International journal of applied earth observation and geoinformation : ITC journal最新文献

Satellite retrieval of bottom reflectance from high-spatial-resolution multispectral imagery in shallow coral reef waters

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-03-19 DOI: 10.1016/j.jag.2025.104483

Benqing Chen , Yanming Yang , Mingsen Lin , Bin Zou , Shuhan Chen , Erhui Huang , Wenfeng Xu , Yongqiang Tian

Under anthropogenic disturbances and global warming, coral reef ecosystems are degrading, and there is growing concern about the changes in benthic habitats in shallow coral reef waters. As an essential parameter, bottom reflectance can be used to indicate the health of benthic habitats in coral reefs. However, accurately determining bottom reflectance from satellite data remains challenging. This study presents an equation-based analytical method to estimate the bottom reflectance from high-spatial-resolution multispectral images in shallow coral reef waters by establishing two equations independent of bottom type and water depth. With the required parameters estimated from the sampling pixels of the multi-spectral image, the bottom reflectance data for the blue and green bands were derived by solving the two equations without a prior knowledge of bottom types, water properties, and water depths. To evaluate the method, simulated remote-sensing reflectance datasets from various combinations of the water properties, depths, and bottom types were used to derive the bottom reflectance. The root mean square errors (RMSEs) of the derived bottom reflectance in the blue band were generally <0.02 for most cases, except when the colored dissolved organic matter spectral absorption coefficient at the 440 nm wavelength [a_CDOM (440)] was 0.1 m⁻¹ and concentration of chlorophyll (C_CHL) was ≥0.5 μg/L. Comparatively, the lower RMSEs in the green band were observed only when a_CDOM(440) < 0.05 m⁻¹, concentration of non-algal particles (C_NAP) < 0.25 mg/L, and C_CHL < 0.5 μg/L. Furthermore, the proposed method was applied to the two real satellite multispectral images to derive the bottom reflectance. By visually comparing to the subsurface reflectance images and validating with the field-measured reflectance data, we demonstrated that the satellite derived bottom reflectance in the blue and green bands was accurate in both magnitude and shape by the proposed method. Finally, the impacts of the spatial inhomogeneity of the water properties, purity of sampling pixels for estimating the band ratio of the total diffused attenuation coefficients, and errors in the radiometric correction on the bottom reflectance retrieval were discussed and analyzed.

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引用次数: 0

Assessing urban residents’ exposure to greenspace in daily travel from a dockless bike-sharing lens

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-03-19 DOI: 10.1016/j.jag.2025.104487

Xijie Xu , Jie Wang , Stefan Poslad , Xiaoping Rui , Guangyuan Zhang , Yonglei Fan , Guangxia Yu

Considering the importance of greenspace for the health and life of urban citizens, different levels of greenspace exposure (GE) have received increasing attention. However, the understanding of human travel-related greenspace exposure is still limited, especially the lack of quantitative description of the fine-grained dynamics of greenspace exposure for active travel. Therefore, this study aims to quantify and analyse the spatio-temporal dynamics and equality of greenspace exposure during daily travel using dockless bike-sharing data in Beijing. Firstly, this study analysed the spatio-temporal patterns and community structure of bike-sharing travel using graph networks. Second, the daily travel-related greenspace exposure dynamics were estimated using a population-weighted exposure model. Finally, the spatial heterogeneity and equality of greenspace exposure during daily travel were assessed. The results show that greenspace exposure is shaped by both human mobility and greenspace distribution. Greenspace exposure is higher during the daytime than the early morning, and there are no significant changes of the average greenspace exposure across weekdays and weekends. In addition, there is an imbalance between greenspace coverage and exposure, with high greenspace coverage not implying high greenspace exposure and vice versa. Areas with lower greenspace coverage (less than 30 %) occurred for more than 80 % of the travels. We also found significant inequality of greenspace exposure during daily travel, with an average Gini index above 0.50. Driven by human mobility, inequality varied over time, with the highest inequality occurring between midnight and early morning, when the Gini index is higher than 0.65. This study provides a detailed understanding of greenspace exposure in active travel modes and may offer valuable insights for urban greenspace planning and health-oriented mobility strategies.

