{"title":"Out-of-field stray light correction in optical instruments: the case of Metop-3MI","authors":"Lionel Clermont, Céline Michel","doi":"10.1117/1.jrs.18.016508","DOIUrl":"https://doi.org/10.1117/1.jrs.18.016508","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140079726","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}
Infrared (IR) imaging systems have sensor and optical limitations that result in degraded imagery. Apart from imperfect optics and the finite detector size being responsible for introducing blurring and aliasing, the detector fixed-pattern noise also adds a significant layer of degradation in the collected imagery. Here, we propose a single-shot super-resolution method that compensates for the nonuniformity noise of long-wave IR imaging systems. The strategy combines wavefront modulation and a reconstruction methodology based on total variation and nonlocal means regularizers to recover high-spatial frequencies while reducing noise. In simulations and experiments, we demonstrate a clear improvement of up to 16× in image resolution while significantly decreasing the fixed-pattern noise in the reconstructed images.
{"title":"Single-shot super-resolution and non-uniformity correction through wavefront modulation in infrared imaging systems","authors":"Guillermo Machuca, Pablo Meza, Esteban Vera","doi":"10.1117/1.jrs.18.022205","DOIUrl":"https://doi.org/10.1117/1.jrs.18.022205","url":null,"abstract":"Infrared (IR) imaging systems have sensor and optical limitations that result in degraded imagery. Apart from imperfect optics and the finite detector size being responsible for introducing blurring and aliasing, the detector fixed-pattern noise also adds a significant layer of degradation in the collected imagery. Here, we propose a single-shot super-resolution method that compensates for the nonuniformity noise of long-wave IR imaging systems. The strategy combines wavefront modulation and a reconstruction methodology based on total variation and nonlocal means regularizers to recover high-spatial frequencies while reducing noise. In simulations and experiments, we demonstrate a clear improvement of up to 16× in image resolution while significantly decreasing the fixed-pattern noise in the reconstructed images.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140076525","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}
{"title":"GODANet: an object detection model for remote sensing images fusing contextual information and dynamic convolution","authors":"Xing Rong, Zhihua Zhang, Hao Yuan, Shaobin Zhang","doi":"10.1117/1.jrs.18.016507","DOIUrl":"https://doi.org/10.1117/1.jrs.18.016507","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140427126","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}
In recent years, addressing spectral variability in hyperspectral data has improved blind hyperspectral unmixing performance and gained attention in endmember detection applications. Current approaches to address the problem of spectral variability associate the variabilities with the valid endmember and attempt to mitigate the ill-effects caused by them. However, intrinsic variabilities induced by material-specific compositional changes are crucial for identifying within-class materials like diverse soil types, forest species, and urban areas. Despite this significance, no studies have attempted a direct implementation to explicitly identify the intrinsic variants of an endmember. In this paper, we propose a framework to solve two important problems: first, to separate the intrinsic variants from illumination-based variants, and second, to simultaneously estimate the number of intrinsic variants and extract their spectral signatures without any knowledge of the number of such sources. The proposed method utilizes a spectral analysis technique with local minima/maxima to remove illumination-based variabilities, followed by a simplex-volume maximization-based reordering of potential endmembers and an iterative reconstruction error-based technique to simultaneously count the number of intrinsic variants and capture their signatures. The approach is validated on synthetic and real datasets, showcasing comparable results with state-of-the-art methods.
