A multiscale morphological algorithm for improvements to canopy height models

Liu, Li; Lim, Samsung; Shen, Xuesong; Yebra, Marta

doi:10.1016/j.cageo.2019.05.012

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…According to the gray distribution characteristics of the pearl images [27], the maximum between-class variance method [28] was used to segment the preprocessed pearl images and solved the problems of cavities and rough contour edges in the segmented pearl area. Linear structural elements were processed by the morphological algorithm [29]; the result is shown in Figure 3b.…”

Section: Boundary Connectivity Problemmentioning

confidence: 99%

Measuring Shape Parameters of Pearls in Batches Using Machine Vision: A Case Study

et al. 2022

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To solve the problem of low precision of pearl shape parameters’ measurement caused by the mutual contact of batches of pearls and the error of shape sorting, a method of contacting pearls’ segmentation based on the pit detection was proposed. Multiple pearl images were obtained by backlit imaging, the quality of the pearl images was improved through appropriate preprocessing, and the contacted pearl area was extracted by calculating the area ratio of the connected domains. Then, the contour feature of the contact area was obtained by edge tracking to establish the mathematical model of the angles between the edge contour points. By judging the angle with a threshold of 60° as the candidate concave point, a concave point matching algorithm was introduced to get the true concave point, and the Euclidean distance was adopted as a metric function to achieve the segmentation of the tangent pearls. The pearl shape parameters’ model was established through the pearl contour image information, and the shape classification standard was constructed according to the national standard. Experimental results showed that the proposed method produced a better segmentation performance than the popular watershed algorithm and morphological algorithm. The segmentation accuracy was above 95%, the average loss rate was within 4%, and the sorting accuracy based on the shape information was 94%.

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Section: Boundary Connectivity Problemmentioning

confidence: 99%

Measuring Shape Parameters of Pearls in Batches Using Machine Vision: A Case Study

et al. 2022

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“…e shape and size of structural element play an important role in the application of mathematical morphology. e triangular SE, the sinusoidal SE, and the linear SE are widely used SEs for signal analysis [32][33][34][35][36].…”

Section: E Shape Selection Of Structural Elementmentioning

confidence: 99%

Chaotic Signal Denoising Based on Adaptive Smoothing Multiscale Morphological Filtering

2020

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Nonlinear time series denoising is the prerequisite for extracting effective information from observation sequence. An effective chaotic signal denoising method not only has a good signal-to-noise ratio (SNR) enhancement performance, but also can remain as a good unpredictable denoised signal. However, the inherent characteristics of chaos, such as extreme sensitivity to initial values and broadband spectrum, pose challenges for noise reduction of polluted chaotic signals. To address these issues, an adaptive smoothing multiscale morphological filtering (ASMMF) is proposed to reconstruct chaotic signals. In the process of noise reduction for contaminated chaotic signals, firstly, a multiscale morphological filter is constructed adaptively according to the multiscale permutation entropy (MPE) of morphological filter residuals, and the contaminated signals are filtered. Secondly, the weight coefficients of scale structural element are calculated by the residual sum of squares operation, and the chaotic signals are reconstructed. Finally, the resampled filter signals are smoothed by cubic B-spline interpolation operation. In the experiment, the Lorenz signal with white Gaussian noise, the measured sunspot, and the chaotic vibration signal are reconstructed by four comparison methods. The test results show that the proposed ASMMF method has obvious advantages in noise suppression and topological trajectory restoration.

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“…Zhao et al [20] improved Ben-Arie's method by proposing the concept of canopy control to limit the canopy range, which improved the efficiency of detecting canopy pits. However, it is difficult to detect crowns with large differences in crown size under a fixed window [35]. Liu et al [35] improved the CHM by using multiscale operators to overcome the window problem; the disadvantage of this is that multiple parameters need to be determined artificially in each multiscale operator, which makes the method rely more on manual experience and trial and error.…”

Section: Introductionmentioning

confidence: 99%

Weighted Differential Gradient Method for Filling Pits in Light Detection and Ranging (LiDAR) Canopy Height Model

Zhou,

Li,

Huang

et al. 2024

Remote Sensing

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The canopy height model (CHM) derived from LiDAR point cloud data is usually used to accurately identify the position and the canopy dimension of single tree. However, local invalid values (also called data pits) are often encountered during the generation of CHM, which results in low-quality CHM and failure in the detection of treetops. For this reason, this paper proposes an innovative method, called “pixels weighted differential gradient”, to filter these data pits accurately and improve the quality of CHM. First, two characteristic parameters, gradient index (GI) and Z-score value (ZV) are extracted from the weighted differential gradient between the pit pixels and their eight neighbors, and then GIs and ZVs are commonly used as criterion for initial identification of data pits. Secondly, CHMs of different resolutions are merged, using the image processing algorithm developed in this paper to distinguish either canopy gaps or data pits. Finally, potential pits were filtered and filled with a reasonable value. The experimental validation and comparative analysis were carried out in a coniferous forest located in Triangle Lake, United States. The experimental results showed that our method could accurately identify potential data pits and retain the canopy structure information in CHM. The root-mean-squared error (RMSE) and mean bias error (MBE) from our method are reduced by between 73% and 26% and 76% and 28%, respectively, when compared with six other methods, including the mean filter, Gaussian filter, median filter, pit-free, spike-free and graph-based progressive morphological filtering (GPMF). The average F1 score from our method could be improved by approximately 4% to 25% when applied in single-tree extraction.

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A multiscale morphological algorithm for improvements to canopy height models

Cited by 6 publications

References 17 publications

Measuring Shape Parameters of Pearls in Batches Using Machine Vision: A Case Study

Measuring Shape Parameters of Pearls in Batches Using Machine Vision: A Case Study

Chaotic Signal Denoising Based on Adaptive Smoothing Multiscale Morphological Filtering

Weighted Differential Gradient Method for Filling Pits in Light Detection and Ranging (LiDAR) Canopy Height Model

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