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引用次数: 0

Near real-time land surface temperature reconstruction from FY-4A satellite using spatio-temporal attention network

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-03-19 DOI: 10.1016/j.jag.2025.104480

Ruijie Li , Hequn Yang , Xu Zhang , Xin Xu , Liuqing Shao , Kaixu Bai

Land Surface Temperature (LST) is a critical parameter for climate studies and land surface process models as it indicates ground surface temperature variations across landscapes and timescales. However, satellite-based LST products derived from infrared sensors suffer from substantial missing values due to extensive cloud covers on the Earth’s surface. Traditional methods rely heavily on numerical LST simulations for gap-filling, but the latency significantly limits the timeliness of gapless LST products. In this study, a novel deep learning method called the Spatio-Temporal Attention Network (STAN) was proposed, which was based on a U-Net architecture but enhanced with two unique feature extraction modules for capturing spatially and temporally dependent LST variations. Unlike many previous methods depending highly on numerical simulations, STAN reconstructs LST relying on spatiotemporal context information learned from historical memories, enabling more efficient LST reconstruction in a more timely manner. Ground validation results demonstrate better performance of STAN over other companion methods, with root-mean-square errors of 1.99 K and 2.89 K under clear and cloudy sky respectively, when reconstructing LST data collected from the Chinese Fengyun-4A geostationary satellite in the Yangtze River Delta. Intercomparison studies and error analysis also confirm the superiority of STAN, showing high LST reconstruction accuracy across different land covers and seasons. Overall, the proposed STAN method offers a much more efficient solution to facilitate timely LST reconstruction, and the method can also be easily transferred to other parameters with significant spatio-temporal variation context.

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引用次数: 0

Using street view imagery and localized crowdsourcing survey to model perceived safety of the visual built environment by gender

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-03-18 DOI: 10.1016/j.jag.2025.104421

Hanlin Zhou , Jue Wang , Kathi Wilson , Michael Widener , Devin Yongzhao Wu , Eric Xu

Scholars have documented that perceived safety of the visual built environment (VBE) can influence human behaviors. The dual developments of street view imagery (SVI) and deep learning techniques offer a cost-effective approach to measure perceived safety. However, current SVI-based perception models often lack specific definitions of perceived safety and demographic information when collecting data for model training. Furthermore, these models are rarely validated by onsite perception evaluations, which undermines their credibility.

Given these gaps, this study builds a localized crowdsourcing survey to train crime-related and barrier-related perceived safety of the VBE captured by SVIs, and compares model-predicted perceptions with onsite perceptions. This study specifically focuses on their ability to represent onsite perceptions and examines gender differences as a test case in safety perception. This study recruits over 1,800 participants living in the Greater Toronto Area to rate SVIs in terms of crime-related and barrier-related perceived safety.

Pearson correlation coefficients reveal a positive but weak correlation between female and male safety perceptions, indicating some consistency while highlighting potential gender differences in safety perceptions. Machine-learning perception models are then trained using this localized SVI survey. Model-predicted perceptions are further validated to assess their alignments with onsite perceptions at sampling locations. The results show that model-predicted perceptions do not exactly match onsite perceptions but align better when less stringent criteria are applied (within ± 1 scale point).

In short, this study underscores the necessity of gender inclusivity and a clear definition of safety terms when using SVIs to model perceptions. While SVI-based perception models are cost-effective, the predicted perceptions cannot yet fully substitute onsite perceptions, necessitating broader research to refine the effectiveness.

