Mingrui Xin, Yibin Fu, Weiming Li, Haoxuan Ma, Hongyang Bai
Abstract. The road segmentation task has become increasingly important in fields such as urban planning, traffic management, and environmental monitoring. However, most existing deep learning-based methods suffer from issues such as poor temporal effectiveness and connectivity, making it a significant challenge to achieve high-precision and high-efficiency road segmentation. We propose a road segmentation model based on a detail-enhanced lightweight transformer. Through the connectivity enhancement module, the issue of spatial information loss is addressed, enhancing the modeling capability of the road network connectivity. The model incorporates a detail-enhancement strategy to capture the relationship between roads and the environment, enhancing the perception and expression of details while maintaining low computational complexity. Furthermore, the use of a lightweight multiple feature fusion module promotes information fusion from features at different scales while a maintaining lightweight design. Extensive experiments on two publicly available datasets demonstrate that our method achieves the best performance in terms of real-time effectiveness and accuracy.
{"title":"DELFormer: detail-enhanced lightweight transformer for road segmentation","authors":"Mingrui Xin, Yibin Fu, Weiming Li, Haoxuan Ma, Hongyang Bai","doi":"10.1117/1.JRS.17.046507","DOIUrl":"https://doi.org/10.1117/1.JRS.17.046507","url":null,"abstract":"Abstract. The road segmentation task has become increasingly important in fields such as urban planning, traffic management, and environmental monitoring. However, most existing deep learning-based methods suffer from issues such as poor temporal effectiveness and connectivity, making it a significant challenge to achieve high-precision and high-efficiency road segmentation. We propose a road segmentation model based on a detail-enhanced lightweight transformer. Through the connectivity enhancement module, the issue of spatial information loss is addressed, enhancing the modeling capability of the road network connectivity. The model incorporates a detail-enhancement strategy to capture the relationship between roads and the environment, enhancing the perception and expression of details while maintaining low computational complexity. Furthermore, the use of a lightweight multiple feature fusion module promotes information fusion from features at different scales while a maintaining lightweight design. Extensive experiments on two publicly available datasets demonstrate that our method achieves the best performance in terms of real-time effectiveness and accuracy.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"116 1","pages":"046507 - 046507"},"PeriodicalIF":1.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139326641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Efficient compression is pertinent for the convenient storage, transmission, and processing of modern high-resolution hyperspectral images (HSI). We propose a high-performance HSI compression method using library-based spectral unmixing and tensor decomposition. Unlike the existing approaches, our proposed work incorporates unmixing in the compression framework and achieves significantly higher compression performance with negligible loss. The proposed library-based unmixing method includes an index for accurate endmember number estimation, followed by exact library pruning and a sparsity regularized formulation with norm-smoothing to compute the abundance maps. As the spectral library is available at the reconstruction (decoder) side also; compressing the abundance maps is as good as compressing the original HSI data. Since the abundance constraints used for the unmixing indicate the correlation of the abundance maps, compressing all abundance maps seems to cause redundant computation. A metric using the image smoothness and information measures is used here to identify the abundance map hardest to compress and the remaining part is left uncompressed. Subsequently, the work compresses the abundance map tensor using orthogonal PARAFAC decomposition with optimal rank determination. The orthogonalization process ensures that the factors span independent subspaces and reduces redundancy, whereas the rank selection prevents noisy or insignificant components. Extensive experiments are carried out to demonstrate that the unmixing workflow leads to negligible loss due to accurate endmember number estimation, exact library pruning, and accurate physically meaningful sparse inversion. Comparative assessments of compression efficacy suggest that the proposed work corresponds to better compression performance and higher classification accuracy.
