximation (DTEA) network for pansharpening, where DTEA includes the following key procedures: Firstly, the PAN image is hierarchically decomposed into feature maps to represent the degradation information through a proposed Taylor expansion approximation network (TEANet). Next, a multi-level information fusion network (MIFNet) is employed to integrate these feature maps with LRMS images, yielding fused maps with rich spatial and spectral information. Finally, the fused map from each layer is utilized to synthesize the desired HRMS image through inverse Taylor expansion, thereby overcoming diverse information degradation. To rigorously evaluate the effectiveness of our DTEA network, we conduct a systematic performance analysis across PAN images with diverse qualities. Extensive quantitative and qualitative experiments on three datasets demonstrate that our method outperforms state-of-the-art approaches while exhibiting excellent generalization capability in real-world scenarios. Source code will be made publicly available on https://github.com/MysterYxby/DTEA.

The underwater acoustic (UWA) communication system faces challenges due to environmental factors, extensive multipath spread, and rapidly changing propagation conditions. Deep learning based solutions, especially for orthogonal frequency division multiplexing (OFDM) receivers, have been shown to improve performance. However, the UWA channel characteristics are highly dynamic and depend on the specific underwater conditions. Therefore, these models suffer from model mismatch when deployed in environments different from those used for training, leading to performance degradation and requiring costly, time-consuming retraining. To address these issues, we propose a transfer learning (TL)-based pre-trained model for OFDM based UWA communication. Rather than training separate models for each underwater channel, we aggregate received signals from five distinct WATERMARK channels, across varying signal to noise ratios (SNRs), into a unified dataset. This diverse training set enables the model to generalize across various underwater conditions, ensuring robust performance without extensive retraining. We evaluate the pre-trained model using real-world data from Qingdao Lake in Hangzhou, China, which serves as the target environment. Our experiments show that the model adapts well to these challenging environment, overcoming model mismatch and minimizing computational costs. The proposed TL-based OFDM receiver outperforms traditional methods in terms of bit error rate (BER) and other evaluation metrics. It demonstrates strong adaptability to varying channel conditions. This includes scenarios where training and testing occur on the same channel, under channel mismatch, and with or without fine-tuning on target data. At 10 dB SNR, it achieves an approximately 80% improvement in BER compared to other methods.

Underwater acoustic (UWA) communication presents unique challenges due to the unpredictable and dynamic nature of acoustic channels, influenced by Doppler spread, low signal-to-noise ratios (SNRs), and the general need for complex channel characteristics, coupled with a scarcity of real-world data. Accurate orthogonal frequency division multiplexing (OFDM) channel estimation is pivotal for ensuring reliable data transmission in such challenging environments. In this study, we introduce the DenseNet estimator, which is specifically used for OFDM channel estimation in UWA communication. The use of dense connectivity within the DenseNet structure proves to be advantageous in capturing the intricacies of the complex and dynamic UWA channels. This architecture, showcasing robustness even when there's a limited number of pilots, sets it apart from conventional methods. The DenseNet estimator is trained on the WATERMARK data set, leveraging the richness of real-time varying channel impulse responses to provide the necessary diversity for accurate channel estimation. Uniquely, once trained, our DenseNet estimator operates without necessitating additional channel statistics like SNR, relying solely on the received signal as its primary input. This approach offers a simplified and more direct application in real-world scenarios. Our numerical results underscore the DenseNet estimator's efficacy: It consistently outperforms traditional methods such as least square, minimum mean square error, and fully connected neural network, recording improvements of up to 96.3%, 94.2%, and 40.0% in bit error rate. Performance assessments across various watermark underwater channels demonstrate the DenseNet estimator's adaptability and robustness in both stable and challenging environments.

Synthetic aperture radar (SAR) plays a crucial role in maritime surveillance due to its capability for all-weather, all-day operation. However, SAR ship recognition faces challenges, primarily due to the imbalance and inadequacy of ship samples in publicly available datasets, along with the presence of numerous outliers. To address these issues, this paper proposes a SAR ship classification method based on text-generated images to tackle dataset imbalance. Firstly, an image generation module is introduced to augment SAR ship data. This method generates images from textual descriptions to overcome the problem of insufficient samples and the imbalance between ship categories. Secondly, given the limited information content in the black background of SAR ship images, the Tokens-to-Token Vision Transformer (T2T-ViT) is employed as the backbone network. This approach effectively combines local information on the basis of global modeling, facilitating the extraction of features from SAR images. Finally, a Squeeze-and-Excitation (SE) model is incorporated into the backbone network to enhance the network’s focus on essential features, thereby improving the model’s generalization ability. To assess the model’s effectiveness, extensive experiments were conducted on the OpenSARShip2.0 and FUSAR-Ship datasets. The performance evaluation results indicate that the proposed method achieves higher classification accuracy in the context of imbalanced datasets compared to eight existing methods.

A novel network for enhancement of underwater optical images is proposed in this paper. It contains a Reinforcement Fusion Module for Haar wavelet (RFM-Haar) images. Fusion is performed to enrich available underlying information for better enhancement. As this network transform Haar Images into Fusion Images, it is called HIFI-Net. The experimental results based on Mean Square Error (0.1952, 0.1512 and 0.4683), Peak Signal to Noise Ratio (25.2241, 26.3342 and 21.4253) and Structure Similarity Index Measure (0.8265, 0.8819 and 0.8012) on three public datasets (EUVP, UFO-110 and UIEB) demonstrate that the proposed HIFI-Net performs better than some existing state-of-the-art methods.

In this paper, a novel infrared maritime small target detection method, called local dissimilarity measure with anti interference based on global graph clustering (LDMGGC), is proposed. The Wasserstein distance is introduced to calculate the dissimilarity of gray level distribution between a central region and its neighborhoods. These dissimilarities construct the feature of a region. With this feature, detection for recalling all suspected targets is achieved. As the maritime interferences among suspected targets are able to be clustered, relaxing mutual k nearest neighbor graph is introduced in global graph clustering for filtering interferences. With this method, real targets are detected and maritime interferences are filtered out. Experiments are conducted on three maritime datasets and a non-maritime dataset for comparison. On three datasets, the proposed method achieves the best Receiver Operating Characteristic curves and Area Under Curve (0.99529, 0.99945, 0.99573, and 0.9906) values, meaning that the proposed method has high detection probability and low false-alarm ratio. Target Hit Rate (98.04%, 97.96%, 100%, and 99.24%) and Intersection of Union (0.8170, 0.7542, 0.5824, 0.7707) on four datasets of the proposed method show it has a strong ability to suppress the interferences