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引用次数: 0

Transfer learning for enhancing the generality of leaf spectroscopic models in estimating crop foliar nutrients across growth stages

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-03-17 DOI: 10.1016/j.jag.2025.104481

Yurong Huang , Wenqian Chen , Wei Tan , Yujia Deng , Cuihong Yang , Xiguang Zhu , Jian Shen , Nanfeng Liu

China, despite being a leading producer of potatoes, has a potato yield below the global average, primarily due to inefficient nutrient management practices. Remote sensing provides a non-invasive and large-scale approach to monitor crop nutrient status, offering an efficient alternative to traditional plant tissue analysis. However, the generalization of foliar nutrient models is often constrained by factors such as growth stages and planting cultivars. Transfer learning offers a powerful solution by utilizing knowledge acquired from one task to enhance performance in related one, addressing challenges in model generalizability. Here, we investigated the potential of integrating various transfer learning techniques with partial least squares regression (PLSR) for retrieving three key potato foliar nutrients (nitrogen, phosphorus and potassium) across five growth stages (emergence, tuber initiation, early tuber bulking, mid-tuber bulking and tuber maturation). Three categories of transfer learning techniques were examined: 1) instance-based, including PLSR-KMM (kernel mean matching) and PLSR-TrAdaBoostR2 (transfer adaptive boosting for regression); 2) feature-based, including PLSR-TCA (transfer component analysis); and 3) parameter-based, including PLSR-parameter-based. We found that: 1) The combination of transfer learning techniques with PLSR could generally enhance the model transferability across growth stages, with a decrease in the normalized root mean squared error (nRMSE of 1–10 % for nitrogen, 3–60 % for phosphorous, and 1–15 % potassium; 2) The ranking of transfer learning techniques for improving model generalizability was: PLSR-TrAdaBootR2 > PLSR-parameter based > PLSR-recalibrated > PLSR-TCA > PLSR-KMM; 3) Foliar nitrogen demonstrated the highest transferability, followed by potassium and phosphorus; 4) PLSR models integrated with transfer learning techniques more effectively leveraged the absorption features of foliar biochemistry (e.g., chlorophyll, water and dry matters) to predict nutrients.

中国虽然是马铃薯的主要生产国，但马铃薯产量却低于全球平均水平，这主要是由于养分管理方法效率低下造成的。遥感技术为监测作物养分状况提供了一种非侵入性的大规模方法，可有效替代传统的植物组织分析。然而，叶面养分模型的通用性往往受到生长阶段和种植品种等因素的限制。迁移学习提供了一个强大的解决方案，它利用从一项任务中获得的知识来提高相关任务的性能，从而应对模型通用性方面的挑战。在此，我们研究了将各种迁移学习技术与偏最小二乘回归（PLSR）相结合，在五个生长阶段（出苗、块茎开始、块茎膨大早期、块茎膨大中期和块茎成熟）检索三种关键马铃薯叶面养分（氮、磷和钾）的潜力。对三类迁移学习技术进行了研究：1）基于实例，包括 PLSR-KMM（核均值匹配）和 PLSR-TrAdaBoostR2（用于回归的迁移自适应提升）；2）基于特征，包括 PLSR-TCA（迁移成分分析）；3）基于参数，包括基于 PLSR 参数。我们发现1）迁移学习技术与 PLSR 的结合总体上提高了模型在不同生长阶段的可迁移性，氮的归一化均方根误差（nRMSE）降低了 1-10%，磷降低了 3-60%，钾降低了 1-15%；2）迁移学习技术在提高模型泛化能力方面的排名是：PLSR-TrAdaB、PLSR-TrAdaB、PLSR-TrAdaB、PLSR-TrAdaB：PLSR-TrAdaBootR2；PLSR-基于参数；PLSR-重新校准；PLSR-TCA；PLSR-KMM；3）叶面氮的可迁移性最高，其次是钾和磷；4）与迁移学习技术相结合的 PLSR 模型能更有效地利用叶面生物化学的吸收特征（如叶绿素、水分和干物质）来预测养分。

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引用次数: 0

Free satellite data and open-source tools for urban green spaces and temperature pattern analysis in Algiers

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-03-17 DOI: 10.1016/j.jag.2025.104482

Nadia Mekhloufi , Mariella Aquilino , Amel Baziz , Chiara Richiardi , Maria Adamo

