Journal of Applied Remote Sensing最新文献_第6页

Out-of-field stray light correction in optical instruments: the case of Metop-3MI 光学仪器的场外杂散光校正：Metop-3MI 案例

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-03-04 DOI: 10.1117/1.jrs.18.016508

Lionel Clermont, Céline Michel

引用次数: 0

Single-shot super-resolution and non-uniformity correction through wavefront modulation in infrared imaging systems 在红外成像系统中通过波前调制实现单次超分辨率和非均匀性校正

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-03-01 DOI: 10.1117/1.jrs.18.022205

Guillermo Machuca, Pablo Meza, Esteban Vera

引用次数: 0

GODANet: an object detection model for remote sensing images fusing contextual information and dynamic convolution GODANet：融合上下文信息和动态卷积的遥感图像目标检测模型

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-02-27 DOI: 10.1117/1.jrs.18.016507

Xing Rong, Zhihua Zhang, Hao Yuan, Shaobin Zhang

引用次数: 0

Detection of intrinsic variants of an endmember in hyperspectral images based on local spatial and spectral features 基于局部空间和光谱特征检测高光谱图像中内构件的内在变体

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-02-01 DOI: 10.1117/1.jrs.18.016506

Gouri Shankar Chetia, Bishnulatpam Pushpa Devi

{"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}

引用次数: 0

Generation of synthetic generative adversarial network-based multispectral satellite images with improved sharpness 基于合成生成对抗网络生成清晰度更高的多光谱卫星图像

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-02-01 DOI: 10.1117/1.jrs.18.014510

Lydia Abady, Mauro Barni, Andrea Garzelli, Benedetta Tondi

{"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}

引用次数: 0

Automated classification of citrus disease on fruits and leaves using convolutional neural network generated features from hyperspectral images and machine learning classifiers 利用从高光谱图像生成特征的卷积神经网络和机器学习分类器对柑橘果实和叶片上的病害进行自动分类

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-02-01 DOI: 10.1117/1.jrs.18.014512

Pappu Kumar Yadav, Thomas Burks, Jianwei Qin, Moon Kim, Quentin Frederick, Megan M. Dewdney, Mark A. Ritenour

{"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}

引用次数: 0

Retrieval of land surface temperature from INS-2TD thermal infrared observations using a generalized single-channel algorithm over South-Asia region 使用通用单通道算法从 INS-2TD 热红外观测数据中获取南亚地区的陆地表面温度

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-02-01 DOI: 10.1117/1.jrs.18.022202

Jalpesh A. Dave, Mehul R. Pandya, Dhiraj B. Shah, Hasmukh K. Varchand, Parthkumar N. Parmar, Himanshu J. Trivedi, Vishal N. Pathak

{"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}

引用次数: 0

Radar high-speed maneuvering weak target detection based on radon dynamic path optimization and fixed point iteration 基于氡动态路径优化和定点迭代的雷达高速机动弱目标探测

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-02-01 DOI: 10.1117/1.jrs.18.014518

Fatao Hou

{"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}

引用次数: 0

Mangrove ecosystem species mapping from integrated Sentinel-2 imagery and field spectral data using random forest algorithm 利用随机森林算法从综合哨兵-2 图像和实地光谱数据中绘制红树林生态系统物种分布图

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-02-01 DOI: 10.1117/1.jrs.18.014509

Nirmawana Simarmata, Ketut Wikantika, Soni Darmawan, Agung Budi Harto, Anjar Dimara Sakti, Aki Asmoro Santo

{"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}

引用次数: 0

Multi-depth temperature prediction using machine learning for pavement sections 利用机器学习对路面断面进行多深度温度预测

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-02-01 DOI: 10.1117/1.jrs.18.014517

Yunyan Huang, Mohamad Molavi Nojumi, Shadi Ansari, Leila Hashemian, Alireza Bayat

{"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}

引用次数: 0