Accurate relative radiometric normalization (RRN) is important for reliable multitemporal remote sensing image analysis. Traditional methods often depend on coregistered image pairs, limiting their applicability with unregistered data. Keypoint-based RRN (KRRN) relaxes this constraint but remains affected by residual radiometric errors due to normalization inaccuracies and nonlinear effects. This letter introduces a refinement strategy that leverages the Trust-Region Reflective (TRR) algorithm to optimize normalization parameters, coupled with Laplacian Pyramid (LP) fusion for seamless image integration. Evaluation on four multispectral image pairs from different sensors (e.g., Landsat 8 and Sentinel-2, IRS and Landsat 5, Landsat 7 and SPOT-5, and UK-DMC2 and Landsat 5) and one pair from the same sensor (Sentinel-2) showed that our method reduces residual radiometric discrepancies, achieving up to 29% lower RMSE than some well-known models. The source code and datasets are available on GitHub: https://github.com/ArminMoghimi/Tensor-based-keypoint-detection.

In recent years, detecting and monitoring landslides have become increasingly critical for disaster management and mitigation efforts. Here, we propose a model for landslide detection utilizing a combination of the Swin Transformer architecture with multi-scale feature fusion lateral connection and local information aggregation modules. The Swin Transformer, known for its effectiveness in image understanding tasks, serves as the backbone of our detection system. By leveraging its hierarchical self-attention mechanism, the Swin Transformer can effectively capture both local and global contextual information from input images, facilitating accurate feature representation. To increase the performance of the Swin Transformer specifically for landslide detection, we introduce two additional modules: the multi-scale feature fusion lateral connection module and the local information aggregation module. The former module enables the integration of features across multiple scales, allowing the model to capture both fine-grained details and broader contextual information relevant to landslide characteristics. Meanwhile, the latter module focuses on aggregating local information within regions of interest, further refining the model's ability to discriminate between landslide and non-landslide areas. Through extensive test and evaluation of benchmark datasets, our proposed method demonstrates promising results in detecting landslides with high mIoU, F1 score, kappa, precision, and recall 84.2, 90.7%, 82.6%, 89.9%, and 91.9%, respectively. Moreover, its robustness to variations in terrain and environmental conditions suggests its potential for real-world applications in landslide monitoring and early warning systems. Overall, our study highlights the effectiveness of integrating advanced transformer architectures with tailored modules for addressing complex geospatial challenges like landslide detection.

Detecting earthquake-induced landslides in remote sensing images is challenging due to the varying sizes of landslides, uneven distribution, and the prevalence of small targets. This study proposes a novel approach, the TLSTMF-YOLO model, which combines a C3-Swin-Transformer and multiscale feature fusion techniques to enhance detection accuracy and efficiency. Key innovations include the use of a convolutional block attention module (CBAM) to improve feature representation, and a bidirectional feature pyramid network (BiFPN) for optimized cross-scale feature fusion. To address data scarcity, a transfer learning strategy is applied, supported by an AdamW optimizer and cosine learning rate strategy for faster convergence. Evaluations on the Jiuzhaigou and Luding landslide datasets demonstrate the model’s effectiveness, achieving precision, recall, and mean average precision (mAP)@0.5 of 95.7%, 89.9%, and 90.5% on the Jiuzhaigou dataset, and 96.0%, 90.9%, and 94.5% on the Luding dataset, respectively. In addition, the model processes frames efficiently, with times of 6.61 and 12.2 ms on the two datasets. These results confirm the model’s capability for accurate and efficient landslide detection, highlighting its potential for real-world applications.