{"title":"Unmixing aware compression of hyperspectral image by rank aware orthogonal parallel factorization decomposition","authors":"Samiran Das, Sandip Ghosal","doi":"10.1117/1.JRS.17.046509","DOIUrl":"https://doi.org/10.1117/1.JRS.17.046509","url":null,"abstract":"Abstract. Efficient compression is pertinent for the convenient storage, transmission, and processing of modern high-resolution hyperspectral images (HSI). We propose a high-performance HSI compression method using library-based spectral unmixing and tensor decomposition. Unlike the existing approaches, our proposed work incorporates unmixing in the compression framework and achieves significantly higher compression performance with negligible loss. The proposed library-based unmixing method includes an index for accurate endmember number estimation, followed by exact library pruning and a sparsity regularized formulation with norm-smoothing to compute the abundance maps. As the spectral library is available at the reconstruction (decoder) side also; compressing the abundance maps is as good as compressing the original HSI data. Since the abundance constraints used for the unmixing indicate the correlation of the abundance maps, compressing all abundance maps seems to cause redundant computation. A metric using the image smoothness and information measures is used here to identify the abundance map hardest to compress and the remaining part is left uncompressed. Subsequently, the work compresses the abundance map tensor using orthogonal PARAFAC decomposition with optimal rank determination. The orthogonalization process ensures that the factors span independent subspaces and reduces redundancy, whereas the rank selection prevents noisy or insignificant components. Extensive experiments are carried out to demonstrate that the unmixing workflow leads to negligible loss due to accurate endmember number estimation, exact library pruning, and accurate physically meaningful sparse inversion. Comparative assessments of compression efficacy suggest that the proposed work corresponds to better compression performance and higher classification accuracy.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"86 1","pages":"046509 - 046509"},"PeriodicalIF":1.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139328382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sally Deborah Pereira da Silva, Fernando Coelho Eugenio, Roberta Aparecida Fantinel, Lucio de Paula Amaral, Caroline Lorenci Mallmann, Fernanda Dias dos Santos, Alexandre Rosa dos Santos, Rudiney Soares Pereira
We aimed to combine the use of images obtained from remotely piloted aircraft systems (RPAS) and machine learning (ML) to identify the invasive alien species Psidium guajava in a protected area in southern Brazil. Field data were obtained in a sampling area, where the species’ geographic coordinates were collected with a global positioning system device. Remote data were collected with the Parrot Sequoia® multispectral camera onboard the Phantom 4® Pro platform. Image processing was used to generate reflectance maps and vegetation indices, after which four classes of interest were defined for model training. The supervised classification involved two approaches (pixel-based—BP and object-based image analysis—OBIA) and two ML algorithms compared (random forest—RF and support vector machine—SVM). For performance analysis, confusion matrices with user and producer accuracies, Kappa values and overall accuracy (OA) were calculated. The results demonstrate that the multispectral composition was excellent in identifying the invasive P. guajava, in an OBIA approach with the RF algorithm (0.90 Kappa and 93% OA). Thus, considering the priority of biodiversity conservation and the importance of the Brazilian Atlantic Forest for the maintenance of endemic and endangered species, we present a robust methodology to identify the invasive species P. guajava in subtropical forest, which can be applied in management strategies for the species control and eradication.
{"title":"Identification of invasive trees in a Brazilian subtropical forest using remotely piloted aircraft systems and machine learning","authors":"Sally Deborah Pereira da Silva, Fernando Coelho Eugenio, Roberta Aparecida Fantinel, Lucio de Paula Amaral, Caroline Lorenci Mallmann, Fernanda Dias dos Santos, Alexandre Rosa dos Santos, Rudiney Soares Pereira","doi":"10.1117/1.jrs.17.034514","DOIUrl":"https://doi.org/10.1117/1.jrs.17.034514","url":null,"abstract":"We aimed to combine the use of images obtained from remotely piloted aircraft systems (RPAS) and machine learning (ML) to identify the invasive alien species Psidium guajava in a protected area in southern Brazil. Field data were obtained in a sampling area, where the species’ geographic coordinates were collected with a global positioning system device. Remote data were collected with the Parrot Sequoia® multispectral camera onboard the Phantom 4® Pro platform. Image processing was used to generate reflectance maps and vegetation indices, after which four classes of interest were defined for model training. The supervised classification involved two approaches (pixel-based—BP and object-based image analysis—OBIA) and two ML algorithms compared (random forest—RF and support vector machine—SVM). For performance analysis, confusion matrices with user and producer accuracies, Kappa values and overall accuracy (OA) were calculated. The results demonstrate that the multispectral composition was excellent in identifying the invasive P. guajava, in an OBIA approach with the RF algorithm (0.90 Kappa and 93% OA). Thus, considering the priority of biodiversity conservation and the importance of the Brazilian Atlantic Forest for the maintenance of endemic and endangered species, we present a robust methodology to identify the invasive species P. guajava in subtropical forest, which can be applied in management strategies for the species control and eradication.