Search Results (421)

Traditional image feature matching methods often fail to achieve satisfactory performance on multimodal remote sensing images (MRSIs), mainly due to significant nonlinear radiometric distortion (NRD) and complex geometric deformation caused by different imaging mechanisms. The key to successful MRSI matching lies in preserving high-frequency edge structures that are robust to geometric deformation, while overcoming nonlinear intensity mappings induced by NRD. To address these challenges, this paper proposes a novel high-precision matching framework, termed structure-aware feature descriptor with multi-scale side window filtering (SA-SWF). The proposed framework consists of three stages: (1) an anisotropic morphological scale space is constructed based on multi-scale side window filtering to strictly preserve geometric edges, and feature points are extracted using a multi-scale adaptive structure tensor with sub-pixel refinement to ensure high localization precision; (2) a structure-aware feature descriptor is constructed by integrating gradient reversal invariance and entropy-weighted attention mechanisms, rendering the multi-modal description highly robust against contrast inversion and noise; and (3) a coarse-to-fine robust matching strategy is established to progressively refine correspondences from descriptor-space matching to strict sub-pixel geometric verification, thereby minimizing alignment errors. Experiments on 60 multimodal image pairs from six categories, including infrared-infrared, optical–optical, infrared–optical, depth–optical, map–optical, and SAR–optical datasets, demonstrate that SA-SWF consistently outperforms seven state-of-the-art competitors. Across all six dataset categories, SA-SWF achieves a 100% success rate, the highest average number of correct matches (356.8), and the lowest average root mean square error (1.57 pixels). These results confirm the superior robustness, stability, and geometric accuracy of SA-SWF under severe radiometric and geometric distortions. Full article

In recent years, deep learning has driven remarkable progress in remote sensing change detection (CD); however, practical deployment is still hindered by two limitations. First, CD results are easily degraded by imaging-induced uncertainties—mixed pixels and blurred boundaries, radiometric inconsistencies (e.g., shadows and seasonal illumination changes), and slight residual misregistration—leading to pseudo-changes and fragmented boundaries. Second, prevailing methods follow a static one-pass inference paradigm and lack an explicit feedback mechanism for adaptive error correction, which weakens generalization in complex or unseen scenes. To address these issues, we propose a feedback-driven CD framework that integrates a dual-branch U-Net with deep reinforcement learning (RL) for pixel-level probabilistic iterative refinement of an initial change probability map. The backbone produces a preliminary posterior estimate of change likelihood from multi-scale bi-temporal features, while a PPO-based RL agent formulates refinement as a Markov decision process. The agent leverages a state representation that fuses multi-scale features, prediction confidence/uncertainty, and spatial consistency cues (e.g., neighborhood coherence and edge responses) to apply multi-step corrective actions. From an imaging and interpretation perspective, the RL module can be viewed as a learnable, self-adaptive imaging optimization mechanism: for high-risk regions affected by blurred boundaries, radiometric inconsistencies, and local misalignment, the agent performs feedback-driven multi-step corrections to improve boundary fidelity and spatial coherence while suppressing pseudo-changes caused by shadows and illumination variations. Experiments on four datasets (CDD, SYSU-CD, PVCD, and BRIGHT) verify consistent improvements. Using SiamU-Net as an example, the proposed RL refinement increases mIoU by 3.07, 2.54, 6.13, and 3.1 points on CDD, SYSU-CD, PVCD, and BRIGHT, respectively, with similarly consistent gains observed when the same RL module is integrated into other representative CD backbones. Full article

As climate change increasingly impacts the water cycle across the Alpine region, monitoring surface soil moisture is essential for hydrological models and drought early warning. Yet operational products either mask steep terrain, or lack the spatial resolution to capture the surface soil moisture (SSM) spatial variability of the Alpine catchments. This study presents a novel retrieval approach aggregating Sentinel-1 radiometric terrain-corrected backscatter (${\gamma }^0$) into 100 m elevation bands per sub-basin and aspect across the Austrian Alps. The resulting Alpine backscatter product is processed through an orbit-wise change detection to derive over 34,000 SSM timeseries, evaluated using ERA5-Land and compared to 264 precipitation stations from Geosphere for the period from 2016 to 2024. The results show satisfactory agreement with ERA5-Land (Pearson correlation > 0.46 below 400 m) and capture in situ precipitation-driven anomalies with the strongest performance below 400 m (Spearman correlation > 0.47), particularly over grasslands and south-facing slopes. Despite its limitations at high elevation and over dense vegetation, Sentinel-1 provides consistent and elevation-stratified information across more than 80% of the Austrian Alps, typically excluded from operational products. The new Alpine SSM product highlights Sentinel-1’s potential to support hydrological modeling, drought monitoring, and water resource management across complex topography such as the Alps. Full article