{"title":"Detection of intrinsic variants of an endmember in hyperspectral images based on local spatial and spectral features","authors":"Gouri Shankar Chetia, Bishnulatpam Pushpa Devi","doi":"10.1117/1.jrs.18.016506","DOIUrl":"https://doi.org/10.1117/1.jrs.18.016506","url":null,"abstract":"In recent years, addressing spectral variability in hyperspectral data has improved blind hyperspectral unmixing performance and gained attention in endmember detection applications. Current approaches to address the problem of spectral variability associate the variabilities with the valid endmember and attempt to mitigate the ill-effects caused by them. However, intrinsic variabilities induced by material-specific compositional changes are crucial for identifying within-class materials like diverse soil types, forest species, and urban areas. Despite this significance, no studies have attempted a direct implementation to explicitly identify the intrinsic variants of an endmember. In this paper, we propose a framework to solve two important problems: first, to separate the intrinsic variants from illumination-based variants, and second, to simultaneously estimate the number of intrinsic variants and extract their spectral signatures without any knowledge of the number of such sources. The proposed method utilizes a spectral analysis technique with local minima/maxima to remove illumination-based variabilities, followed by a simplex-volume maximization-based reordering of potential endmembers and an iterative reconstruction error-based technique to simultaneously count the number of intrinsic variants and capture their signatures. The approach is validated on synthetic and real datasets, showcasing comparable results with state-of-the-art methods.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139759592","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}
Lydia Abady, Mauro Barni, Andrea Garzelli, Benedetta Tondi
The generation of synthetic multispectral satellite images has not yet reached the quality level achievable in other domains, such as the generation and manipulation of face images. Part of the difficulty stems from the need to generate consistent data across the entire electromagnetic spectrum covered by such images at radiometric resolutions higher than those typically used in multimedia applications. The different spatial resolution of image bands corresponding to different wavelengths poses additional problems, whose main effect is a lack of spatial details in the synthetic images with respect to the original ones. We propose two generative adversarial networks-based architectures explicitly thought to generate synthetic satellite imagery by applying style transfer to 13-band Sentinel-2 level1-C images. To avoid losing the finer spatial details and improve the sharpness of the generated images, we introduce a pansharpening-like approach, whereby the spatial structures of the input image are transferred to the style-transferred images without introducing visible artifacts. The results we got by applying the proposed architectures to transform barren images into vegetation images and vice versa and to transform summer (res. winter) images into winter (res. summer) images, which confirm the validity of the proposed solution.
{"title":"Generation of synthetic generative adversarial network-based multispectral satellite images with improved sharpness","authors":"Lydia Abady, Mauro Barni, Andrea Garzelli, Benedetta Tondi","doi":"10.1117/1.jrs.18.014510","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014510","url":null,"abstract":"The generation of synthetic multispectral satellite images has not yet reached the quality level achievable in other domains, such as the generation and manipulation of face images. Part of the difficulty stems from the need to generate consistent data across the entire electromagnetic spectrum covered by such images at radiometric resolutions higher than those typically used in multimedia applications. The different spatial resolution of image bands corresponding to different wavelengths poses additional problems, whose main effect is a lack of spatial details in the synthetic images with respect to the original ones. We propose two generative adversarial networks-based architectures explicitly thought to generate synthetic satellite imagery by applying style transfer to 13-band Sentinel-2 level1-C images. To avoid losing the finer spatial details and improve the sharpness of the generated images, we introduce a pansharpening-like approach, whereby the spatial structures of the input image are transferred to the style-transferred images without introducing visible artifacts. The results we got by applying the proposed architectures to transform barren images into vegetation images and vice versa and to transform summer (res. winter) images into winter (res. summer) images, which confirm the validity of the proposed solution.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139759383","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}
Pappu Kumar Yadav, Thomas Burks, Jianwei Qin, Moon Kim, Quentin Frederick, Megan M. Dewdney, Mark A. Ritenour
Citrus black spot (CBS) is a fungal disease caused by Phyllosticta citricarpa that poses a quarantine threat and can restrict market access to fruits. It manifests as lesions on the fruit surface and can result in premature fruit drops, leading to reduced yield. Another significant disease affecting citrus is canker, which is caused by the bacterium Xanthomonas citri subsp. citri (syn. X. axonopodis pv. citri); it causes economic losses for growers due to fruit drops and blemishes. Early detection and management of groves infected with CBS or canker through fruit and leaf inspection can greatly benefit the Florida citrus industry. However, manual inspection and classification of disease symptoms on fruits or leaves are labor-intensive and time-consuming processes. Therefore, there is a need to develop a computer vision system capable of autonomously classifying fruits and leaves, expediting disease management in the groves. This paper aims to demonstrate the effectiveness of convolutional neural network (CNN) generated features and machine learning (ML) classifiers for detecting CBS infected fruits and leaves with canker symptoms. A custom shallow CNN with radial basis function support vector machine (RBF SVM) achieved an overall accuracy of 92.1% for classifying fruits with CBS and four other conditions (greasy spot, melanose, wind scar, and marketable), and a custom Visual Geometry Group 16 (VGG16) with the RBF SVM classified leaves with canker and four other conditions (control, greasy spot, melanoses, and scab) at an overall accuracy of 93%. These preliminary findings demonstrate the potential of utilizing hyperspectral imaging (HSI) systems for automated classification of citrus fruit and leaf diseases using shallow and deep CNN-generated features, along with ML classifiers.
