UAV-hyperspectral imaging of spectrally complex environments

Banerjee, Bikram Pratap; Raval, Simit; Cullen, Patrick J.

doi:10.1080/01431161.2020.1714771

Cited by 65 publications

(51 citation statements)

References 45 publications

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“…Varying illumination issues are more acute in direct sunlight and less severe under cloudy conditions, where the light is more diffuse. So far there are no standard calibration methods, the method of choice has to be designed depending on the senor-platform and illumination situation (Banerjee et al 2020). For HSI under laboratory conditions, calibration routines are well established (Behmann et al 2015a).…”

Section: Sources Of Error Affecting Accuracymentioning

confidence: 99%

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

et al. 2020

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The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and artificial intelligence may help overcome issues for automating severity measurement under hyper-variable field conditions. The adoption of appropriate scales, training, instruction and aids (standard area diagrams) has contributed to improved accuracy of visual estimates. The apogee of accuracy for visual estimation is likely being approached, and any remaining increases in accuracy are likely to be small. Due to automation and rapidity, sensor-based measurement offers potential advantages compared with visual estimates, but the latter will remain important for years to come. Mobile, automated sensor-based systems will become increasingly common in controlled conditions and, eventually, in the field for measuring plant disease severity for the purpose of research and decision making.

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Section: Sources Of Error Affecting Accuracymentioning

confidence: 99%

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

et al. 2020

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“…On the one hand, while hyperspectral imagery shows detailed spectral information, this type of imagery has limitations when discriminating objects with similar spectral characteristics [ 5 , 6 ]. On the other hand, although an airborne LiDAR system can obtain high-precision three-dimensional vertical structure information, such a system cannot accurately classify ground objects due to the lack of corresponding spectral information [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Fusion of Hyperspectral CASI and Airborne LiDAR Data for Ground Object Classification through Residual Network

Chang

Zhang

et al. 2020

Sensors

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Modern satellite and aerial imagery outcomes exhibit increasingly complex types of ground objects with continuous developments and changes in land resources. Single remote-sensing modality is not sufficient for the accurate and satisfactory extraction and classification of ground objects. Hyperspectral imaging has been widely used in the classification of ground objects because of its high resolution, multiple bands, and abundant spatial and spectral information. Moreover, the airborne light detection and ranging (LiDAR) point-cloud data contains unique high-precision three-dimensional (3D) spatial information, which can enrich ground object classifiers with height features that hyperspectral images do not have. Therefore, the fusion of hyperspectral image data with airborne LiDAR point-cloud data is an effective approach for ground object classification. In this paper, the effectiveness of such a fusion scheme is investigated and confirmed on an observation area in the middle parts of the Heihe River in China. By combining the characteristics of hyperspectral compact airborne spectrographic imager (CASI) data and airborne LiDAR data, we extracted a variety of features for data fusion and ground object classification. Firstly, we used the minimum noise fraction transform to reduce the dimensionality of hyperspectral CASI images. Then, spatio-spectral and textural features of these images were extracted based on the normalized vegetation index and the gray-level co-occurrence matrices. Further, canopy height features were extracted from airborne LiDAR data. Finally, a hierarchical fusion scheme was applied to the hyperspectral CASI and airborne LiDAR features, and the fused features were used to train a residual network for high-accuracy ground object classification. The experimental results showed that the overall classification accuracy was based on the proposed hierarchical-fusion multiscale dilated residual network (M-DRN), which reached an accuracy of 97.89%. This result was found to be 10.13% and 5.68% higher than those of the convolutional neural network (CNN) and the dilated residual network (DRN), respectively. Spatio-spectral and textural features of hyperspectral CASI images can complement the canopy height features of airborne LiDAR data. These complementary features can provide richer and more accurate information than individual features for ground object classification and can thus outperform features based on a single remote-sensing modality.

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“…The application of UAV is no longer limited to the original military field. In many fields, such as environmental monitoring, emergency relief, relay communication, target recognition, UAV is playing a core role [1]- [4].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Routing Protocols for UAV Communication Networks

Tan

Zuo

et al. 2020

IEEE Access

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The design of routing protocol is an important and key problem in unmanned aerial vehicle (UAV) communication networks. In low altitude environment, UAV information transmission is a complex task. It is an important scientific challenge to design a routing protocol that can provide efficient and reliable node to node packet transmission. This paper develops a more realistic simulation environment based on OPNET 14.5, and performs performance tests and comparisons on four classic routing protocols: Ad Hoc on demand distance vector (AODV), dynamic source routing (DSR), optimized link state routing (OLSR), and geographic routing protocol (GRP). The performance parameters such as network delay, traffic received, data dropped and throughput are compared and analyzed. The experimental results indicate that different routing protocols can be adapted to different UAV communication network scenarios. Therefore, the quantitative results can provide pertinent reference for choosing the best routing protocol in different scenarios. INDEX TERMS UAV communication networks, ad hoc, routing protocols, performance analysis.

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UAV-hyperspectral imaging of spectrally complex environments

Cited by 65 publications

References 45 publications

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

Fusion of Hyperspectral CASI and Airborne LiDAR Data for Ground Object Classification through Residual Network

Performance Analysis of Routing Protocols for UAV Communication Networks

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