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Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Remote sensing scene classification is a hot research topic in the field of computer vision, and it is of great significance to semantic understanding of remote sensing images. At present, remote sensing scene classification methods based on deep learning occupy a dominant position in this field. However, it suffers from the lack of samples and poor model generalization ability in actual application scenarios. Therefore, this paper proposes a few-shot remote scene classification method based on attention mechanism, and designs a structure of dual- branches similarity measurement. This method is based on the meta-learning training strategy to divide the dataset into tasks. At the meantime, the input images are divided into blocks in order to preserve the feature distribution in the remote sensing image. Then the lightweight attention module is introduced into the feature extraction network to reduce the risk of overfitting and ensure the acquisition of discriminative features. Finally, based on earth mover's distance (EMD), a dual-branches similarity measurement module is added to improve the discriminative ability of the classifier. The results show that compared with the classic smallsample learning method, the few-shot remote scene classification method proposed in this paper can significantly improve the classification performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Yellow River is the property of Editorial Board of Yellow River and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the problems of IspecHyper multi-rotor UAV hyperspectral imaging system, such as lack of data processing supporting software, large error in collecting multi-band hyperspectral data, lack of coordinates, and inability to automatically stitch. Taking the citrus planting base of Jiaolong Village, Taiping Town, Wuming District as the research area, this paper studies the hyperspectral image processing method of IspecHyper multi-rotor UAV. Firstly, the IspecHyper-VM200 imaging system was used to obtain high-definition photos and multi-band hyperspectral image data of the study area. Secondly, using high-definition photos as data sources, high-resolution UAV orthophotos were formed by PXI4D Mapper software preprocessing and ENVI software image geometric correction. Finally, the ENVI software was used to cut the distorted boundary data of multi-band hyperspectral images, and the geometric correction was completed based on the UAV orthophoto image, and then the hyperspectral reflectance image product was formed through image mosaic and spectral conversion calculation. The experimental results show that the technical method formed in this paper can effectively solve the problems of hyperspectral image processing of IspecHyper multi-rotor UAV, and provide technical reference for hyperspectral image processing of UAV. [ABSTRACT FROM AUTHOR]

A way to quickly and accurately extract building information from visible light images acquired by drones is a hot issue in remote sensing image processing research. In response to the above problems, this paper combined feature extraction, random forest regression, and improved morphological alternating filtering algorithms on the basis of in-depth research on the morphological building index and enhanced morphological building index. A targeted improvement was made to the building extraction method. The main improvements in this paper are as follows. (1) A method for building enhancement of UAV visible light images based on random forest regression is proposed. By fusing the original visible light band, visible light remote sensing index features, texture features, etc., the feature space is constructed, and no artificial feature selection is required while enhancing the building information in the case of the situation. (2) Aiming at the problem of a lot of noise in the homogeneous area inside the enhanced image of the building, the image is morphologically filtered through EASFs. Finally, in order to verify the effectiveness of the method in the paper, the representative city center building area and the city surrounding building area are selected for extraction experiments. The main building types of the two areas are quite different. In the above two areas, the F1 value of the proposed method is increased by 10.40% and 10.08% compared with the MBI algorithm, and the F1 of the first proposed an improved enhanced morphological building index value is increased by 5.23% and 7.87% compared with the EMBI algorithm. The results show that this method can better extract the building information from the UAV's visible light image. [ABSTRACT FROM AUTHOR]

Land cover information extracted from high-resolution remote sensing images is of great value in the fields of urban planning and construction and land use, etc. However, high-quality semantic segmentation of land cover categories is still restricted at present due to the complicated land cover types and small spectral variability. Therefore, a novel fully-connected network MFC-Net is proposed in this paper to address this challenge, and the model adopts a novel dot-product attentionatrous spatial pyramid pooling (DPA-ASPP), which improves the capability and efficiency in terms of aggregating contextual information. Furthermore, the attention enhanced fusion module (AEFM) is proposed for different scales of features to enhance the feature representation and improve the segmentation of features of different shapes and sizes. The model makes full use of the rich contextual and multi-scale information in high-resolution remote sensing images, and achieves segmentation results (84.26% MPA and 69.67% MIOU) better than the current mainstream models on LoveDA large remote sensing image dataset. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agricultural Resources & Environment / Nongye Ziyuan yu Huanjing Xuebao is the property of Journal of Agricultural Resources & Environment Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objective] The Grand Canal is a complex hydraulic project in the eastern China. This paper studies the flooding and its detrimental impacts in the lowland areas of the canal. [Method] The research was conducted in the Pichang-Tanxin Plain, using multi-source remote sensing data and ground surveys to analyze flooding events. We calculated the normalized difference water body index (NDWI) and the normalized difference vegetation index (NDVI) before and after flooding to assess flood severity, impact on crop growth, as well as the efficacy of hydraulic facilities in mitigating flooding effects. [Result] The NDVI difference between 2004 and 2005 for crops in flooded and non-flooded areas was 0.5 and 0.25, respectively, indicating floodings detrimentally affected crop growth and development. NDWI analysis from 2003 to 2017 demonstrated that the Taierzhuang Control Gate project mitigated flooding impacts on nearby farmlands. Flooding also affected crops in non-inundated regions, with lowland crops more adversely impacted than those outside lowlands. The hydraulic facilities significantly reduced economic losses caused by flooding. [Conclusion] Remote sensing is an effective tool for monitoring inundated areas and assessing the consequences of flooding on crops. The construction of hydraulic facilities plays a crucial role in reducing the detrimental effects of flooding in lowland regions of the Grand Canal. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Wildland Fire Science is the property of Journal of Wildland Fire Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As urbanization continues, construction-related dust has emerged as a significant contributor to urban air pollution. A comprehensive evaluation of the environmental impact of construction dust necessitates an understanding of the pollution discharge from an urban perspective. Consequently, this paper proposes a construction dust emission inventory construction method from the urban perspective, which combines remote sensing image interpretation and the emission factor method. On this basis, a particulate matter emission inventory of construction dust sources in Guangzhou in 2020 is established, and the emission characteristics of the study area are analyzed using a 2kmx2km grid distribution. The findings of this research provide a crucial data foundation for high-resolution pollutant diffusion simulations and environmental impact assessments. They also offer a macro perspective for observing construction pollution, which can be utilized by the government to monitor and control pollution from an urban perspective. [ABSTRACT FROM AUTHOR]