High-fidelity static map construction is essential for reliable autonomous navigation, yet dynamic environments introduce severe artifacts caused by moving objects (also referred to as dynamic artifacts) in accumulated maps. While geometry-based methods perform well on dense point clouds, their performance notably degrades on sparse 16-beam LiDAR due to the “Sparsity Trap”: dynamic objects are frequently missed by ray-based geometry, and purely geometric cues fail in radiometrically ambiguous scenarios. To address this, we propose RI-DVP, a physics–geometry dual-driven framework. Unlike conventional approaches, RI-DVP first performs a physics-inspired radiometric normalization that compensates for range attenuation and incidence-angle effects to establish a consistent signal baseline. Subsequently, a Dual-Residual Aggressive Removal (DRAR) module jointly exploits geometric residuals—bounded by a range-dependent spatial uncertainty envelope—and calibrated intensity residuals to detect geometrically indistinguishable objects. To balance recall and precision, a Hierarchical Static Reversion strategy (HSR) employs two-stage recovery to retrieve large-scale structures and correct fine-grained artifacts via topology-based adhesion reasoning. Experiments on SemanticKITTI and custom sparse datasets demonstrate that RI-DVP outperforms state-of-the-art geometric baselines, improving Dynamic Accuracy by over 36 percentage points in sparse scanning scenarios using a VLP-16 LiDAR sensor (Velodyne Acoustics, Inc., Morgan Hill, CA, USA) compared to baselines that fail under the sparsity trap while achieving real-time performance at approximately 15.3 Hz. Full article

Monitoring agricultural production in developing nations is essential for assessing food security. Nevertheless, persistent cloud cover in tropical regions severely limits optical satellite observations, and ground-truth data for classification validation are typically unavailable. This study developed a remote sensing methodology to classify agricultural land in southern Uasin Gishu County, Kenya, using weather-independent Synthetic Aperture Radar (SAR) imagery without requiring in situ training data. We processed 29 Sentinel-1 C-band VH-polarized scenes through the Alaska Satellite Facility’s Radiometric Terrain Correction pipeline. We computed the Coefficient of Variation (CV) across the 2017 time series to quantify temporal backscatter variance. VH polarization was selected over VV because a preliminary analysis showed that VV sensitivity to water surface dynamics confounded the CV algorithm. Preprocessing masks excluded water bodies, urban areas, and edge pixels to reduce classification errors from non-agricultural sources of temporal variability. Unsupervised ISO Cluster classification partitioned the CV raster into land-cover classes, and a Python-based statistical analysis determined optimal threshold values. Active agriculture pixels (n = 581,807) exhibited a mean CV of 0.469 (SD = 0.087), while non-agricultural pixels (n = 623,484) showed a mean CV of 0.274 (SD = 0.049). The optimal classification threshold of 0.357, determined by the intersection of fitted normal distributions, achieved an overall accuracy of 87.5% (Kappa = 0.73) when validated against Sentinel-2 reference imagery. User’s accuracy for agriculture was 96.6%, indicating that pixels classified as agricultural were highly reliable, while omission errors reducing producer’s accuracy to 84.6% were primarily attributable to edge pixels and land cover types where preprocessing masks or threshold placement excluded pixels exhibiting intermediate temporal dynamics. The classification identified approximately 810 km² of actively cultivated land (54% of the southern study area), corresponding to an estimated 69,500 to 162,200 metric tonnes (assuming 30–70% maize fraction) of potential maize production based on FAO yield data. The methodology provides a replicable, cost-effective tool for food security monitoring in cloud-prone regions where ground-truth data are unavailable. Full article

