Otsu Algorithm Research Articles

Maize (Zea mays L.) Stem Target Region Extraction and Stem Diameter Measurement Based on an Internal Gradient Algorithm in Field Conditions

The target region and diameter of maize stems are important phenotyping parameters for evaluating crop vitality and estimating crop biomass. To address the issue that the target region and diameter of maize stems obtained after transplantation may not accurately reflect the true growth conditions of maize, a phenotyping monitoring technology based on an internal gradient algorithm is proposed for acquiring the target region and diameter of maize stems. Observations were conducted during the small bell stage of maize. First, color images of maize plants were captured by an Intel RealSense D435i camera. The color information in the color image was extracted by the hue saturation value (HSV) color space model. The maximum between-class variance (Otsu) algorithm was applied for image threshold segmentation to obtain the main stem of maize. Median filtering, image binarization, and morphological opening operations were then utilized to remove noise from the images. Subsequently, the morphological gradient algorithm was applied to acquire the target region of maize stems. The similarity between the three types of gradient images and the manually segmented image was evaluated by pixel ratio extraction and image quality assessment indicators. Evaluation results indicated that the internal gradient algorithm could more accurately obtain the target region of maize stems. Finally, a checkerboard was employed as a reference for measurement assistance, and the stem diameter of maize was calculated by the pinhole imaging principle. The mean absolute error of stem diameter was 1.92 mm, the mean absolute percentage error (MAPE) was 5.16%, and the root mean square error (RMSE) was 2.25 mm. The R² value was 0.79. With an R² greater than 0.7 and a MAPE within 6%, the phenotyping monitoring technology based on the internal gradient algorithm was proven to accurately measure the diameter of maize stems. The application of phenotyping monitoring technology based on the internal gradient algorithm in field conditions provides technological support for smart agriculture.

An Approach towards a Practicable Assessment of Neonatal Piglet Body Core Temperature Using Automatic Object Detection Based on Thermal Images

Body core temperature (BCT) is an important characteristic for the vitality of pigs. Suboptimal BCT might indicate or lead to increased stress or diseases. Thermal imaging technologies offer the opportunity to determine BCT in a non-invasive, stress-free way, potentially reducing the manual effort. The current approaches often use multiple close-up images of different parts of the body to estimate the rectal temperature, which is laborious under practical farming conditions. Additionally, images need to be manually annotated for the regions of interest inside the manufacturer’s software. Our approach only needs a single (top view) thermal image of a piglet to automatically estimate the BCT. We first trained a convolutional neural network for the detection of the relevant areas, followed by a background segmentation using the Otsu algorithm to generate precise mean, median, and max temperatures of each detected area. The best fit of our method had an R2 = 0.774. The standardized setup consists of a “FLIROnePro” attached to an Android tablet. To sum up, this approach could be an appropriate tool for animal monitoring under commercial and research farming conditions.

A dataset of the annual water bodies in wet seasons on the Mongolian Plateau during 1990–2021

The Mongolian Plateau is characteristic of fragile ecosystem and serious land desertification. As one of the main climate-varied sensitive regions in Asia, it is of great value for the study on temporal and spatial changes of water resources. For the production of this dataset, we used Google Earth engine (GEE) platform to process the high–quality Landsat series satellite images available in the past 32 years (1990–2021). Using the minimum cloud amount synthesis algorithm, we obtained the minimum cloud amount images in the wet season (from June to September) of each year. After calculating NDWI, we further used the OTSU algorithm for threshold segmentation, and extracted the yearly water body data with 30m resolution on the Mongolian Plateau in the wet season for 32 years. The final data results are saved in GeoTIFF format. Through comparison, the average consistency between this dataset and the JRC annual water body data in both permanent water body and maximum water body is 93.0% and 90.9% respectively, which indicates the high reliability of this dataset. The dataset can provide data support for water resource changes, ecological construction planning, environmental protection, etc. on the Mongolian Plateau.

Comparing Water Indices for Landsat Data for Automated Surface Water Body Extraction under Complex Ground Background: A Case Study in Jilin Province

Derived from Landsat imagery and capable of enhancing the contrast between surface water bodies and the background, water indices are widely used in surface water body extraction. Whether one index and its optimal threshold can maintain the best all year round is a question. At present, however, little research has considered the effect of time or conducted experiments with data from different months. To identify the best index for surface water body extraction, two regions in Jilin Province were selected for the case study and a comprehensive comparative analysis considering the imagery acquisition time was conducted. Ten water indices were selected and calculated based on the 30 m spatial resolution Landsat TM/OLI imagery acquired in 1999 and 2001 and 2019 and 2021 from May to October. The indices included the Modified Normalized Difference Water Index (NDWI3 and MNDWI), Automated Water Extraction Index (AWEI) for images with and without shadow, Multi-Band Water Index (MBWI), New Water Index (NWI), Water Ratio Index (WRI), Sentinel-2 Water Index (SWI) originally calculated based on the Sentinel-2 imagery, New Comprehensive Water Index (NCIWI), Index of Water Surfaces (IWS), and Enhanced Water Index (EWI). The OTSU algorism was adopted to adaptively determine the optimal segmentation threshold for each index and the indices were compared in terms of inter-class separability, threshold sensitivity, optimal threshold robustness, and water extraction accuracy. The result showed that NWI and EWI performed the best in different months and years, with the best water enhancement effect that could suppress background information, especially for the water-related land use types and cloud pollution. Their optimal segmentation thresholds throughout the study period were more stable than others, with the ranges of −0.342 to −0.038 and −0.539 to −0.223, respectively. Based on the optimal thresholds, they achieved overall accuracies of 0.952 to 0.981 and 0.964 to 0.981, commission errors of 0 to 28.2% and 0 to 7.7%, and omission errors of 0 to 15% and 0 to 8%, with a kappa coefficient above 0.8 indicating good extraction results. The results demonstrated the effectiveness of NWI and EWI combined with the OTSU algorithm in better monitoring surface water during different water periods and offering reliable results. Even though this study only focuses on the lakes within two regions, the indices have the potential for accurately monitoring the surface water over other regions.