Rapid urbanization and global climate change are intensifying the Urban Heat Island (UHI) effect in cities worldwide, with consequences for human health and well-being. Urban green spaces (UGSs) mitigate extreme temperatures, but their cooling potential depends on spatial configuration, size, shape, and distribution. This study fills a geographic gap by providing one of the first detailed analyses of UGSs-Land Surface Temperature (LST) dynamics in a North African context. It combines spatial pattern analysis with temporal trend detection to comprehensively evaluate UGSs-LST relationships in Algiers from 2004 to 2022, addressing a common limitation in the literature where these approaches are often treated separately. Using freely available satellite data and open-source tools—including Landsat data from Google Earth Engine (GEE), QGIS, and RStudio—we integrate supervised classification, landscape metrics (LMs) computation via our custom PyQGIS LMs calculator, and Mann-Kendall trend analysis to quantify the static spatial configuration of green spaces and their dynamic thermal impacts over time. Findings reveal a 38% decrease in green spaces and a 10% reduction in agricultural land, accompanied by increased urbanization. Strong negative correlations between some LMs and LST were observed, with PLAND (Percentage of Landscape) explaining 61% of LST variability at an optimal 600-meter scale. This medium-sized scale differs from previous findings in other regions, highlighting the importance of context-specific analysis. LST trend analysis identified specific heat-resistant zones characterized by large, contiguous green patches. Despite greening initiatives, UGSs in Algiers continue to decline, underlining the need to preserve and strategically expand UGSs to combat rising temperatures.

快速城市化和全球气候变化正在加剧世界各地城市的城市热岛效应（UHI），对人类健康和福祉造成影响。城市绿地（UGS）可以缓解极端温度，但其降温潜力取决于空间配置、大小、形状和分布。本研究填补了这一地理空白，首次详细分析了北非地区的城市绿地-地表温度（LST）动态。该研究将空间模式分析与时间趋势检测相结合，全面评估了阿尔及尔 2004 年至 2022 年的 UGSs-LST 关系，解决了文献中通常将这两种方法分开处理的局限性。利用免费提供的卫星数据和开源工具（包括来自谷歌地球引擎 (GEE)、QGIS 和 RStudio 的 Landsat 数据），我们整合了监督分类、通过自定义 PyQGIS LMs 计算器进行的景观度量（LMs）计算以及 Mann-Kendall 趋势分析，以量化绿地的静态空间配置及其随时间变化的动态热影响。研究结果表明，随着城市化进程的加快，绿地减少了 38%，农田减少了 10%。一些 LMs 与 LST 之间存在很强的负相关，在最佳 600 米尺度上，PLAND（景观百分比）可解释 61% 的 LST 变化。这种中型尺度与其他地区的研究结果不同，凸显了针对具体情况进行分析的重要性。LST 趋势分析确定了以大片连续绿色斑块为特征的特定耐热区。尽管采取了绿化措施，但阿尔及尔的 UGS 仍在继续减少，这凸显了保护和战略性扩大 UGS 以应对气温上升的必要性。