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135420584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is of great significance to explore the spatial-temporal variations and estimate the relative importance of the influencing factors of PM2.5 and O3 pollution. The study established nationwide surface O3, NO2, and SO2 estimation models using the extreme gradient boosting model and the data fusion method. The cross-validation results indicated that the forecasted models performed well (R-values from 0.86 to 0.95). The results revealed that the pollution levels of O3, PM2.5, NO2, and SO2 in the North China Plain (NCP) were the highest in China. Subsequently, a multi-task learning model was utilized to estimate the relative importance of influential factors on the PM2.5 and O3 pollution in the NCP. The sensitivity analysis results indicated that the O3 pollution from 2010–2020 in the NCP was susceptible to meteorological factors such as ultraviolet radiation and temperature, as well as anthropogenic precursors such as NOX, and PM2.5 pollution in the NCP was constrained by both meteorological factors (44.62%) and anthropogenic emissions (16.86%). The impact of NO2 on PM2.5 pollution was similar to its impact on O3 pollution; therefore, the importance of NO2 emission reduction to PM2.5 pollution is as important as that of O3 pollution, whereas the impact of SO2 on PM2.5 was much greater than its impact on O3 pollution, so SO2 emission reduction is more important for PM2.5.
{"title":"Resolving contributions of NO2 and SO2 to PM2.5 and O3 pollutions in the North China Plain via multi-task learning","authors":"Mingliang Ma, Mengnan Liu, Mengjiao Liu, Ke Li, Huaqiao Xing, Fei Meng","doi":"10.1117/1.jrs.18.012004","DOIUrl":"https://doi.org/10.1117/1.jrs.18.012004","url":null,"abstract":"It is of great significance to explore the spatial-temporal variations and estimate the relative importance of the influencing factors of PM2.5 and O3 pollution. The study established nationwide surface O3, NO2, and SO2 estimation models using the extreme gradient boosting model and the data fusion method. The cross-validation results indicated that the forecasted models performed well (R-values from 0.86 to 0.95). The results revealed that the pollution levels of O3, PM2.5, NO2, and SO2 in the North China Plain (NCP) were the highest in China. Subsequently, a multi-task learning model was utilized to estimate the relative importance of influential factors on the PM2.5 and O3 pollution in the NCP. The sensitivity analysis results indicated that the O3 pollution from 2010–2020 in the NCP was susceptible to meteorological factors such as ultraviolet radiation and temperature, as well as anthropogenic precursors such as NOX, and PM2.5 pollution in the NCP was constrained by both meteorological factors (44.62%) and anthropogenic emissions (16.86%). The impact of NO2 on PM2.5 pollution was similar to its impact on O3 pollution; therefore, the importance of NO2 emission reduction to PM2.5 pollution is as important as that of O3 pollution, whereas the impact of SO2 on PM2.5 was much greater than its impact on O3 pollution, so SO2 emission reduction is more important for PM2.5.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135385763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hao Yang, Duoyang Qiu, Zhiyuan Fang, Yalin Hu, Fei Ming
Atmospheric transmittance, turbulence, and wind play a crucial role in the field of laser atmospheric transmission. In response to the demand for comprehensive detection of atmospheric optical parameters, a LiDAR system for comprehensive measurement of atmospheric transmittance, turbulence, and wind (ACW-LiDAR) has been developed through integrated optical and mechanical design. The remote sensing measurement of atmospheric transmittance, turbulence, and wind simultaneously and along a common path has been realized by the ACW-LiDAR. By proposing a limb scanning algorithm, the problem of preference for atmospheric transmittance estimation has been overcome, and the problem of inconvenient turbulence measurement at near-surface has been effectively solved. It is also possible to obtain wind information on the laser transmission route. The experimental results based on the ACW-LiDAR system indicate that the detection distance of atmospheric transmittance is greater than 10 km @ 1064 nm. The detection distance of turbulence is greater than 10 km @ 532 nm. The detection distance of wind is greater than 4 km @ 1550 nm. The comparison between the ACW-LiDAR system and the ground meteorological automatic observation system shows that the variation trend in extinction coefficient, turbulence, and radial wind velocity is consistent, with a correlation better than 0.69, verifying the accuracy of the developed ACW-LiDAR system. The analysis of comprehensive scanning detection indicates that the three key atmospheric parameters mentioned above are interrelated and mutually influencing. So the measurement of the same time and common path of atmospheric transmittance, turbulence, and wind is of great significance. These can provide a theoretical and experimental basis for long-term observation and accumulation of atmospheric parameters, as well as the correction of atmospheric parameters.
