Xinhong Wang

Arable land is fundamental to agricultural production and a crucial component of ecosystems. However, its complex texture and distribution in remote sensing images make it susceptible to interference from other land cover types, such as water bodies, roads, and buildings, complicating accurate identification. Building on previous research, this study proposes an efficient and lightweight CNN-based network, U-MGA, to address the challenges of feature similarity between arable and non-arable areas, insufficient fine-grained feature extraction, and the underutilization of multi-scale information. Specifically, a Multi-Scale Adaptive Segmentation (MSAS) is designed during the feature extraction phase to provide multi-scale and multi-feature information, supporting the model’s feature reconstruction stage. In the reconstruction phase, the introduction of the Multi-Scale Contextual Module (MCM) and Group Aggregation Bridge (GAB) significantly enhances the efficiency and accuracy of multi-scale and fine-grained feature utilization. The experiments conducted on an arable land dataset based on GF-2 imagery and a publicly available dataset show that U-MGA outperforms mainstream networks (Unet, A2FPN, Segformer, FTUnetformer, DCSwin, and TransUnet) across six evaluation metrics (Overall Accuracy (OA), Precision, Recall, F1-score, Intersection-over-Union (IoU), and Kappa coefficient). Thus, this study provides an efficient and precise solution for the arable land recognition task, which is of significant importance for agricultural resource monitoring and ecological environmental protection.

Detecting small targets in infrared images presents significant challenges due to their tiny size and complex backgrounds, making this task a hotspot for research. Traditional methods rely on assumption-based modeling and manual design, struggling to handle the variability of real-world scenarios. Although convolutional neural networks (CNNs) increase robustness to diverse scenes with a data-driven paradigm, many CNN-based methods are insufficient in capturing fine-grained details necessary for small targets and are less effective during multi-scale feature fusion. To overcome these challenges, we propose the novel Wide-scale Gated Fully Fusion Network (WGFFNet) in this article, which contributes to infrared small-target detection (IRSTD). WGFFNet uses a classic encoder–decoder structure, where the designed stepped fusion block (SFB) embedded in the feature extraction stage captures finer local context across multiple scales during encoding, and along the decoding path, the multi-level features are progressively integrated by a Fully Gated Interaction (FGI) Module to enhance feature representation. The inclusion of a boundary difference loss further optimizes the edge details of targets. We conducted comprehensive experiments on two public infrared small-target datasets: SIRST-V2 and IRSTD-1k. Quantitative and qualitative results demonstrate that our WGFFNet outperforms representative methods when considering various evaluation metrics together, achieving an improved detection performance and computational efficiency for detecting small targets in infrared images.

The Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night band (DNB) data is very sensitive to low radiation and capable of detecting faint light sources emitted by vessels at night. Existing vessel detection methods are mainly based on pixel statistical feature estimation or manual experience to determine the threshold for detection. In complex scenes, problems such as interference targets and inaccurate background modeling result in inaccurate detection accuracy and poor robustness. In this paper, a vessel detection method based on an improved constant false alarm rate detector is proposed, and more accurate threshold selection is obtained through background sample truncation and distribution adjustment. Then, the local peak detection algorithm removes the bright spots in the non-vessel location and realizes the vessel detection. We selected data from two research areas, the waters around the East China Sea and the waters around the Gulf of Mexico of the United States, to build a vessel dataset and applied the algorithm to the dataset. The experimental results show that the total detection accuracy of the algorithm is 94.48%, and the recall rate is 93.40%, which is higher than the other two comparison methods. Especially in complex scenarios with high target density, such as ports, the recall rate is significantly improved, which proves the applicability of the algorithm.

Robust calibration and validation (Cal and Val) should guarantee the accuracy of the retrieved information, make the remote sensing data consistent and traceable, and maintain the sensor performance during the operational phase. The DRAGON program has set up many remote sensing research topics on various application domains. In order to promote the effectiveness of data modeling and interpretation, it is necessary to solve various challenges in Cal and Val for quantitative RS applications. This project in the DRAGON 4 program aims to promote the cooperation of the Cal and Val experts from European and Chinese institutes in Cal and Val activities, and several achievements have been obtained in the advanced on-orbit optical sensor calibration, as well as microwave remote sensor calibration and product generation. The outcomes of the project have benefited the related remote sensing modeling and product retrieval, and promoted the radiometric calibration network (RadCalNet) as an international operational network for calibration, intercalibration, and validation. Moreover, this project provided local governments with a more accurate OMI NO₂ data in China, which were used to study the air quality control during APEC period, Parade period and G20 period. This will be of ongoing be value for monitoring atmospheric environmental quality and formulating pollution control strategies.