Spatial Coordinates Correction Based on Multi-Sensor Low-Altitude Remote Sensing Image Registration for Monitoring Forest Dynamics

Abstract

Tree species diversity plays a significant role in our ecosystem. In order to monitor forest dynamics, hyperspectral remote sensing equipped on a small unmanned aerial vehicle (UAV) is commonly applied, such as individual tree detection and classification. However, low resolution, positioning errors and the imaging perspective of small UAV affected by wind speed/direction, complex terrain, battery capacity, aircraft posture, flying height and other human factors result in relatively large positional errors (i.e., GPS errors) in such hyperspectral images, and the precise forest dynamics monitoring is limited, especially in spatial analysis. In order to reduce the positional errors of hyperspectral images captured from a small UAV and provide a precise forest dynamics monitoring, we present a novel spatial coordinates correction approach by registering low-altitude UAV visible light and hyperspectral images. The proposed method first employs visible light images and ground control points to stitch a geographic coordinate system as our groundtruth. Hyperspectral images (UHI) are then registered onto the stitched visible light image (UVI) via a novel image registration method. Finally, spatial coordinates of the registered hyperspectral images are updated by using the aforementioned groundtruth. Extensive experiments on image registration and spatial coordinates correction demonstrate the favorable performance of our method. Compared against four stateof-the-art registration methods, our method shows the best registration performance, and the positional errors of hyperspectral images are significantly reduced. Such accuracy is considered very high in this research. INDEX TERMS Forest dynamics, small unmanned aerial vehicle, multi-sensor image registration, spatial coordinates correction, hyperspectral images.

Study of the Urban Heat Island (UHI) Using Remote Sensing Data/Techniques: A Systematic Review

“…Although LST is widely applied for studies that aim to analyze SUHI, this parameter is cross-cutting and can corroborate for the analysis of other topics, for example: environmental analyses with research on aquifers/water bodies [80], biomes [2], forest dynamics [81], and natural hazards such as volcanic activity studies [82]; air pollution [83] and determination of particulate concentration in urbanized areas [84]; architectural assessment to identify the building area and building layout with the least impact to the local microclimate [85] and identification of Local Climate Zones (LCZ) [86], from the identification of regions with uniform characteristics, from hundreds of meters to several kilometers, with respect to land cover, surface structure, building materials and human activities [10], use of hybrid data, including physical and mobile equipment for climate characterization of a site [87], socioeconomic and cultural analysis, including studies on biophysical and socioeconomic impacts [88][89][90], and social events [91]; and public health and mortality associated with HWs [92].…”

Qualitative Precipitation Estimation from Satellite Data Based on Distributed Domain-Specific Architecture

“…High-level semantic information and low-level location information Multiscale object detection [6] Region-based convolutional neural networks (RCNNs) and feature pyramid network Adaptive anchor networks Automatic aggregation [7] Hierarchical similarity diffusion To measure the similarity matrix of image in the data set cluster by cluster Monitoring forest dynamics [8] Spatial coordinates correction approach Registering low-altitude UAV visible light and hyperspectral images 2 Modelling and Simulation in Engineering embedded manycore cluster [20] was presented implemented on a Parallella board. For big-data-related applications, an in-memory computing architecture for heterogeneous CPU-GPU clusters was presented in [21], called GFlink.…”

“…Based on the intelligent annotation approach, an automatic aggregation via hierarchical similarity diffusion has been modified [7]. A dynamic monitoring technology using a spatial coordinates correction approach [8] has been presented by using low altitude unmanned aerial vehicle (UAV) visible light and hyperspectral images for monitoring forest dynamics. So, the research work on the remote sensing applications can be summarized in Table 1.…”

Research on Fault Diagnosis Model of Generative Adss Based on Improved Semisupervised Diagnosis Algorithm

“…Some scholars have proposed a self-organizing fault diagnosis algorithm for heterogeneous networks based on AdaBoost ensemble learning, by training Bayesian, decision tree, artificial neural network, and K nearest neighbor classifiers, combined their classification results to gradually approach the real classification results, and used SMOTE technology to deal with the problem of unbalanced data in cellular networks, avoiding the low classification accuracy of a few classes and effectively improving the diagnostic accuracy of classifiers [3]. It is undeniable that intelligent diagnosis algorithms such as RNN, LSTM, and CNN have satisfactory accuracy, but the premise of this high accuracy is to manually add labels to all samples, which will undoubtedly increase the cost of fault diagnosis [4]. Some scholars have proposed a semisupervised generation countermeasure network algorithm based on Gini regularization, which improves the convergence speed and accuracy of the model by adding Gini regularization constraints to the unsupervised loss function of the discriminator.…”