Remote sensing applications require high-resolution images to obtain precise information about the Earth???s surface. Multispectral images have high spatial resolution but low spectral resolution. Hyperspectral images have high spectral resolution but low spatial resolution. This study proposes a residual learning and attention-based parallel network based on residual network and channel attention. The network performs image fusion of a high spatial resolution multispectral image and a low spatial resolution hyperspectral image. The network training and fusion experiments are conducted on four public benchmark data sets to show the effectiveness of the proposed model. The fusion performance is compared with classical signal processing???based image fusion techniques. Four image metrics are used for the quantitative evaluation of the fused images. The proposed network improved fusion ability by reducing the root mean square error and relative dimensionless global error in synthesis and increased the peak signal-to-noise ratio when compared to other state-of-the-art models.
{"title":"ReLAP-Net: Residual Learning and Attention Based Parallel Network for Hyperspectral and Multispectral Image Fusion","authors":"Aditya Agrawal, SourajaKundu, Touseef Ahmad, Manish Bhatt","doi":"10.14358/pers.24-00003r2","DOIUrl":"https://doi.org/10.14358/pers.24-00003r2","url":null,"abstract":"Remote sensing applications require high-resolution images to obtain precise information about the Earth???s surface. Multispectral images have high spatial resolution but low spectral resolution. Hyperspectral images have high spectral resolution but low spatial resolution. This study\u0000 proposes a residual learning and attention-based parallel network based on residual network and channel attention. The network performs image fusion of a high spatial resolution multispectral image and a low spatial resolution hyperspectral image. The network training and fusion experiments\u0000 are conducted on four public benchmark data sets to show the effectiveness of the proposed model. The fusion performance is compared with classical signal processing???based image fusion techniques. Four image metrics are used for the quantitative evaluation of the fused images. The proposed\u0000 network improved fusion ability by reducing the root mean square error and relative dimensionless global error in synthesis and increased the peak signal-to-noise ratio when compared to other state-of-the-art models.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141696124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Book Review ‐ Top 20 Essential Skills for ArcGIS Pro","authors":"","doi":"10.14358/pers.90.7.391","DOIUrl":"https://doi.org/10.14358/pers.90.7.391","url":null,"abstract":"","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141701834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.14358/pers.23-00085r2
Chaofei Zhang, Zhanghua Xu, Yuanyao Yang, Lei Sun, Haitao Li
To evaluate the spatiotemporal changes in the ecological environment of eastern Ukraine since the Russia-Ukraine conflict, this study used MODIS images from March to September 2020 and 2022 to calculate the Remote Sensing???Based Ecological Index. In 2022, compared with 2020, conflict zones exhibited reduced improvement and increased slight degradation, whereas nonconflict areas showed marginal enhancement. Through propensity score matching, the research confirmed the causal relationship between conflict and ecological trends. Pathway analysis revealed that the conflict contributed to 0.016 units increase in ecological quality while reducing the improvement rate by 0.042 units. This study provides empirical support for understanding the correlation between conflicts and specific environmental factors, offering technical references for ecological quality assessments in other conflict areas and future evaluations by the Ukrainian government.
{"title":"Dynamic Monitoring of Ecological Quality in Eastern Ukraine Amidst the Russia‐Ukraine Conflict","authors":"Chaofei Zhang, Zhanghua Xu, Yuanyao Yang, Lei Sun, Haitao Li","doi":"10.14358/pers.23-00085r2","DOIUrl":"https://doi.org/10.14358/pers.23-00085r2","url":null,"abstract":"To evaluate the spatiotemporal changes in the ecological environment of eastern Ukraine since the Russia-Ukraine conflict, this study used MODIS images from March to September 2020 and 2022 to calculate the Remote Sensing???Based Ecological Index. In 2022, compared with 2020, conflict\u0000 zones exhibited reduced improvement and increased slight degradation, whereas nonconflict areas showed marginal enhancement. Through propensity score matching, the research confirmed the causal relationship between conflict and ecological trends. Pathway analysis revealed that the conflict\u0000 contributed to 0.016 units increase in ecological quality while reducing the improvement rate by 0.042 units. This study provides empirical support for understanding the correlation between conflicts and specific environmental factors, offering technical references for ecological quality assessments\u0000 in other conflict areas and future evaluations by the Ukrainian government.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141715119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.14358/pers.24-00013r2
Yebin Zou
Remote sensing has been applied to observe large areas of surface water to obtain higher-resolution and long-term continuous observation records of surface water. However, limitations remain in the detection of large-scale and multi-temporal surface water mainly due to the high variability in water surface signatures in space and time. In this study, we developed a surface water remote sensing information extraction model that integrates spectral and temporal characteristics to extract surface water from multi-dimensional data of long-term Landsat scenes to explore the spatiotemporal changes in surface water over decades. The goal is to extract open water in vegetation, clouds, terrain shadows, and other land cover backgrounds from medium-resolution remote sensing images. The average overall accuracy and average kappa coefficient of the classification were verified to be 0.91 and 0.81, respectively. Experiments applied to China’s inland arid area have shown that the method is effective under complex surface environmental conditions.