Water body extraction from high-resolution remote sensing images often results in incomplete and discontinuous extraction results due to factors such as complex types of ground objects and narrow rivers. To address these challenges comprehensively, combined with the spectral and texture characteristics of the water body itself, this paper proposed an innovative method for water body extraction and optimization in high-resolution remote sensing images that combines global features and local interconnection. The proposed method integrates the intrinsic spectral and textural features of water bodies, adopting a strategy of initial extraction followed by optimization and skillfully blending global and local communication features. Firstly, linear tributaries are recognized based on the initial water extraction results using multiscale Frangi filtering and the OTSU segmentation algorithm and river replenishment is applied to these linear features, resulting in preliminary optimization results. Then, a locally interconnected disconnection continuation algorithm is introduced to connect disjointed segments in the preliminary optimization results ingeniously. The final stage involves the extraction of water body segments using K-means clustering, and topology inspection and spectral inspection are performed with the accurate optimization results to achieve verification and screening of blocky water bodies. Then, rigorous checks are conducted to validate and filter block-shaped water bodies, ensuring the precision of the extraction results. Experimental results validate the outstanding performance of this method, particularly in the extraction of small tributaries, resulting in a substantial improvement in optimization accuracy. Introducing the disconnection continuation algorithm enhances the completeness of river extraction and fortifies the overall robustness of water body extraction. Comparative analysis with other methods reveals an overall accuracy improvement of 1.04% and 1.50%, along with an increase in comprehensive value by 5.84% and 8.28%, respectively. It can be used as a means to effectively improve the post-processing of water body optimization and improve the integrity and accuracy of water body extraction. [ABSTRACT FROM AUTHOR]

To solve the large proportion of small targets and complex background in UAV (unmanned aerial vehicle) aerial image, the current object detection model has the problems of low accuracy and missed detection of small targets. Based on the YOLOv8s model, this paper proposes a 3D multi-layer feature collaboration UAV remote sensing object detection algorithm. Firstly, based on the coordinate attention, this paper proposes 3D multi-branch coordinate attention (MBCA), which improves the global feature extraction ability of the model and reduces the computation of spatial dimension by increasing the information interaction of channel dimension and the splitting and fusion of extended branches. Secondly, SPD-Conv is used to replace part of the standard convolution, which effectively retains more feature information and speeds up inference during downsampling. Then, a more efficient FastDBB_Bottleneck module is used in the C2f module, combining PConv and DBB structure reparameterization superposition to further reduce the calculation of the model. Finally, PG-Detect detection head is introduced to significantly reduce the calculation and effectively reduce the missed detection rate of small targets. Experimental results on the VisDrone2019 dataset show that the mAP50 value of the proposed method reaches 44.5%, which is 5.7 percentage points higher than that of the YOLOv8s baseline model. Simultaneously, the crack detection verification experiment is carried out on the self-built dam crack dataset, and the mAP50 value of the improved method is 3.3 percentage points higher than that of YOLOv8s, the FPS reaches 289 frames. Experimental results show that the proposed method improves the accuracy and real- time performance of the detection model in complex scene object detection, and has good adaptability and robustness. [ABSTRACT FROM AUTHOR]

As a fundamental computer vision task, image dehazing aims to preprocess degraded images by restoring color contrast and texture information to improve visibility and image quality, thereby the clear images can be recovered for subsequent high- level visual tasks, such as object detection, tracking, and object segmentation. In recent years, neural network-based dehazing methods have achieved notable success, with a growing number of Transformerbased dehazing approaches being proposed. Up to now, there is a lack of comprehensive review that thoroughly analyzes Transformer-based image dehazing algorithms. To fill this gap, this paper comprehensively sorts out Transformerbased daytime, nighttime and remote sensing image dehazing algorithms, which not only covers the fundamental principles of various types of dehazing algorithms, but also explores the applicability and performance of these algorithms in different scenarios. In addition, the commonly used datasets and evaluation metrics in image dehazing tasks are introduced. On this basis, analysis of the performance of existing representative dehazing algorithms is car- generried out from both quantitative and qualitative perspectives, and the performance of typical dehazing algorithms in terms of dehazing effect, operation speed, resource consumption is compared. Finally, the application scenarios of image dehazing technology are summarized, and the challenges and future development directions in the field of image dehazing are analyzed and prospected. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)