Remote sensing has revolutionized monitoring landscapes that are inaccessible or impractical to survey on the ground. Satellite platforms such as Sentinel-2 enable assessment of ecosystem changes over extensive areas with high temporal frequency, while Unmanned Aerial Systems (UAS) offer flexible, ultra-high-resolution observations ideal for site-specific analysis and sensitive environments. This study compares the performance of Sentinel-2 and Phantom 4 multispectral RTK data for monitoring vegetation dynamics in Mediterranean shrubland ecosystems, focusing on the Normalized Difference Vegetation Index (NDVI). Both platforms produced broadly consistent patterns in seasonal and interannual vegetation dynamics. However, UAS outperformed satellite data in capturing fine-scale heterogeneity, regeneration patches, and subtle disturbance responses, particularly in sparsely vegetated or heterogeneous terrain where satellite metrics may be insensitive. The comparison of NDVI across platforms accounted for standardized processing, harmonization, radiometric and atmospheric correction, and spatial resolution differences. Results show platform selection can be optimized according to monitoring objectives: satellite data are well suited for long-term monitoring of landscape-level vegetation dynamics, as both platforms capture consistent patterns when evaluated at comparable, spatially aggregated scales, while UAS data provide critical detail for localized management, early stress detection, and restoration prioritization by resolving fine-scale features. A combined approach enhances ecosystem disturbance assessments and resource management by binding the strengths of both wide-area coverage and precise spatial detail. Full article

Systematic radiometric anomalies, manifesting as non-physical range-direction oscillations, significantly compromise the quality of Miniature Radio Frequency (Mini-RF) S-band SAR imagery and its scientific application in the lunar south polar region. In this study, we analyzed 1262 scenes from the Mini-RF archive in south polar regions. By employing a statistical screening method based on fitting the relationship of backscattering signal and off-nadir angle, 377 scenes (29.9%) were identified as radiometrically anomalous scenes with systematic errors. To correct these errors, a physics-based radiometric correction framework has been proposed by reconstructing the effective antenna gain pattern (AGP) of Mini-RF. Referenced relationship between the backscattering signal and the local incidence angle was established using normal scenes. For each anomalous scene, a simulation-driven gradient descent optimization approach is developed to estimate the offset of the AGP. Subsequently, the derived offset is applied to realign the AGP of the anomalous scene, effectively compensating for the systematic range-direction oscillations and restoring the true backscatter intensity. Using the proposed method, systematic errors in anomalous scenes have been eliminated effectively, reducing the Root Mean Square Error (RMSE) relative to the reference radiometric curve from 2.11 to 1.21 and decreasing the image entropy from 2.83 to 2.29. By eliminating systematic banding artifacts, the proposed method has significantly improved the radiometric fidelity of Mini-RF data. Furthermore, a temporal periodicity was found in the gain offsets, suggesting dynamic instrument distortion driven by variations in the orbital thermal environment. Full article

This article investigates the estimation of water quality parameters, specifically chlorophyll-a, applying machine learning techniques to Sentinel-2 images. This study focuses on five small reservoirs located in the Extremadura region (Spain), as these are the ones for which continuous daily records from automatic in situ sensors are available. Chlorophyll-a estimates are obtained from two sources: (1) From the C2RCC atmospheric correction of Sentinel-2 images using Sen2Cor and radiometric calibration to ensure temporal consistency, and (2) from in situ data obtained from the official website of the Guadiana Basin Automatic Network Information System. The machine learning (ML)-based methodology significantly improves the predicted results for inland water bodies, enabling enhanced continuous assessment of water quality in small reservoirs. Full article

This study presents a measurement–modeling pathway for diagnosing corrosion-driven contact degradation in power equipment using infrared thermography and color-channel analysis. Thermal data were acquired with a Fluke Ti450 (LWIR, 7.5–14 μm) under typical high-altitude, temperate conditions in Quito, Ecuador. Radiometric parameters (emissivity, distance, ambient/reflected temperature, and humidity) are reported explicitly, and images are processed with a reproducible pipeline that combines adaptive thresholding, morphology, and region-of-interest statistics, including $\Delta T$ relative to a reference region. A worked example links an observed hotspot to emissivity-corrected temperature and discusses qualitative implications for the effective contact resistance $R_{\mathrm{eff}}$. Uncertainty is summarized through a per-case template that propagates $u_{\Delta T}$ to $u\left(R_{\mathrm{eff}}\right)$ and Weibull characteristic life $\eta$. Environmental influences (solar load, wind, and emissivity variability) are acknowledged and mitigated. Two field cases illustrate the approach to substation assets. Because the dataset comprises single-visit inspections, formal parameter estimation (e.g., EIS-validated $R_{\mathrm{eff}}$ and full Weibull/Arrhenius fits) is reserved for longitudinal follow-up. By making radiometry, processing steps, and limitations explicit, the study reduces ambiguity in the transition from temperature contrast to physics-based interpretation and supports auditable maintenance decisions. Full article