{"title":"A Surface Water Extraction Method Integrating Spectral and Temporal Characteristics","authors":"Yebin Zou","doi":"10.14358/pers.24-00013r2","DOIUrl":"https://doi.org/10.14358/pers.24-00013r2","url":null,"abstract":"Remote sensing has been applied to observe large areas of surface water to obtain higher-resolution and long-term continuous observation records of surface water. However, limitations remain in the detection of large-scale and multi-temporal surface water mainly due to the high variability\u0000 in water surface signatures in space and time. In this study, we developed a surface water remote sensing information extraction model that integrates spectral and temporal characteristics to extract surface water from multi-dimensional data of long-term Landsat scenes to explore the spatiotemporal\u0000 changes in surface water over decades. The goal is to extract open water in vegetation, clouds, terrain shadows, and other land cover backgrounds from medium-resolution remote sensing images. The average overall accuracy and average kappa coefficient of the classification were verified to\u0000 be 0.91 and 0.81, respectively. Experiments applied to China’s inland arid area have shown that the method is effective under complex surface environmental conditions.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141705139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.14358/pers.23-00070r2
Ruilin Wang, Meng Wang, Xiaofang Sun, Junbang Wang, Guicai Li
In ecologically vulnerable regions with intricate land use dynamics, such as ecotones, frequent and intense land use transitions unfold. Therefore, the precise and timely mapping of land use becomes imperative. With that goal, by using principal component analysis, we integrated Sentinel-1 and Sentinel-2 data, using an object-oriented methodology to craft a 10-meter-resolution land use map for the forest‐grassland ecological zone of the Greater Khingan Mountains spanning the years 2019 to 2021. Our research reveals a substantial enhancement in classification accuracy achieved through the integration of synthetic aperture radar‐optical remote sensing data. Notably, our products outperformed other land use/land cover data sets, excelling particularly in delineating intricate riverine wetlands. The 10-meter land use product stands as a pivotal guide, offering indispensable support for sustainable development, ecological assessment, and conservation endeavors in the Greater Khingan Mountains region.
{"title":"Enhancing Forest‐Steppe Ecotone Mapping Accuracy through Synthetic ApertureRadar‐Optical Remote Sensing Data Fusion and Object-based Analysis","authors":"Ruilin Wang, Meng Wang, Xiaofang Sun, Junbang Wang, Guicai Li","doi":"10.14358/pers.23-00070r2","DOIUrl":"https://doi.org/10.14358/pers.23-00070r2","url":null,"abstract":"In ecologically vulnerable regions with intricate land use dynamics, such as ecotones, frequent and intense land use transitions unfold. Therefore, the precise and timely mapping of land use becomes imperative. With that goal, by using principal component analysis, we integrated Sentinel-1\u0000 and Sentinel-2 data, using an object-oriented methodology to craft a 10-meter-resolution land use map for the forest‐grassland ecological zone of the Greater Khingan Mountains spanning the years 2019 to 2021. Our research reveals a substantial enhancement in classification accuracy\u0000 achieved through the integration of synthetic aperture radar‐optical remote sensing data. Notably, our products outperformed other land use/land cover data sets, excelling particularly in delineating intricate riverine wetlands. The 10-meter land use product stands as a pivotal guide,\u0000 offering indispensable support for sustainable development, ecological assessment, and conservation endeavors in the Greater Khingan Mountains region.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141714758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.14358/pers.24-00016r1
Jung-Kuan Liu, Rongjun Qin, Samantha T. Arundel
Uncrewed aerial systems (UASs) have been used to collect “pseudo field plot” data in the form of large-scale stereo imagery to supplement and bolster direct field observations to monitor areas in Alaska. These data supplement field data that is difficult to collect in such a vast landscape with a relatively short field season. Dense photogrammetrically derived point clouds are created and are facilitated to extract land cover data using a support vector machine (SVM) classifier in this study. We test our approach using point clouds derived from 1-cm stereo imagery of plots in the Alaska North Slope region and compare the results to field observations. The results show that the overall accuracy of six land cover classes (bare soil, shrub, grass, forb/herb, rock, and litter) is 96.8% from classified patches. Shrub had the highest accuracy (>99%) and forb/herb achieved the lowest (<48%). This study reveals that the approach could be used as reference data to check field observations in remote areas.