Nowadays, unmanned aerial vehicle (UAV) remote sensing data are key operational sources used to produce a reliable building damage map (BDM), which is of great importance in instant response and rescue operations after earthquakes. The present study proposes a novel weighted ensemble transferred U-Net-based model (WETUM) consisting of two major steps to create a reliable binary BDM using UAV data. In the first step of the proposed approach, three individual initial BDMs are predicted by three pre-trained U-Net-based composite networks. In the second step, these three individual predictions are linearly integrated through a proposed grid search technique so that an optimized hybrid BDM (OHBDM) incorporating complementary damage information is made. The proposed WETUM was then compared with several conventional deep learning (DL) and machine learning (ML) models. The models were compared across two pivotal scenarios, addressing the impact of diverse feature sets on model performance and generalizability. Specifically, the first scenario focused solely on spectral features, while the second incorporated both spectral and geometrical features. To make the comparisons, this study conducted empirical analyses using UAV spectral and geometrical data acquired over Sarpol-e Zahab, Iran. The experimental findings showed that the synergic use of spectral and geometrical data boosted both DL- and ML-based approaches in damage detection. Moreover, the proposed WETUM with DDR values of 65.22 and 78.26 (%), respectively, for the first and second scenarios, outperformed all the compared methods. Notably, WETUM with only spectral data outperformed the random forest (RF) classifier equipped with many hand-crafted spectral and geometrical features, indicating the highest potential and generalizability of the proposed WETUM for building damage evaluation in a new unseen earthquake-affected area.

The shipping industry is pivotal in transporting approximately 90% of the world’s goods, and it is characterized by evolving trends in vessel sizes and energy-efficient designs. Continuous advancements in technology for ship management have focused on detecting and analyzing anomalous and illicit vessels. In this study, we introduce ShipGeoNet, a model designed to extract geometric features from ships captured in Sentinel-1 synthetic aperture radar (SAR) images. ShipGeoNet employs a combination of convolutional neural networks (CNNs) and nonlinear regression techniques to extract various geometric features of ships from SAR imagery. The model follows a two-step approach. First, it utilizes a modified Mask R-CNN architecture and the ViTDet model to accurately detect ships, generating high-quality object masks for precise localization. In the subsequent step, a regression model utilizes the detected ship masks to extract key geometric attributes, including length, width, and orientation. The proposed nonlinear regression techniques are specifically crafted to address the complex nonlinear deformations inherent in SAR images. Through extensive experiments on a large-scale SAR dataset, ShipGeoNet demonstrates its efficiency and accuracy in ship size extraction and matching, outperforming existing methods. Developing the ShipGeoNet model opens up possibilities for future applications in maritime surveillance, navigation, and environmental monitoring.

Abstract Security is a major challenge in the design and implementation of Internet‐of‐Things (IoT)‐Fog networks and it can be resolved by an efficient authentication mechanism. As the devices have limited resources like battery, processor and memory, any mechanism designed for IoT‐Fog has to be resource aware. In this paper, we proposed a lightweight, secure, dynamic, and efficient scheme for mutual authentication of devices in IoT‐Fog networks. In the proposed scheme, the devices obtain a primary security value from the cloud server and this acquired primary security value is used to authenticate them mutually. We used XOR operation, concatenation operation, one‐way hash function and symmetric key cryptography to accommodate resource constraint nature of devices. The proposed scheme has been analyzed for security formally using BAN logic and automated using Automated Validation of Internet Security Protocols and Applications & ProVerif tools and it is proved that the proposed scheme is secure from the known attacks. The comparative analysis of the proposed scheme with the existing schemes proved that it is better than existing schemes and its experimental analysis showed that it consumes 20%–30% less energy compared to existing schemes. Our proposed scheme is better with reference to security and efficient in respect of computation, communication, and storage overheads.

Ionospheric phase delay is a critical error source in Time Series Interferometric Synthetic Aperture Radar (TS-InSAR) for the purpose of monitoring ground surface deformation with SAR data obtained from low-frequency radar systems. Recently, the Range Split-Spectrum Interferometry (RSSI) method has been employed to estimate and rectify ionospheric errors in TS-InSAR. However, the performance of the RSSI method is largely restricted by the significant linear scale factors resulting from the current small SAR bandwidth. In this study, we propose a Reformulating RSSI (Re-RSSI)-based method for correcting the ionospheric error in TS-InSAR by optimizing the linear scale factors, with the aim of improving the accuracy of TS-InSAR measurements. We evaluate the Re-RSSI method using 121 ALOS-1 PALSAR images that cover two distinct regions: the low-latitude Lazufre volcano region and the high-latitude Anaktuvuk River tundra fire region. Our results demonstrate that the Re-RSSI method can effectively remove time series ionospheric errors at both test sites, where we detected ionospheric delays of approximately 2.5 cm/yr and 2.0 cm/yr, respectively. Using Global Navigation Satellite System (GNSS) measurements as ground truth, we achieved an 86.59% improvement rate in root mean square error (RMSE) with the Re-RSSI method, which is significantly higher than the 66.40% improvement rate achieved with the traditional RSSI method.