The photovoltaic (PV) bands on the mid-wave and long-wave infrared (MWIR and LWIR) cold focal plane assemblies of Terra and Aqua MODIS have suffered from gradually increasing electronic crosstalk contamination as both instruments have continued to operate in their extended missions, respectively. This contamination has considerable impact, particularly for the PV LWIR bands, which includes image striping and radiometric bias in the Level-1B (L1B)-calibrated radiance products as well as higher level (and mostly atmospheric but also land and oceanic) products (e.g., cloud phase particle, cloud mask, land and sea surface temperatures). The crosstalk was characterized early in the mission, and test corrections were developed then. Ultimately, the groundwork for a robust electronic crosstalk correction algorithm was developed in 2016 and implemented in MODIS Collection 6.1 (C6.1) back in 2017 for the Terra MODIS PV LWIR bands. It was later introduced in Aqua MODIS C6.1 for the same group of bands in April 2022. Additional improvements were made in MODIS Collection 7 (C7) to better characterize the electronic crosstalk in the PV LWIR bands, and the electronic crosstalk correction algorithm was also extended to select detectors in the MODIS MWIR bands. This work will describe the electronic crosstalk correction algorithm and its application on the MODIS L1B product, the differences in application between C6.1 and C7, as well as additional improvements made to enhance the contamination correction and improve image quality for the Aqua MODIS PV LWIR bands. The electronic crosstalk correction coefficient time series for the MODIS PV bands will be discussed, and some cases will be presented to illustrate how image quality improves on the L1B and Level 2 products after the correction is applied. Lastly, experiences gained regarding the PV bands electronic crosstalk and the strategy used to correct it will be discussed to provide future data users and scientists with an insight as to how to improve on the legacy record that the Terra and Aqua MODIS sensors will leave behind after both spacecrafts are decommissioned. Full article

Accurate registration of optical and synthetic aperture radar (SAR) images is a fundamental prerequisite for multi-source remote sensing data fusion and analysis. However, due to the substantial differences in imaging mechanisms, optical–SAR image pairs often exhibit significant radiometric discrepancies and spatially varying geometric inconsistencies, which severely limit the robustness of traditional feature or region-based registration methods in cross-modal scenarios. To address these challenges, this paper proposes an end-to-end Optical–SAR Registration Network (OSR-Net) based on multi-constraint joint optimization. The proposed framework explicitly decouples cross-modal feature alignment and geometric correction, enabling robust registration under large appearance variation. Specifically, a multi-modal feature extraction module constructs a shared high-level representation, while a multi-scale channel attention mechanism adaptively enhances cross-modal feature consistency. A multi-scale affine transformation prediction module provides a coarse-to-fine geometric initialization, which stabilizes parameter estimation under complex imaging conditions. Furthermore, an improved spatial transformer network is introduced to perform structure-preserving geometric refinement, mitigating spatial distortion induced by modality discrepancies. In addition, a multi-constraint loss formulation is designed to jointly enforce geometric accuracy, structural consistency, and physical plausibility. By employing a dynamic weighting strategy, the optimization process progressively shifts from global alignment to local structural refinement, effectively preventing degenerate solutions and improving robustness. Extensive experiments on public optical–SAR datasets demonstrate that the proposed method achieves accurate and stable registration across diverse scenes, providing a reliable geometric foundation for subsequent multi-source remote sensing data fusion. Full article

Accurate nearshore bathymetry is essential for various marine applications, including navigation, resource management, and the protection of coastal ecosystems and the services they provide. This study presents an approach to enhance the accuracy of bathymetric estimates derived from high-spatial- and high-temporal-resolution optical satellite imagery. The proposed technique is particularly suited for multispectral sensors that acquire spectral bands sequentially rather than simultaneously. PlanetScope SuperDove imagery was employed and validated against bathymetric data collected using a multibeam echosounder. The study area is the Gulf of Sciacca, located along the southwestern coast of Sicily in the Mediterranean Sea. Here, multibeam data were acquired along transects that are subparallel to the shoreline, covering depths ranging from approximately 7 m to 50 m. Satellite imagery was radiometrically and atmospherically corrected and then processed using a simplified radiative transfer transformation to generate a continuous bathymetric map extending over the entire gulf. The resulting satellite-derived bathymetry achieved reliable accuracy between approximately 5 m and 25 m depth. Beyond these limits, excessive signal attenuation for higher depths and increased water turbidity close to shore introduced significant uncertainties. The innovative aspect of this approach lies in the combined use of spectral averaging among the most water-penetrating bands, temporal averaging across multiple acquisitions, and a liquid-facets noise reduction technique. The integration of these multi-layer inputs led to improved accuracy compared to using single-date or single-band imagery alone. Results show a strong correlation between the satellite-derived bathymetry and multibeam measurements over sandy substrates, with an estimated error of ±6% at a 95% confidence interval. Some discrepancies, however, were observed in the presence of mixed pixels (e.g., submerged vegetation or rocky substrates) or surface artifacts. Full article

