Estimation of Soil Surface Roughness Using Stereo Vision Approach

Azizi, Afshin; Abbaspour‐Gilandeh, Yousef; Mesri-Gundoshmian, Tarahom; Farooque, Aitazaz A.; Afzaal, Hassan

doi:10.3390/s21134386

Cited by 12 publications

(3 citation statements)

References 29 publications

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“…Root Mean Square Error (RMSE) X (Pixel)= 1.07, RMSE Y= 1.65, and RMSE XY= as the Fig. 19[23].HerodowiczMleczak, et al, (2022), this study shows the way of doing a quantitative analysis of the roughness of different soil surfaces. The researchers discovered an intrinsic association that revealed various information regarding the roughness of the soil surface.…”

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confidence: 63%

Evaluating The Approaches of the 3D Models Generation of a Soil Surface: A Review Study

Shaimaa Hailem Teamaa,

Jasim Ahmed Ali ALbaghdadi,

Farid Majid Abd

2023

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There are various approaches used to create 3D models of soil surfaces, including field surveying, photogrammetry and laser scanning methods. However, most of these methods are high cost, requiring many employees and long processing time. Thus, this study will evaluate these approaches and will show the merits and the significances of each approach used for 3D soil generations. The review study shows that the videogrammetric method is considered the efficient approach for 3D soil surface generation because it is economic, precise, fast, and needs few workers.

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confidence: 63%

Evaluating The Approaches of the 3D Models Generation of a Soil Surface: A Review Study

Shaimaa Hailem Teamaa,

Jasim Ahmed Ali ALbaghdadi,

Farid Majid Abd

2023

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“…. The dataset includes six prairie animals, elephants, zebras, bison, wild horses, giraffes, and hippos, each with about 500 images [48]. Consideration of different time periods, different angles, different distances and occlusions, etc., was achieved by rotating the pictures at different angles, adjusting the contrast, etc.…”

Section: Other Tricksmentioning

confidence: 99%

Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5

Namiki

Suzuki

et al. 2022

Applied Sciences

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With the development of science and technology, the traditional industrial structures are constantly being upgraded. As far as drones are concerned, an increasing number of researchers are using reinforcement learning or deep learning to make drones more intelligent. At present, there are many algorithms for object detection. Although many models have a high accuracy of detection, these models have many parameters and high complexity, making them unable to perform real-time detection. Therefore, it is particularly important to design a lightweight object detection algorithm that is able to meet the needs of real-time detection using UAVs. In response to the above problems, this paper establishes a dataset of six animals in grassland from different angles and during different time periods on the basis of the remote sensing images of drones. In addition, on the basis of the Yolov5s network model, a lightweight object detector is designed. First, Squeeze-and-Excitation Networks are introduced to improve the expressiveness of the network model. Secondly, the convolutional layer of branch 2 in the BottleNeckCSP structure is deleted, and 3/4 of its input channels are directly merged with the results of branch 1 processing, which reduces the number of model parameters. Next, in the SPP module of the network model, a 3 × 3 maximum pooling layer is added to improve the receptive field of the model. Finally, the trained model is applied to NVIDIA-TX2 processor for real-time object detection. After testing, the optimized YOLOv5 grassland animal detection model was able to effectively identify six different forms of grassland animal. Compared with the YOLOv3, EfficientDet-D0, YOLOv4 and YOLOv5s network models, the mAP_0.5 value was improved by 0.186, 0.03, 0.007 and 0.011, respectively, and the mAP_0.5:0.95 value was improved by 0.216, 0.066, 0.034 and 0.051, respectively, with an average detection speed of 26 fps. The experimental results show that the grassland animal detection model based on the YOLOv5 network has high detection accuracy, good robustness, and faster calculation speed in different time periods and at different viewing angles.

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“…They concluded that the surface soil roughness is more sensitive to the vertical variation of the profile than the horizontal. More recently, Azizi et al [45] developed a computerised approach to estimate surface soil roughness based on the stereo vision technique. They computed the elevation component to reconstruct the 3-D model of the images taken from the field.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning to Estimate Surface Roughness from Satellite Images

et al. 2021

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We apply the Support Vector Regression (SVR) machine learning model to estimate surface roughness on a large alluvial fan of the Kosi River in the Himalayan Foreland from satellite images. To train the model, we used input features such as radar backscatter values in Vertical–Vertical (VV) and Vertical–Horizontal (VH) polarisation, incidence angle from Sentinel-1, Normalised Difference Vegetation Index (NDVI) from Sentinel-2, and surface elevation from Shuttle Radar Topographic Mission (SRTM). We generated additional features (VH/VV and VH–VV) through a linear data fusion of the existing features. For the training and validation of our model, we conducted a field campaign during 11–20 December 2019. We measured surface roughness at 78 different locations over the entire fan surface using an in-house-developed mechanical pin-profiler. We used the regression tree ensemble approach to assess the relative importance of individual input feature to predict the surface soil roughness from SVR model. We eliminated the irrelevant input features using an iterative backward elimination approach. We then performed feature sensitivity to evaluate the riskiness of the selected features. Finally, we applied the dimension reduction and scaling to minimise the data redundancy and bring them to a similar level. Based on these, we proposed five SVR methods (PCA-NS-SVR, PCA-CM-SVR, PCA-ZM-SVR, PCA-MM-SVR, and PCA-S-SVR). We trained and evaluated the performance of all variants of SVR with a 60:40 ratio using the input features and the in-situ surface roughness. We compared the performance of SVR models with six different benchmark machine learning models (i.e., Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Binary Decision Tree (BDT), Bragging Ensemble Learning, Boosting Ensemble Learning, and Automated Machine Learning (AutoML)). We observed that the PCA-MM-SVR perform better with a coefficient of correlation (R = 0.74), Root Mean Square Error (RMSE = 0.16 cm), and Mean Square Error (MSE = 0.025 cm2). To ensure a fair selection of the machine learning model, we evaluated the Akaike’s Information Criterion (AIC), corrected AIC (AICc), and Bayesian Information Criterion (BIC). We observed that SVR exhibits the lowest values of AIC, corrected AIC, and BIC of all the other methods; this indicates the best goodness-of-fit. Eventually, we also compared the result of PCA-MM-SVR with the surface roughness estimated from different empirical and semi-empirical radar backscatter models. The accuracy of the PCA-MM-SVR model is better than the backscatter models. This study provides a robust approach to measure surface roughness at high spatial and temporal resolutions solely from the satellite data.

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Estimation of Soil Surface Roughness Using Stereo Vision Approach

Cited by 12 publications

References 29 publications

Evaluating The Approaches of the 3D Models Generation of a Soil Surface: A Review Study

Evaluating The Approaches of the 3D Models Generation of a Soil Surface: A Review Study

Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5

Machine Learning to Estimate Surface Roughness from Satellite Images

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