柑橘黑斑病（CBS）是一种由 Phyllosticta citricarpa 引起的真菌病害，对检疫构成威胁，并可能限制水果的市场准入。它表现为果实表面的病变，可导致过早落果，从而导致减产。影响柑橘的另一种重要病害是腐烂病，它是由柑橘黄单胞菌（Xanthomonas citri subsp.通过果实和叶片检查，及早发现和管理感染 CBS 或腐烂病的果园，对佛罗里达柑橘产业大有裨益。然而，对果实或叶片上的病害症状进行人工检查和分类是一项耗费大量人力和时间的工作。因此，有必要开发一种能够自主对果实和叶片进行分类的计算机视觉系统，以加快果园的病害管理。本文旨在展示卷积神经网络（CNN）生成的特征和机器学习（ML）分类器在检测受 CBS 感染并出现腐烂症状的果实和叶片方面的有效性。采用径向基函数支持向量机（RBF SVM）的定制浅层 CNN 对感染 CBS 的果实和其他四种情况（油斑、黑斑、风疤和适销）进行分类的总体准确率为 92.1%，而采用 RBF SVM 的定制视觉几何组 16（VGG16）对感染腐烂病的叶片和其他四种情况（对照、油斑、黑斑和疮痂）进行分类的总体准确率为 93%。这些初步研究结果表明，利用浅层和深层 CNN 生成的特征以及 ML 分类器，利用高光谱成像（HSI）系统对柑橘果实和叶片病害进行自动分类是很有潜力的。
{"title":"Automated classification of citrus disease on fruits and leaves using convolutional neural network generated features from hyperspectral images and machine learning classifiers","authors":"Pappu Kumar Yadav, Thomas Burks, Jianwei Qin, Moon Kim, Quentin Frederick, Megan M. Dewdney, Mark A. Ritenour","doi":"10.1117/1.jrs.18.014512","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014512","url":null,"abstract":"Citrus black spot (CBS) is a fungal disease caused by Phyllosticta citricarpa that poses a quarantine threat and can restrict market access to fruits. It manifests as lesions on the fruit surface and can result in premature fruit drops, leading to reduced yield. Another significant disease affecting citrus is canker, which is caused by the bacterium Xanthomonas citri subsp. citri (syn. X. axonopodis pv. citri); it causes economic losses for growers due to fruit drops and blemishes. Early detection and management of groves infected with CBS or canker through fruit and leaf inspection can greatly benefit the Florida citrus industry. However, manual inspection and classification of disease symptoms on fruits or leaves are labor-intensive and time-consuming processes. Therefore, there is a need to develop a computer vision system capable of autonomously classifying fruits and leaves, expediting disease management in the groves. This paper aims to demonstrate the effectiveness of convolutional neural network (CNN) generated features and machine learning (ML) classifiers for detecting CBS infected fruits and leaves with canker symptoms. A custom shallow CNN with radial basis function support vector machine (RBF SVM) achieved an overall accuracy of 92.1% for classifying fruits with CBS and four other conditions (greasy spot, melanose, wind scar, and marketable), and a custom Visual Geometry Group 16 (VGG16) with the RBF SVM classified leaves with canker and four other conditions (control, greasy spot, melanoses, and scab) at an overall accuracy of 93%. These preliminary findings demonstrate the potential of utilizing hyperspectral imaging (HSI) systems for automated classification of citrus fruit and leaf diseases using shallow and deep CNN-generated features, along with ML classifiers.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139772546","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}
Jalpesh A. Dave, Mehul R. Pandya, Dhiraj B. Shah, Hasmukh K. Varchand, Parthkumar N. Parmar, Himanshu J. Trivedi, Vishal N. Pathak
The experimental Indian Nano-Satellite (INS)-2TD acquires data in a long-wave infrared (7 to 16 μm) region with a fairly good spatial resolution of 175 m. Our study focuses on the retrieval of land surface temperature (LST) using a physics-based generalized single-channel (GSC) algorithm for the INS-2TD observations. A total of 597,240 at-sensor radiance simulations were carried out using moderate resolution atmospheric transmittance 5.3 radiative transfer model for varying conditions pertaining to surface, atmosphere, and sensor geometry to develop and validate the GSC algorithm for broadband INS-2TD sensor. The result from simulated test dataset shows the algorithm’s consistent performance with root-mean-square error (RMSE) of 2.87 K and 0.97 R2. Pixel-to-pixel intercomparison of retrieved LST and standard LST product of Indian National Satellite (INSAT)-3D indicates a good agreement with 0.99 R2 and range of RMSE from 1.17 to 4.78 K over the six selected datasets of South-Asia. The results reveal that the retrieved INS-2TD LST products perform very well, except having a hot bias of around 4.78 K compared to INSAT-3D LST over the Himalayan mountains due to the topographic effect. These results show the overall reasonable accuracy of the retrieved LST over heterogeneous surfaces and highly dynamic atmospheric conditions.