{"title":"Free satellite data and open-source tools for urban green spaces and temperature pattern analysis in Algiers","authors":"Nadia Mekhloufi , Mariella Aquilino , Amel Baziz , Chiara Richiardi , Maria Adamo","doi":"10.1016/j.jag.2025.104482","DOIUrl":"10.1016/j.jag.2025.104482","url":null,"abstract":"<div><div>Rapid urbanization and global climate change are intensifying the Urban Heat Island (UHI) effect in cities worldwide, with consequences for human health and well-being. Urban green spaces (UGSs) mitigate extreme temperatures, but their cooling potential depends on spatial configuration, size, shape, and distribution. This study fills a geographic gap by providing one of the first detailed analyses of UGSs-Land Surface Temperature (LST) dynamics in a North African context. It combines spatial pattern analysis with temporal trend detection to comprehensively evaluate UGSs-LST relationships in Algiers from 2004 to 2022, addressing a common limitation in the literature where these approaches are often treated separately. Using freely available satellite data and open-source tools—including Landsat data from Google Earth Engine (GEE), QGIS, and RStudio—we integrate supervised classification, landscape metrics (LMs) computation via our custom PyQGIS LMs calculator, and Mann-Kendall trend analysis to quantify the static spatial configuration of green spaces and their dynamic thermal impacts over time. Findings reveal a 38% decrease in green spaces and a 10% reduction in agricultural land, accompanied by increased urbanization. Strong negative correlations between some LMs and LST were observed, with PLAND (Percentage of Landscape) explaining 61% of LST variability at an optimal 600-meter scale. This medium-sized scale differs from previous findings in other regions, highlighting the importance of context-specific analysis. LST trend analysis identified specific heat-resistant zones characterized by large, contiguous green patches. Despite greening initiatives, UGSs in Algiers continue to decline, underlining the need to preserve and strategically expand UGSs to combat rising temperatures.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104482"},"PeriodicalIF":7.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Large-Area DEM modeling of GF-7 stereo imagery: Integrating ICESat-2 data with Multi-characteristic constraint filtering and terrain matching correction

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-03-15 DOI: 10.1016/j.jag.2025.104485

Kai Chen , Wen Dai , Fayuan Li , Sijin Li , Chun Wang

The integration of Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data with Optical Photogrammetric Satellite Stereo Imagery (OPSSI) for Block Adjustment (BA) has emerged as a novel approach for generating large-area, high-accuracy Digital Elevation Models (DEMs). However, owing to the discrepancies between these two data platforms and the systematic errors of their sensors, errors arise in the BA fusion outcomes during the matching process of the two datasets. To tackle this issue, this paper proposes a method aimed at enhancing the accuracy of the BA process. Initially, the multi-characteristic constraint is used to filter the ICESat-2 ATL08 product to obtain control points and check points. Subsequently, the Terrain Matching Correction is applied to control points, and then integrated with the GF-7 OPSSI for BA to generate DEM. Ultimately, the check points are employed to assess the accuracy of the established DEM. Experiments in a 2,000 km² test area in the Wuding River Basin show that: (1) The inclusion of ICESat-2 data has remarkably enhanced the accuracy of DEM modeling utilizing GF-7 OPSSI, and the Root Mean Square Error (RMSE) has been reduced from the range of 5–10 m to 2–6 m. (2) Multi-characteristic constraint filtering is crucial for the identification of high quality ICESat-2 control points in flat and low relief areas. When implementing this filtering method, the established criteria should comprehensively consider both the quantity and the spatial distribution of control points to ensure optimal results. (3) Terrain Matching Correction on ICESat-2 data has effectively elevated the vertical accuracy of DEM modeling, particularly in regions with flat terrain. The RMSE of the vertical accuracy in such areas can be decreased by 1–3 m. In summary, the integration of spaceborne laser altimeter data with OPSSI holds immense significance for the production of large-scale and high-accuracy DEMs, offering a promising solution for terrain modeling and analysis on regional scales.

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引用次数: 0

VCDFormer: Investigating cloud detection approaches in sub-second-level satellite videos

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-03-15 DOI: 10.1016/j.jag.2025.104465

Xianyu Jin , Jiang He , Yi Xiao , Ziyang Lihe , Jie Li , Qiangqiang Yuan

Satellite video, as an emerging data source for Earth observation, enables dynamic monitoring and has wide-ranging applications in diverse fields. Nevertheless, cloud occlusion hinders the ability of satellite video to provide uninterrupted monitoring of the Earth’s surface. To mitigate the interference of clouds, cloud-free areas need to be selected before application, or an optimized solution like a cloud removal algorithm can be utilized to recover the occluded regions, both of which inherently demand the precise detection of clouds. However, no existing methods are capable of robust cloud detection in satellite videos. We propose the first sub-second-level satellite video cloud detection model VCDFormer to handle this problem. In VCDFormer, a spatial–temporal-enhanced transformer consisting of a local spatial–temporal reconfiguration block and a spatial-enhanced block is introduced to explore global spatial–temporal correspondence efficiently. Additionally, we construct WHU-VCD, the first sub-second-level synthetic dataset specifically designed to capture the more realistic motion characteristics of both thick and thin clouds in satellite videos. Compared to the state-of-the-art cloud detection methods, VCDFormer achieves an approximate 10%–15% improvement in the IoU metric and a 5%–8% increase in the F1-Score on the simulated test set. Experimental evaluations on Jilin-1 satellite videos, involving both synthetic and real-world scenarios, demonstrate that our proposed VCDFormer achieves superior performance in satellite video cloud detection tasks. The source code is available at https://github.com/XyJin99/VCDFormer.