大气透过率、湍流和风在激光大气传输领域起着至关重要的作用。针对大气光学参数综合探测的需求，通过光学与机械一体化设计，研制了一种综合测量大气透过率、湍流、风的激光雷达系统(ACW-LiDAR)。利用ACW-LiDAR，实现了对大气透过率、湍流度和风场在同一路径上的同时遥感测量。通过提出一种翼缘扫描算法，克服了大气透过率估算偏向化的问题，有效解决了近地表湍流测量不方便的问题。还可以获得激光传输路线上的风信息。基于ACW-LiDAR系统的实验结果表明，在1064 nm处，大气透过率的探测距离大于10 km。湍流探测距离大于10km @ 532nm。风的探测距离大于4 km @ 1550 nm。ACW-LiDAR系统与地面气象自动观测系统的对比表明，消光系数、湍流度和径向风速的变化趋势一致，相关系数大于0.69，验证了ACW-LiDAR系统的精度。综合扫描探测分析表明，上述三个关键大气参数是相互联系、相互影响的。因此，测量大气透过率、湍流和风的同时间共径具有重要意义。为大气参数的长期观测和积累以及大气参数的校正提供了理论和实验依据。
{"title":"LiDAR technology and experimental research for comprehensive measurement of atmospheric transmittance, turbulence, and wind","authors":"Hao Yang, Duoyang Qiu, Zhiyuan Fang, Yalin Hu, Fei Ming","doi":"10.1117/1.jrs.18.012002","DOIUrl":"https://doi.org/10.1117/1.jrs.18.012002","url":null,"abstract":"Atmospheric transmittance, turbulence, and wind play a crucial role in the field of laser atmospheric transmission. In response to the demand for comprehensive detection of atmospheric optical parameters, a LiDAR system for comprehensive measurement of atmospheric transmittance, turbulence, and wind (ACW-LiDAR) has been developed through integrated optical and mechanical design. The remote sensing measurement of atmospheric transmittance, turbulence, and wind simultaneously and along a common path has been realized by the ACW-LiDAR. By proposing a limb scanning algorithm, the problem of preference for atmospheric transmittance estimation has been overcome, and the problem of inconvenient turbulence measurement at near-surface has been effectively solved. It is also possible to obtain wind information on the laser transmission route. The experimental results based on the ACW-LiDAR system indicate that the detection distance of atmospheric transmittance is greater than 10 km @ 1064 nm. The detection distance of turbulence is greater than 10 km @ 532 nm. The detection distance of wind is greater than 4 km @ 1550 nm. The comparison between the ACW-LiDAR system and the ground meteorological automatic observation system shows that the variation trend in extinction coefficient, turbulence, and radial wind velocity is consistent, with a correlation better than 0.69, verifying the accuracy of the developed ACW-LiDAR system. The analysis of comprehensive scanning detection indicates that the three key atmospheric parameters mentioned above are interrelated and mutually influencing. So the measurement of the same time and common path of atmospheric transmittance, turbulence, and wind is of great significance. These can provide a theoretical and experimental basis for long-term observation and accumulation of atmospheric parameters, as well as the correction of atmospheric parameters.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135387292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Most health studies have used residential addresses to assess personal exposure to air pollution. These exposure assessments may suffer from bias due to not considering individual movement. Here, we collected 45,600 hourly movement trajectory data points for 185 individuals in Nanjing from COVID-19 epidemiological surveys. We developed a fusion algorithm to produce hourly 1-km PM2.5 concentrations, with a good performance for out-of-station cross validation (correlation coefficient of 0.89, root-mean-square error of 5.60 μg / m3, and mean absolute error (MAE) of 4.04 μg / m3). Based on these PM2.5 concentrations and location data, PM2.5 exposures considering individual movement were calculated and further compared with residence-based exposures. Our results showed that daily residence-based exposures had an MAE of 0.19 μg / m3 and were underestimated by <1 % overall. For hourly residence-based exposures, the MAE exhibited a diurnal variation: it decreased from 0.58 μg / m3 at 09:00 to 0.44 μg / m3 at 12:00 and then continuously increased to 0.74 μg / m3 at 17:00. The biases also depended on activity types and distances from home to activity locations. Specifically, the largest MAE (3.86 μg / m3) occurred in visits that were among the top four types of activity other than being at home. As distances changed from <10 to >30 km, the degree of underestimation for hourly residence-based exposures increased from 1% to 6%. This trend was more obvious for work activities, suggesting that personal exposure assessments should consider individual movement for work cases with long commuting distances.