{"title":"Retrieval of land surface temperature from INS-2TD thermal infrared observations using a generalized single-channel algorithm over South-Asia region","authors":"Jalpesh A. Dave, Mehul R. Pandya, Dhiraj B. Shah, Hasmukh K. Varchand, Parthkumar N. Parmar, Himanshu J. Trivedi, Vishal N. Pathak","doi":"10.1117/1.jrs.18.022202","DOIUrl":"https://doi.org/10.1117/1.jrs.18.022202","url":null,"abstract":"The experimental Indian Nano-Satellite (INS)-2TD acquires data in a long-wave infrared (7 to 16 μm) region with a fairly good spatial resolution of 175 m. Our study focuses on the retrieval of land surface temperature (LST) using a physics-based generalized single-channel (GSC) algorithm for the INS-2TD observations. A total of 597,240 at-sensor radiance simulations were carried out using moderate resolution atmospheric transmittance 5.3 radiative transfer model for varying conditions pertaining to surface, atmosphere, and sensor geometry to develop and validate the GSC algorithm for broadband INS-2TD sensor. The result from simulated test dataset shows the algorithm’s consistent performance with root-mean-square error (RMSE) of 2.87 K and 0.97 R2. Pixel-to-pixel intercomparison of retrieved LST and standard LST product of Indian National Satellite (INSAT)-3D indicates a good agreement with 0.99 R2 and range of RMSE from 1.17 to 4.78 K over the six selected datasets of South-Asia. The results reveal that the retrieved INS-2TD LST products perform very well, except having a hot bias of around 4.78 K compared to INSAT-3D LST over the Himalayan mountains due to the topographic effect. These results show the overall reasonable accuracy of the retrieved LST over heterogeneous surfaces and highly dynamic atmospheric conditions.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139956177","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}
Long-time coherent integration is known as a powerful method to detect the weak target. However, its effectiveness is limited by the target motion across range and Doppler bins. For the high-speed target, it is highly possible that the range bin crossing (RBC) problem occurs, and for maneuvering target, the Doppler bin crossing (DBC) problem cannot be neglected. In this paper, we propose a Radon dynamic path optimization and fixed point iteration method to deal with the RBC and DBC problem, and thus make the radar able to detect the high-speed maneuvering weak target effectively. Radon transform is essentially a parameter searching method to find the target range moving path. We derive a cost function based on the property of the slow time time-frequency and frequency-time matrix, and solve it with the dynamic path optimization and fixed point iteration algorithm. The proposed method does not demand any a priori information,and is free of the ambiguity of the velocity or the acceleration caused by the potential undersampling of the slow time. Both the simulated and real Radar echo signals validate the effectiveness of the proposed method.