Synthetic aperture radar interferometry (InSAR) technology has been widely used for landslide monitoring in mountainous areas. The troposphere in steep mountainous areas is affected by the variable topography, temperature, and humidity, which differs from that in plain areas and thus exhibits large spatial heterogeneity. Traditional InSAR troposphere correction methods are limited in this area, and the accuracy of InSAR measurements will be significantly affected. In this paper, we proposed a tropospheric delay correction method based on deep learning (AtmNet) without external data considering the spatial-heterogenetiy in each individual interferogram. The tropospheric correction and landslides monitoring based on Sentinel-1 SAR data was carried out in Mao County, a high landslide-prone area in southwest Sichuan Province (China). A simulation experiment was conducted to analyze the adaptability of the model and evaluate the effectiveness of the AtmNet method. Furthermore, we demonstrated the good performance of the AtmNet method through a comparison with the linear model (LM) and GACOS method, revealing that the proposed method could effectively model the spatial heterogeneity of tropospheric delay in steep mountains. The slope displacements that cannot be seen in the interferogram were very clear after the tropospheric delay correction. This method provides important technical support for the accurate DInSAR and time-series InSAR for landslide monitoring in steep mountainous areas in the future.

Multiple aperture interferometric synthetic aperture radar (MAI) can measure ground displacements along SAR track. However, MAI measurements may suffer from severe ionospheric error, especially for long-wavelength SAR sensors. This study presents a new approach, azimuth split-spectrum interferometry (AziSSI), to correct ionospheric errors in MAI measurements. The proposed method can straightforwardly resolve ionospheric phase delay by exploiting two subband MAI interferograms with different centroid frequencies. We utilized two groups of ALOS-1 PALSAR images, which cover the 2008 Wenchuan earthquake containing strong coseismic ground deformation and a stable coast region in Chile, respectively, to test the proposed method. Experimental results show that the AziSSI method can successfully recover the coseismic displacements induced by the Wenchuan earthquake and circumvent the azimuth stripes for the Chile case’s MAI measurements. The maximum ionosphere-induced azimuth shifts are about 2.2 m for the Wenchuan case and 2.5 m for the Chile case. We validated the correction performance of the AziSSI method with the Wenchuan case by comparing the compensated azimuth displacements with the simulated deformation from a forward fault slip model and coseismic global positioning system (GPS) measurements. The validations show that the deformation patterns after the ionospheric correction are consistent with the simulations of the sinistral slip of the causative fault. The root-mean-square error between the GPS and MAI measurements is reduced from 0.77 to 0.28 m before and after the ionospheric correction, indicating that AziSSI can significantly compensate ionospheric errors for MAI.

Finding robust and correct keypoints in images remains a challenge, especially when repetitive patterns are present. In this article, we propose a universal two-step filtering method to solve the mismatch problem in repetitive patterns. Having applied a mean-shift clustering algorithm to remove obvious mismatches, the proposed confusion reduction (CR) method uses a novel confusion index (CI) in a gridding schema to identify and filter out the remaining confusing keypoints. In both steps, the descriptors’ statistical properties are evaluated using kernel density estimation. Various synthetic and real stereo pairs, along with multiview image blocks, were used to assess the performance of the presented algorithm. The results were also compared with those obtained by several state-of-the-art mismatch removal methods. The experiments showed that, on average, the proposed strategy improves the accuracy of matching by 10% and the accuracy of photogrammetric blocks by 20%–30%.

This paper demonstrate the ability of MODIS data for mapping of seasonal salinity spatial distribution in Mersing and Semporna water. The objective of this work is to monitor the seasonal in-shore and open sea of sea surface salinity (SSS). The multi linear regression has been done for estimation salinity‥ In this study, the maximum amount salinity have been determined in the southwest Manson; which is 35.34 psu and the minimum value during north east Manson (31.60 psu). In conclusion, MODIS data can be used as a geomatica tool for accurately mapping of salinity surface along the semporna of Sabah, with implementation of multi linear and minnet algorithms.