{"title":"Assessment of personal exposure using movement trajectory and hourly 1-km PM2.5 concentrations","authors":"Heming Bai, Junjie Song, Huiqun Wu, Rusha Yan, Wenkang Gao, Muhammad Jawad Hussain","doi":"10.1117/1.jrs.18.012003","DOIUrl":"https://doi.org/10.1117/1.jrs.18.012003","url":null,"abstract":"Most health studies have used residential addresses to assess personal exposure to air pollution. These exposure assessments may suffer from bias due to not considering individual movement. Here, we collected 45,600 hourly movement trajectory data points for 185 individuals in Nanjing from COVID-19 epidemiological surveys. We developed a fusion algorithm to produce hourly 1-km PM2.5 concentrations, with a good performance for out-of-station cross validation (correlation coefficient of 0.89, root-mean-square error of 5.60 μg / m3, and mean absolute error (MAE) of 4.04 μg / m3). Based on these PM2.5 concentrations and location data, PM2.5 exposures considering individual movement were calculated and further compared with residence-based exposures. Our results showed that daily residence-based exposures had an MAE of 0.19 μg / m3 and were underestimated by <1 % overall. For hourly residence-based exposures, the MAE exhibited a diurnal variation: it decreased from 0.58 μg / m3 at 09:00 to 0.44 μg / m3 at 12:00 and then continuously increased to 0.74 μg / m3 at 17:00. The biases also depended on activity types and distances from home to activity locations. Specifically, the largest MAE (3.86 μg / m3) occurred in visits that were among the top four types of activity other than being at home. As distances changed from <10 to >30 km, the degree of underestimation for hourly residence-based exposures increased from 1% to 6%. This trend was more obvious for work activities, suggesting that personal exposure assessments should consider individual movement for work cases with long commuting distances.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135387295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automatic registration of medium-resolution remote sensing images of coral reefs, particularly those without artificial facilities, faces two challenges: difficulty in identifying the same coral reefs in different images and instability of the fine-tuning process. To overcome these challenges, we propose an automatic registration method that combines morphological information pairing with constrained iterative fining. This method comprises three steps. First, the contours of the coral reefs were extracted using level set method. Subsequently, the same coral reefs in the two images were identified and paired based on morphological similarities and relative locations. Finally, iterative fine registration with a constrained strategy was performed by controlling abnormal changes in the geometric center to further improve the registration accuracy for every pair of coral reefs. The proposed automatic registration method was validated using a Landsat5 image acquired on April 15, 2005 and a HJ-1B image acquired on May 4, 2010. Compared with the scale-invariant feature transform (SIFT) method and the SIFT with Random Sample Consensus method, the proposed method showed good performance in the automatic registration of coral reef images.