{"title":"Radar high-speed maneuvering weak target detection based on radon dynamic path optimization and fixed point iteration","authors":"Fatao Hou","doi":"10.1117/1.jrs.18.014518","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014518","url":null,"abstract":"Long-time coherent integration is known as a powerful method to detect the weak target. However, its effectiveness is limited by the target motion across range and Doppler bins. For the high-speed target, it is highly possible that the range bin crossing (RBC) problem occurs, and for maneuvering target, the Doppler bin crossing (DBC) problem cannot be neglected. In this paper, we propose a Radon dynamic path optimization and fixed point iteration method to deal with the RBC and DBC problem, and thus make the radar able to detect the high-speed maneuvering weak target effectively. Radon transform is essentially a parameter searching method to find the target range moving path. We derive a cost function based on the property of the slow time time-frequency and frequency-time matrix, and solve it with the dynamic path optimization and fixed point iteration algorithm. The proposed method does not demand any a priori information,and is free of the ambiguity of the velocity or the acceleration caused by the potential undersampling of the slow time. Both the simulated and real Radar echo signals validate the effectiveness of the proposed method.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139923711","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}
Nirmawana Simarmata, Ketut Wikantika, Soni Darmawan, Agung Budi Harto, Anjar Dimara Sakti, Aki Asmoro Santo
Mangroves maintain coastal balance and have the greatest potential for carbon sequestration. Most mapping studies on mangroves have focused on their extent and distribution and rarely featured mangrove species. Therefore, the objective of our study is to investigate mangrove species mapping from integrated Sentinel-2 imagery and field spectral data using a random forest (RF) algorithm. Study areas are located in East and South Lampung, Indonesia. The field samples used represented 144 points of mangrove species. The classification method used an RF algorithm and four models with varying parameters: model 1 with Sentinel-2; model 2 with both Sentinel-2 and field spectral data; model 3 with Sentinel-2, field spectral data, and spectrally transformed data; and model 4 only with spectrally transformed data. The results showed that Rhizophora mucronata, Sonneratia alba, Avicennia lanata, and Avicennia marina were the most common mangrove species in these areas, with reflectance values in the range of 0.002 to 0.493, 0.006 to 0.833, 0.014 to 0.768, and 0.002 to 0.758. Permutation importance (PI) that affects the classification model is the red band, near-infrared, and green normalized difference vegetation index, where the most PI in model 3 is 0.283. The highest level of agreement for mangrove species is found in model 3. Model 3 is the best parameter for RF classification that showed the best mapping accuracy, with the overall accuracy, user accuracy, producer accuracy, and kappa value being 81.25%, 81.68%, 81.25%, and 0.80, respectively.
{"title":"Mangrove ecosystem species mapping from integrated Sentinel-2 imagery and field spectral data using random forest algorithm","authors":"Nirmawana Simarmata, Ketut Wikantika, Soni Darmawan, Agung Budi Harto, Anjar Dimara Sakti, Aki Asmoro Santo","doi":"10.1117/1.jrs.18.014509","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014509","url":null,"abstract":"Mangroves maintain coastal balance and have the greatest potential for carbon sequestration. Most mapping studies on mangroves have focused on their extent and distribution and rarely featured mangrove species. Therefore, the objective of our study is to investigate mangrove species mapping from integrated Sentinel-2 imagery and field spectral data using a random forest (RF) algorithm. Study areas are located in East and South Lampung, Indonesia. The field samples used represented 144 points of mangrove species. The classification method used an RF algorithm and four models with varying parameters: model 1 with Sentinel-2; model 2 with both Sentinel-2 and field spectral data; model 3 with Sentinel-2, field spectral data, and spectrally transformed data; and model 4 only with spectrally transformed data. The results showed that Rhizophora mucronata, Sonneratia alba, Avicennia lanata, and Avicennia marina were the most common mangrove species in these areas, with reflectance values in the range of 0.002 to 0.493, 0.006 to 0.833, 0.014 to 0.768, and 0.002 to 0.758. Permutation importance (PI) that affects the classification model is the red band, near-infrared, and green normalized difference vegetation index, where the most PI in model 3 is 0.283. The highest level of agreement for mangrove species is found in model 3. Model 3 is the best parameter for RF classification that showed the best mapping accuracy, with the overall accuracy, user accuracy, producer accuracy, and kappa value being 81.25%, 81.68%, 81.25%, and 0.80, respectively.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139759462","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}
