We developed an extension of a previously proposed classification scheme that is based upon Freeman–Durden and Cloude–Pottier decompositions of polarimetric Synthetic Aperture Radar (SAR) data, along with a Double-Bounce Eigenvalue Relative Difference (DERD) parameter, and a Random Forest (RF) classifier. The extension was done, firstly, by using dual-copolarization SAR data acquired at shorter wavelengths (C- and X-band, in addition to the previously used L-band) and, secondly, by adding indicators derived from the (polarimetric) Kennaugh elements. The performance of the newly developed classification scheme, herein abbreviated as FCDK-RF, was tested using SAR data of exposed intertidal flats. We demonstrate that the FCDK-RF scheme is capable of distinguishing between different sediment types, namely mud and sand, at high spatial accuracies. Moreover, the classification scheme shows good potential in the detection of bivalve beds on the exposed flats. Our results show that the developed FCDK-RF scheme can be applied for the mapping of sediments and habitats in the Wadden Sea on the German North Sea coast using multi-frequency and multi-polarization SAR from ALOS-2 (L-band), Radarsat-2 (C-band) and TerraSAR-X (X-band).

Fine-grained ship detection tasks require models to accurately classify fine-grained categories and precisely localize them within complex backgrounds, relying on detailed features. The challenges of this task mainly lie in bird’s-eye viewpoints, scale variations, rotational changes, and environmental factors, which lead to minor inter-class differences and significant intra-class variations. This paper presents a novel model, called Wavelet Transform-based Dual-Stream Backbone Network (WTDBNet), which effectively integrates three key strengths: the ability of the Transformer to model long-range dependencies for global context, the capability of convolutional neural networks to extract detailed local features, and the efficiency of wavelet transform in frequency-domain decomposition for enhancing edges and texture details. These components are fused via channel and spatial attention mechanisms, thereby improving the model’s ability to extract discriminative features. The effectiveness of WTDBNet is validated on two widely used benchmarks for fine-grained oriented ship detection, as well as on a self-constructed dataset designed to represent complex scenarios. Experimental results demonstrate the superior performance of the proposed method.

With the rapid development of remote sensing technology, the question of how to leverage large amounts of unlabeled remote sensing data to detect changes in multi-temporal images has become a significant challenge. Self-supervised methods (SSL) for remote sensing image change detection (CD) can effectively address the issue of limited labeled data. However, most SSL algorithms for CD in remote sensing image rely on convolutional neural networks with fixed receptive fields as their feature extraction backbones, which limits their ability to capture objects of varying scales and model global contextual information in complex scenes. Additionally, these methods fail to capture essential topological and structural information from remote sensing images, resulting in a high false positive rate. To address these issues, we introduce a graph diffusion model into the field of CD and propose a novel network architecture called CGD-CD Net, which is driven by a structure-sensitive SSL strategy based on contrastive learning. Specifically, a superpixel segmentation algorithm is applied to bi-temporal images to construct graph nodes, while the k-nearest neighbors algorithm is used to define edge connections. Subsequently, a diffusion model is employed to balance the states of nodes within the graph, enabling the co-evolution of adjacency relationships and feature information, thereby aggregating higher-order feature information to obtain superior feature embeddings. The network is trained with a carefully crafted contrastive loss function to effectively capture high-level structural information. Ultimately, high-quality difference images are generated from the extracted bi-temporal features, then use thresholding analysis to obtain a final change map. The effectiveness and feasibility of the suggested method are confirmed by experimental results on three different datasets, which show that it performs better than several of the top SSL-CD methods.

Ship detection in remote sensing imagery serves as a cornerstone of modern maritime surveillance. Existing visible light detectors suffer from severe performance degradation in adverse environmental conditions (e.g., fog, low light) due to domain gaps. Traditional global enhancement methods often lack adaptability, leading to “negative transfer”—where artifacts are introduced into clean images or mismatched with degradation types. To address these challenges, we propose CLEAR (Cognitive Large Language Model (LLM)-Empowered Adaptive Restoration) framework. Inspired by the dual-process theory of cognition, we introduce a dynamic switching mechanism between fast perception and deep reasoning. Rather than processing all images indiscriminately, it utilizes a hybrid gating mechanism to efficiently filter nominal samples, triggering Vision–Language Model (VLM) only when necessary to diagnose degradation and dispatch targeted restoration operators. Extensive experiments on the constructed HRSC-Robust dataset demonstrate that CLEAR achieves an overall mean Average Precision (mAP) at 0.5 Intersection-over-Union (IoU) of 86.92%, outperforming the baseline by 7.74%. Notably, it establishes a “fail-safe” mechanism for optical degradations. By adaptively resolving fog and low-light, it effectively mitigates detector blindness—exemplified by a doubled Recall rate (52.52%) in dark scenarios. Furthermore, a confidence-based sparse triggering strategy ensures operational efficiency, maintaining a throughput of ~11.8 FPS in nominal conditions. This work validates the potential of VLMs for interpretable and robust remote sensing tasks.

Automatic Dependent Surveillance-Broadcast (ADS-B) systems provide continuous aircraft position reports for aviation surveillance and short-term trajectory prediction. However, many data-driven predictors directly model the longitude, latitude, and altitude contained in ADS-B messages as generic multivariate time-series variables, which can weaken latitude-dependent displacement, bearing, and local motion relationships during multi-step forecasting. This paper proposes Geometry-Informed Adaptive Time-Series Fusion (GeoATF), a forecasting framework for short-term aircraft trajectory prediction from ADS-B position reports. For multi-route learning, each input window is mapped from global absolute coordinates to a local geodesic chart anchored at the last observation. The predicted local offsets are subsequently decoded into World Geodetic System 1984 (WGS-84) coordinates through great-circle forward navigation. GeoATF combines a patch-based time-series Transformer (PatchTST) temporal branch for modeling historical ADS-B position sequences with a geometric–kinematic branch that constructs great-circle distance, bearing, velocity component, and trajectory change rate features from the same sequence. An adaptive branch weight fusion module then learns horizon-dependent geometric–kinematic branch weights for residual correction. Experiments on 945 ADS-B flights from ten routes demonstrate that GeoATF delivers more accurate multi-horizon trajectory forecasts than the evaluated baselines, with more pronounced advantages at longer forecast horizons. Flight-level statistical analysis supports the robustness of these improvements, while resource evaluation indicates a moderate computational footprint under the common inference protocol. These results suggest that explicitly representing geometric–kinematic information and adaptively fusing it with temporal features can improve short-term aircraft trajectory prediction using only historical ADS-B position data.