{"title":"Automatic registration method for medium-resolution remote sensing images of coral reefs with morphological information pairing and constrained iterative fining","authors":"Zhenying Chen, Yuzhe Pian, Zhenjie Chen, Liang Cheng","doi":"10.1117/1.jrs.17.036510","DOIUrl":"https://doi.org/10.1117/1.jrs.17.036510","url":null,"abstract":"Automatic registration of medium-resolution remote sensing images of coral reefs, particularly those without artificial facilities, faces two challenges: difficulty in identifying the same coral reefs in different images and instability of the fine-tuning process. To overcome these challenges, we propose an automatic registration method that combines morphological information pairing with constrained iterative fining. This method comprises three steps. First, the contours of the coral reefs were extracted using level set method. Subsequently, the same coral reefs in the two images were identified and paired based on morphological similarities and relative locations. Finally, iterative fine registration with a constrained strategy was performed by controlling abnormal changes in the geometric center to further improve the registration accuracy for every pair of coral reefs. The proposed automatic registration method was validated using a Landsat5 image acquired on April 15, 2005 and a HJ-1B image acquired on May 4, 2010. Compared with the scale-invariant feature transform (SIFT) method and the SIFT with Random Sample Consensus method, the proposed method showed good performance in the automatic registration of coral reef images.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135536288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ciyun Lin, Jie Yang, Bowen Gong, Hongchao Liu, Ganghao Sun
Ground point identification and segmentation are fundamental to the light detection and ranging (LiDAR) based environment perception because they affect the accuracy and computational efficiency in the following data processing steps. A common problem that results in over- and under-segmentation occurs when the objects of interest are nonhomogeneous, and the sampling density is uneven. This study addresses this issue using grid- and homogeneity-based approaches. This work began with a combined conditional and voxel filtering approach to shrink the spatial range and reduce the amount of point-cloud data. The spatial range was then divided into a concentric circular grid to reduce the complexity of data processing. A dynamic threshold model was used to classify the cloud points to improve the accuracy of ground-point identification on uneven, broken, and sloped roads. Additionally, a point cloud homogeneity model was used to optimize the ground point identification results in areas with vegetation. The experimental study was conducted based on the data provided in the semantic KITTI dataset, wherein comprehensive comparisons were made with state-of-the-art algorithms. The average precision, recall, F1, and running time of the proposed method were 92.5%, 90.89%, 0.92, and 0.146 s, respectively, outperforming most of the selected models in balanced accuracy and computational efficiency.
{"title":"Grid and homogeneity-based ground segmentation using light detection and ranging three-dimensional point cloud","authors":"Ciyun Lin, Jie Yang, Bowen Gong, Hongchao Liu, Ganghao Sun","doi":"10.1117/1.jrs.17.038506","DOIUrl":"https://doi.org/10.1117/1.jrs.17.038506","url":null,"abstract":"Ground point identification and segmentation are fundamental to the light detection and ranging (LiDAR) based environment perception because they affect the accuracy and computational efficiency in the following data processing steps. A common problem that results in over- and under-segmentation occurs when the objects of interest are nonhomogeneous, and the sampling density is uneven. This study addresses this issue using grid- and homogeneity-based approaches. This work began with a combined conditional and voxel filtering approach to shrink the spatial range and reduce the amount of point-cloud data. The spatial range was then divided into a concentric circular grid to reduce the complexity of data processing. A dynamic threshold model was used to classify the cloud points to improve the accuracy of ground-point identification on uneven, broken, and sloped roads. Additionally, a point cloud homogeneity model was used to optimize the ground point identification results in areas with vegetation. The experimental study was conducted based on the data provided in the semantic KITTI dataset, wherein comprehensive comparisons were made with state-of-the-art algorithms. The average precision, recall, F1, and running time of the proposed method were 92.5%, 90.89%, 0.92, and 0.146 s, respectively, outperforming most of the selected models in balanced accuracy and computational efficiency.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135537893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xianyue Wang, Longxia Qian, Chengzu Bai, Jinde Cao
Unsupervised feature extraction techniques of hyperspectral images (HSIs) have recently drawn significant attention for their excellent performance and efficiency in classification. In some existing methods, the denoising process that reduces the influence of inherent noise is ignored, and the nonlinear edge characteristics and multi-scale features that help to classify still need to be fully considered. To solve these issues, we employ a multi-scale nonlinear edge-based unsupervised three-phase model (UTPM) for hyperspectral feature extraction. Specifically, in the initial phase, a noise-adjusted principal components technique is adopted to lower the noise to improve the performance of the proposed model. Then, a neighbor band grouping technique is designed to reduce redundancy and computational cost with information entropy. Because the information entropy can concretely reflect the importance of different bands in the same group, the inner structure can be maximally preserved. Finally, we utilize a multi-scale feature fusion on kernel low-rank entropic analysis to extract nonlinear edge features and combine it with a convolution algorithm to fuse the elements of multiple scales to improve the classification performance. Compared with several other classical or progressive unsupervised hyperspectral feature extraction algorithms, the classification results on three public HSI datasets validate the effectiveness of UTPM.