The temperature of hot mix asphalt (HMA), base, and subgrade layers plays a significant role in pavement performance, because temperature influences the strength of the materials. Therefore, a model to predict temperature throughout the entire pavement structure is desirable. However, most existing models only focus on predicting the temperature of the road surface or the HMA layer, and these models usually need some information related to boundary conditions or material properties that is difficult to obtain. This research aims to demonstrate that machine learning (ML) model can be a powerful generalized approach to predict the temperature within a pavement structure at multiple depths. Data collected from sensors (thermistors and time domain reflectometers) installed in the Integrated Road Research Facility test road in Edmonton, Alberta, Canada, were used to train ML models. Sensitivity analysis was performed to analyze the influence of several input parameters on asphalt and soil temperature. ML models with three input parameters—average daily air temperature, day of the year, and depth—resulted in better performance compared to ML models based on other combinations of parameters. Three ML models were established to predict the average daily temperature, minimum daily temperature, and maximum daily temperature of the pavement structure. To validate model performance, the three ML models were compared with four existing models, and of these the ML models showed the highest accuracy with the coefficient of determination values above than 0.97 and root mean square error values below than 2.21. These results demonstrate that ML models can be used to give accurate predictions of road temperature at multiple depths with only one environmental predictive parameter, average daily air temperature.
热拌沥青（HMA）、基层和底基层的温度对路面性能起着重要作用，因为温度会影响材料的强度。因此，我们需要一个能预测整个路面结构温度的模型。然而，现有的大多数模型只侧重于预测路面或 HMA 层的温度，而且这些模型通常需要一些与边界条件或材料特性相关的信息，而这些信息很难获取。本研究旨在证明，机器学习（ML）模型是一种强大的通用方法，可用于预测多深度路面结构内的温度。从安装在加拿大艾伯塔省埃德蒙顿市综合道路研究设施测试道路上的传感器（热敏电阻和时域反射仪）收集的数据被用于训练 ML 模型。进行了敏感性分析，以分析几个输入参数对沥青和土壤温度的影响。与基于其他参数组合的 ML 模型相比，使用三个输入参数（日平均气温、年份和深度）的 ML 模型性能更好。建立了三个 ML 模型来预测路面结构的日平均温度、日最低温度和日最高温度。为了验证模型的性能，将三个 ML 模型与现有的四个模型进行了比较，其中 ML 模型显示出最高的准确性，其决定系数值大于 0.97，均方根误差值小于 2.21。这些结果表明，只需一个环境预测参数（日平均气温），ML 模型就能准确预测多个深度的路面温度。
{"title":"Multi-depth temperature prediction using machine learning for pavement sections","authors":"Yunyan Huang, Mohamad Molavi Nojumi, Shadi Ansari, Leila Hashemian, Alireza Bayat","doi":"10.1117/1.jrs.18.014517","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014517","url":null,"abstract":"The temperature of hot mix asphalt (HMA), base, and subgrade layers plays a significant role in pavement performance, because temperature influences the strength of the materials. Therefore, a model to predict temperature throughout the entire pavement structure is desirable. However, most existing models only focus on predicting the temperature of the road surface or the HMA layer, and these models usually need some information related to boundary conditions or material properties that is difficult to obtain. This research aims to demonstrate that machine learning (ML) model can be a powerful generalized approach to predict the temperature within a pavement structure at multiple depths. Data collected from sensors (thermistors and time domain reflectometers) installed in the Integrated Road Research Facility test road in Edmonton, Alberta, Canada, were used to train ML models. Sensitivity analysis was performed to analyze the influence of several input parameters on asphalt and soil temperature. ML models with three input parameters—average daily air temperature, day of the year, and depth—resulted in better performance compared to ML models based on other combinations of parameters. Three ML models were established to predict the average daily temperature, minimum daily temperature, and maximum daily temperature of the pavement structure. To validate model performance, the three ML models were compared with four existing models, and of these the ML models showed the highest accuracy with the coefficient of determination values above than 0.97 and root mean square error values below than 2.21. These results demonstrate that ML models can be used to give accurate predictions of road temperature at multiple depths with only one environmental predictive parameter, average daily air temperature.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139923714","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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