{"title":"Assessing the Utility of Uncrewed Aerial System Photogrammetrically Derived Point Clouds for Land Cover Classification in the Alaska North Slope","authors":"Jung-Kuan Liu, Rongjun Qin, Samantha T. Arundel","doi":"10.14358/pers.24-00016r1","DOIUrl":"https://doi.org/10.14358/pers.24-00016r1","url":null,"abstract":"Uncrewed aerial systems (UASs) have been used to collect “pseudo field plot” data in the form of large-scale stereo imagery to supplement and bolster direct field observations to monitor areas in Alaska. These data supplement field data that is difficult to collect in such\u0000 a vast landscape with a relatively short field season. Dense photogrammetrically derived point clouds are created and are facilitated to extract land cover data using a support vector machine (SVM) classifier in this study. We test our approach using point clouds derived from 1-cm stereo imagery\u0000 of plots in the Alaska North Slope region and compare the results to field observations. The results show that the overall accuracy of six land cover classes (bare soil, shrub, grass, forb/herb, rock, and litter) is 96.8% from classified patches. Shrub had the highest accuracy (>99%)\u0000 and forb/herb achieved the lowest (<48%). This study reveals that the approach could be used as reference data to check field observations in remote areas.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141706037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For this months GIS Tips & Tricks, we are revisiting the U.S. Geological Survey (USGS) National Map topoBuilder tool. topoBuilder was featured in this column shortly after its initial release in October 2022. Since then, topoBuilder has become the “goto” application for USGS topographic maps and recently updated to accommodate even more features. This month, our guest columnist, Ariel Doumbouya, USGS, provides an update and tutorial to some new features in topoBuilder.
{"title":"GIS Tips & Tricks ‐ USGS Adds 100K Topo Scale to OnDemand Map Products","authors":"Ariel Doumbouya","doi":"10.14358/pers.90.7.389","DOIUrl":"https://doi.org/10.14358/pers.90.7.389","url":null,"abstract":"For this months GIS Tips & Tricks, we are revisiting the U.S. Geological Survey (USGS) National Map topoBuilder tool. topoBuilder was featured in this column shortly after its initial release in October 2022. Since then, topoBuilder has become the “goto” application\u0000 for USGS topographic maps and recently updated to accommodate even more features. This month, our guest columnist, Ariel Doumbouya, USGS, provides an update and tutorial to some new features in topoBuilder.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141714472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Remote sensing image segmentation is a crucial technique in the field of land management. However, existing semantic segmentation networks require a large number of floating-point operations (FLOPs) and have long run times. In this paper, we propose a dual-path feature aggregation network (DPFANet) specifically designed for the low-latency operations required in land management applications. Firstly, we use four sets of spatially separable convolutions with varying dilation rates to extract spatial features. Additionally, we use an improved version of MobileNetV2 to extract semantic features. Furthermore, we use an asymmetric multi-scale fusion module and dual-path feature aggregation module to enhance feature extraction and fusion. Finally, a decoder is constructed to enable progressive up-sampling. Experimental results on the Potsdam data set and the Gaofen image data set (GID) demonstrate that DPFANet achieves overall accuracy of 92.2% and 89.3%, respectively. The FLOPs are 6.72 giga and the number of parameters is 2.067 million.