The absolute radiometric calibration of a satellite sensor is an essential process that determines the coefficients required to convert the radiometric quantities of satellite images. This procedure is crucial for ensuring the applicability and enhancing the reliability of optical sensors onboard satellites. This study performs the absolute radiometric calibration of the Compact Advanced Satellite 500-1 (CAS500-1) Advanced Earth Imaging Sensor System-C (AEISS-C), a low Earth orbit satellite developed independently by Republic of Korea for precise ground observation. Field campaign using a tarp, an Analytical Spectral Devices FieldSpecIII spectroradiometer, and a MicrotopsII sunphotometer was conducted. Additionally, reflectance-based vicarious calibration was performed using observational data and the MODerate resolution atmospheric TRANsmission model (version 6) radiative transfer model (RTM). Cross-calibration was also performed using data from the Sentinel-2 MultiSpectral Instrument, RadCalNet observations, and MODIS Bidirectional nReflectance Distribution Function (BRDF) products (MCD43A1) to account for differences in spectral response functions, viewing/solar geometry, and atmospheric conditions between the two satellites. From these datasets, two correction factors were derived: the Spectral Band Adjustment Factor and the BRDF Correction Factor. CAS500-1/AEISS-C acquires satellite imagery using two Time Delay Integration (TDI) modes, and the absolute radiometric calibration coefficients were derived considering these TDI modes. The coefficient of determination (R²) ranged from 0.70 to 0.97 for the reflectance-based vicarious calibration and from 0.90 to 0.99 for the cross-calibration. For reflectance-based vicarious calibration, aerosol optical depth was identified as the primary source of uncertainty among atmospheric factors. For cross-calibration, the reference satellite and RTMs were the primary sources of uncertainty. The results of this study will support the monitoring of CAS500-1/AEISS-C, which produces high-resolution imagery with a spatial resolution of 2 m, and can serve as foundational material for absolute radiometric calibration procedures for other CAS500 satellites. Full article

Radiometric calibration of multispectral imagery plays a critical role in the determination of vegetation-related features. This radiometric calibration strongly depends on a proper sensor configuration when acquiring images, the shutter speed being a critical parameter. The objective of the present study was to appraise the influence of shutter speed on the reflectance in the visible and near-infrared (NIR) spectral regions registered by a low-cost multispectral sensor (MAPIR Survey3) on a homogeneous field of turfgrass (Cynodon dactylon L.—Poaceae) and on the vegetation index (VI) values calculated from them, under different solar radiation conditions. For this purpose, 10 shutter speed configurations were tested in field campaigns with variable solar radiation values. The main results demonstrated that the reflectance in the green spectral region was more sensitive to shutter speed than that of the red and NIR spectral regions, particularly under high solar radiation conditions. Moreover, VIs calculated using the green band were more sensitive to slow shutter speeds, thus presenting a higher probability of providing meaningless artifact values. In conclusion, this study provides shutter speed recommendations under different illumination conditions to optimize the reflectance and the VI sensitivity within the image, which can be applied as a simple method to optimize image acquisition from unmanned aerial vehicles under varying solar radiation conditions. Full article

In hyperspectral images, ghost image residuals exceeding a certain threshold not only reduce the recognition accuracy of the imaging detection system but also decrease the target identification rate. Ghost image residuals affect both the recognition accuracy of the detection system and the accuracy of spectral calibration, thereby influencing qualitative and quantitative inversion. Conventional ghost image residual correction methods can significantly affect both the relative and absolute calibration accuracy of hyperspectral images. To minimize the impact on spectral calibration accuracy during ghost image residual correction, we propose a ghost image degradation model and an iterative optimization algorithm. In the proposed approach, a ghost image residual degradation model is constructed based on the point spread function (PSF) of ghost image residuals and their energy distribution characteristics. Using the proportion of ghost image residuals and the accuracy of hyperspectral image calibration as constraints, we iteratively optimized typical regional target ghost image residuals across different spectral channels, achieving automated correction of ghost image residuals in various spectral bands. The experimental results show that the energy proportion of ghost image residuals at different wavelengths decreased from 4.6% to 0.3%, the variations in spectral curves before and after correction were less than 0.8%, and the change in absolute radiometric calibration accuracy was below 0.06%. Full article