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引用次数: 0

A framework for montane forest canopy height estimation via integrating deep learning and multi-source remote sensing data

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-03-15 DOI: 10.1016/j.jag.2025.104474

Hongbin Luo , Guanglong Ou , Cairong Yue , Bodong Zhu , Yong Wu , Xiaoli Zhang , Chi Lu , Jing Tang

Quantitative remote sensing-based forest parameter estimation is challenging in tropical mountainous conditions with complex topography and vegetation. To address this issue, we conducted a study utilizing Landsat 8, ALOS-2 PALSAR, and GEDI data. We applied an effective deep learning framework—Deep Markov Regression (DMR)—along with Random Forest Regression (RF) and 3D Regression Kriging (3DRK) methods to estimate canopy height in subtropical mountain forests. Our goal was to explore effective modeling techniques for this task. Additionally, we treated “slope” as a dummy variable and incorporated factors such as slope and geographic coordinates into the model. The results showed that optical remote sensing provided the highest estimation accuracy in mountainous terrain, significantly outperforming both GEDI and SAR data. The combination of multiple remote sensing datasets further enhanced the estimation accuracy. Incorporating slope and geographic location data also improved model performance. Among all methods, the RF model was most sensitive to topographic variations, whereas the DMR model consistently delivered excellent performance across different slope conditions. The R² of the DMR model was 0.772, the RMSE was 2.968 m, and the prediction accuracy approached 80 %.

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引用次数: 0

Generating high-resolution DEMs in mountainous regions using ICESat-2/ATLAS photons

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-03-14 DOI: 10.1016/j.jag.2025.104461

Yi Zhao , Bin Wu , Gefei Kong , He Zhang , Jianping Wu , Bailang Yu , Jin Wu , Hongchao Fan

High-resolution (≤10 m) digital elevation models (DEMs) are essential for obtaining accurate terrain information and are integral to geographic analysis. However, a majority of currently available DEMs datasets possess a relatively coarse spatial resolution (≥30 m), which limits the terrain features and details that can be accurately represented. Furthermore, due to the substantial production costs associated with high-resolution DEMs, these products are often unavailable or difficult to obtain in numerous countries and regions, particularly in less developed areas. Here, we introduced a novel method named the Spatial interpolation knowledge-constrained Conditional Generative Adversarial Network (SikCGAN). This method can generate high-resolution DEMs from publicly available data sources, specifically the photons collected by the Advanced Topographic Laser Altimeter System (ATLAS) carried by the Ice, Cloud and land Elevation Satellite-2 (ICESat-2). SikCGAN takes ICESat-2/ATLAS photons as the single data source and incorporates spatial interpolation knowledge constraints into a Conditional Generative Adversarial Network (CGAN) to generate DEMs at a 10-m spatial resolution. A case study conducted in boreal mountainous regions demonstrates SikCGAN’s remarkable ability to produce high-resolution and highly accurate DEMs, with an MAE of 22.09 m and RMSE of 29.25 m, which reduced error by 37 %–46 % compared to benchmark methods. Additionally, the results reveal that SikCGAN has remarkable resiliece to interference, including variations in spatial distance, terrain slope, and ATL03 photon count, this further elucidates and substantiates the effectiveness of SikCGAN. These findings demonstrate that SikCGAN provides innovative solutions for generating new high-resolution DEMs products and potentially supplementing existing ones to overcome their limitations.

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引用次数: 0