{"title":"Real-Time Semantic Segmentation of Remote Sensing Images for Land Management","authors":"Yinsheng Zhang, Ru Ji, Yuxiang Hu, Yulong Yang, Xin Chen, Xiuxian Duan, Huilin Shan","doi":"10.14358/pers.23-00083r2","DOIUrl":"https://doi.org/10.14358/pers.23-00083r2","url":null,"abstract":"Remote sensing image segmentation is a crucial technique in the field of land management. However, existing semantic segmentation networks require a large number of floating-point operations (FLOPs) and have long run times. In this paper, we propose a dual-path feature aggregation network\u0000 (DPFANet) specifically designed for the low-latency operations required in land management applications. Firstly, we use four sets of spatially separable convolutions with varying dilation rates to extract spatial features. Additionally, we use an improved version of MobileNetV2 to extract\u0000 semantic features. Furthermore, we use an asymmetric multi-scale fusion module and dual-path feature aggregation module to enhance feature extraction and fusion. Finally, a decoder is constructed to enable progressive up-sampling. Experimental results on the Potsdam data set and the Gaofen\u0000 image data set (GID) demonstrate that DPFANet achieves overall accuracy of 92.2% and 89.3%, respectively. The FLOPs are 6.72 giga and the number of parameters is 2.067 million.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141279914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As I write this column (today is 1 March 2024) and as many readers are probably aware, Esri is formally retiring ArcGIS Desktop (i. e., ArcMap). Retirement for ArcMap means that, there will be no new releases, i.e.no ArcGIS Desktop 10.9, and the ArcGIS Desktop Product Life Cycle will come to an end, that is, no additional software fixes or Esri support on 1 March 2026. As ArcGIS Desktop has been with us since it was released in June 2010, it has served the GIS community well, with a total of 17 releases and numerous patches and fixes for over 14 years! Quite a run. So, with that, I think it fitting that I should say goodbye to ArcMap with these last ArcMap Desktop Tips, a "Say goodbye to ArcMap".
{"title":"GIS Tips & Tricks – Say Goodbye to ArcMap; We Were Just Getting to Know You, Old Friend!","authors":"Al Karlin","doi":"10.14358/pers.90.6.329","DOIUrl":"https://doi.org/10.14358/pers.90.6.329","url":null,"abstract":"As I write this column (today is 1 March 2024) and as many readers are probably aware, Esri is formally retiring ArcGIS Desktop (i. e., ArcMap). Retirement for ArcMap means that, there will be no new releases, i.e.no ArcGIS Desktop 10.9, and the ArcGIS Desktop Product Life Cycle will\u0000 come to an end, that is, no additional software fixes or Esri support on 1 March 2026. As ArcGIS Desktop has been with us since it was released in June 2010, it has served the GIS community well, with a total of 17 releases and numerous patches and fixes for over 14 years! Quite a run. So,\u0000 with that, I think it fitting that I should say goodbye to ArcMap with these last ArcMap Desktop Tips, a \"Say goodbye to ArcMap\".","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141276238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.14358/pers.23-00073r2
Long Chen, Bo Wu, Ran Duan, Zeyu Chen
In global navigation satellite systems (GNSS)-denied environments, vision-based methods are commonly used for the positioning and navigation of aerial robots. However, traditional methods often suffer from accumulative estimation errors over time, leading to trajectory drift and lack real-time performance, particularly in large-scale scenarios. This article presents novel approaches, including feature-based cross-view image matching and the integration of visual odometry and photogrammetric space resection for camera pose determination in real-time. Experimental evaluation with real UAV datasets demonstrated that the proposed method reliably matches features in cross-view images with large differences in spatial resolution, coverage, and perspective views, achieving a root-mean-square error of 4.7 m for absolute position error and 0.33° for rotation error, and delivering real-time performance of 12 frames per second (FPS) when implemented in a lightweight edge device onboard UAV. This approach offters potential for diverse intelligent UAV applications in GNSS-denied environments based on real-time feedback control.
{"title":"Real-Time Cross-View Image Matching and Camera Pose Determination for Unmanned Aerial Vehicles","authors":"Long Chen, Bo Wu, Ran Duan, Zeyu Chen","doi":"10.14358/pers.23-00073r2","DOIUrl":"https://doi.org/10.14358/pers.23-00073r2","url":null,"abstract":"In global navigation satellite systems (GNSS)-denied environments, vision-based methods are commonly used for the positioning and navigation of aerial robots. However, traditional methods often suffer from accumulative estimation errors over time, leading to trajectory drift and lack\u0000 real-time performance, particularly in large-scale scenarios. This article presents novel approaches, including feature-based cross-view image matching and the integration of visual odometry and photogrammetric space resection for camera pose determination in real-time. Experimental evaluation\u0000 with real UAV datasets demonstrated that the proposed method reliably matches features in cross-view images with large differences in spatial resolution, coverage, and perspective views, achieving a root-mean-square error of 4.7 m for absolute position error and 0.33° for rotation error,\u0000 and delivering real-time performance of 12 frames per second (FPS) when implemented in a lightweight edge device onboard UAV. This approach offters potential for diverse intelligent UAV applications in GNSS-denied environments based on real-time feedback control.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141229254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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