{"title":"Multi-scale nonlinear edge-based three-phase model for unsupervised hyperspectral feature extraction","authors":"Xianyue Wang, Longxia Qian, Chengzu Bai, Jinde Cao","doi":"10.1117/1.jrs.17.036509","DOIUrl":"https://doi.org/10.1117/1.jrs.17.036509","url":null,"abstract":"Unsupervised feature extraction techniques of hyperspectral images (HSIs) have recently drawn significant attention for their excellent performance and efficiency in classification. In some existing methods, the denoising process that reduces the influence of inherent noise is ignored, and the nonlinear edge characteristics and multi-scale features that help to classify still need to be fully considered. To solve these issues, we employ a multi-scale nonlinear edge-based unsupervised three-phase model (UTPM) for hyperspectral feature extraction. Specifically, in the initial phase, a noise-adjusted principal components technique is adopted to lower the noise to improve the performance of the proposed model. Then, a neighbor band grouping technique is designed to reduce redundancy and computational cost with information entropy. Because the information entropy can concretely reflect the importance of different bands in the same group, the inner structure can be maximally preserved. Finally, we utilize a multi-scale feature fusion on kernel low-rank entropic analysis to extract nonlinear edge features and combine it with a convolution algorithm to fuse the elements of multiple scales to improve the classification performance. Compared with several other classical or progressive unsupervised hyperspectral feature extraction algorithms, the classification results on three public HSI datasets validate the effectiveness of UTPM.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134885578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
One of the Climate Absolute Radiance and Refractivity Observatory Pathfinder (CPF) mission’s science objectives is to intercalibrate the reflective solar bands of the NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) instrument against high-accuracy CPF measurements utilizing coincident, co-angled, and co-located footprints acquired over diverse Earth targets. To alleviate the effect of high polarization sensitivity of select VIIRS channels on intercalibration analysis, the CPF team will limit the intercalibration footprints over low-polarized scene types, which will be identified based on an empirical estimation of their degree of polarization (DOP) and angle of polarization (AOP) using Polarization and Directionality of the Earth’s Reflectance (POLDER) data. We describe the methodology for evaluating systematic errors in the estimation of DOP and AOP for Earth-reflected radiances using POLDER’s polarized bands and investigates their potential impact on CPF-VIIRS intercalibration uncertainty. The systematic errors were found to be <0.01 for DOP and <2.2 deg for AOP, which will have a negligible impact on CPF-VIIRS intercalibration uncertainty.
{"title":"Evaluation of systematic errors on polarization parameters from POLDER instrument data for use in CLARREO Pathfinder-VIIRS intercalibration","authors":"Daniel Goldin, Rajendra Bhatt, Yolanda Shea","doi":"10.1117/1.jrs.17.034513","DOIUrl":"https://doi.org/10.1117/1.jrs.17.034513","url":null,"abstract":"One of the Climate Absolute Radiance and Refractivity Observatory Pathfinder (CPF) mission’s science objectives is to intercalibrate the reflective solar bands of the NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) instrument against high-accuracy CPF measurements utilizing coincident, co-angled, and co-located footprints acquired over diverse Earth targets. To alleviate the effect of high polarization sensitivity of select VIIRS channels on intercalibration analysis, the CPF team will limit the intercalibration footprints over low-polarized scene types, which will be identified based on an empirical estimation of their degree of polarization (DOP) and angle of polarization (AOP) using Polarization and Directionality of the Earth’s Reflectance (POLDER) data. We describe the methodology for evaluating systematic errors in the estimation of DOP and AOP for Earth-reflected radiances using POLDER’s polarized bands and investigates their potential impact on CPF-VIIRS intercalibration uncertainty. The systematic errors were found to be <0.01 for DOP and <2.2 deg for AOP, which will have a negligible impact on CPF-VIIRS intercalibration uncertainty.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134886162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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