Maize Stem Contour Extraction and Diameter Measurement Based on Adaptive Threshold Segmentation in Field Conditions

Solving the problem of the stem contour extraction of maize is difficult under open field conditions, and the stem diameter cannot be measured quickly and nondestructively. In this paper, maize at the small and large bell stages was the object of study. An adaptive threshold segmentation algorithm based on the color space model was proposed to obtain the stem contour and stem diameter of maize in the field. Firstly, 2D images of the maize stem in the field were captured with an RGB-D camera. Then, the images were processed by hue saturation value (HSV) color space. Next, the stem contour of the maize was extracted by maximum between-class variance (Otsu). Finally, the reference method was used to obtain the stem diameter of the maize. Scatter plots and Dice coefficients were used to compare the contour extraction effects of the HSV + fixed threshold algorithm, the HSV + Otsu algorithm, and the HSV + K-means algorithm. The results showed that the HSV + Otsu algorithm is the optimal choice for extracting the maize stem contour. The mean absolute error, mean absolute percentage error (MAPE), and root mean square error (RMSE) of the maize stem diameter at the small bell stage were 4.30 mm, 10.76%, and 5.29 mm, respectively. The mean absolute error, MAPE, and RMSE of the stem diameter of the maize at the large bell stage were 4.78 mm, 12.82%, and 5.48 mm, respectively. The MAPE was within 10–20%. The results showed that the HSV + Otsu algorithm could meet the requirements for stem diameter measurement and provide a reference for the acquisition of maize phenotypic parameters in the field. In the meantime, the acquisition of maize phenotypic parameters under open field conditions provides technical and data support for precision farming and plant breeding.

Revisited Otsu Algorithm for Skin Cancer Segmentation

Computer-aided technology can be used to perform a quantitative and objective evaluation of pigmented skin lesions during the clinical assessment procedure. This helps to expedite the procedure. The growing development of non-invasive techniques can be of significant benefit in the early identification of malignant melanoma, which can, in turn, help to minimize the necessity for invasive biopsies. The system is primarily focused on two principal schemes: Establishing an effective lesion border detection method and then creating an efficient classification scheme. We address two primary areas in this work. First, we study skin lesion detection to analyze any sign of malignancy for skin cancer diagnosis. This is followed by the system implementation of a color-based method for all the images from the RGB color space using a revisited OTSU thresholding segmentation scheme. The results proved to be promising with at least an 80% accuracy detection rate for a wide range of clinical skin lesion images.

A Wildfire Detection Algorithm Based on the Dynamic Brightness Temperature Threshold

Satellite remote sensing plays an important role in wildfire detection. Methods using the brightness and temperature difference of remote sensing images to determine if a wildfire has occurred are one of the main research directions of forest fire monitoring. However, common wildfire detection algorithms are mainly based on a fixed brightness temperature threshold to distinguish wildfire pixels and non-wildfire pixels, which reduces the applicability of the algorithm in different space–time regions. This paper presents an adaptive wildfire detection algorithm, DBTDW, based on a dynamic brightness temperature threshold. First, a regression dataset, MODIS_DT_Fire, was constructed based on moderate resolution imaging spectroradiometry (MODIS) to determine the wildfire brightness temperature threshold. Then, based on the meteorological information, normalized difference vegetation index (NDVI) information, and elevation information provided by the dataset, the DBTDW algorithm was used to calculate and obtain the minimum brightness temperature threshold of the burning area by using the Planck algorithm and Otsu algorithm. Finally, six regression models were trained to establish the correlation between factors and the dynamic brightness temperature threshold of wildfire. The root-mean-square error (RMSE) and mean absolute error (MAE) were used to evaluate the regression performance. The results show that under the XGBoost model, the DBTDW algorithm has the best prediction effect on the dynamic brightness temperature threshold of wildfire (leave-one-out method: RMSE/MAE = 0.0730). Compared with the method based on a fixed brightness temperature threshold, the method proposed in this paper to adaptively determine the brightness temperature threshold of wildfire has higher universality, which will help improve the effectiveness of satellite remote fire detection.

A Reflection Symmetric Target Extraction Method Based on Hypothesis Testing for PolSAR Calibration

Polarimetric calibration is indispensable to quantitatively apply and analyze the polarimetric synthetic aperture radar (PolSAR) image. At present, the polarimetric calibration methods relying on the assumption of reflection symmetry have been widely used, which need to extract the reference targets that meet the assumptions before calibration and then calculate the cross-pol channel imbalance and crosstalk errors. However, the distortion in the uncalibrated image will affect the calculation of polarization features, resulting in inaccurate target extraction results. Consequently, we proposed a reflection symmetric target extraction method that combines with spatial statistics information. The method first takes the initial extraction result based on the polarization power total Span and introduces the hypothesis testing to judge the homogeneous samples. Finally, we automatically calculate the threshold by the Otsu algorithm to achieve high-precision extraction of the reflection symmetric targets. Meanwhile, we carried out the polarimetric calibration experiments based on real C- and X-band airborne PolSAR data and conducted qualitative and quantitative evaluation and analysis of the experimental results. The studies demonstrated that, compared with classical approaches, the proposed approach further improved the accuracy of polarimetric calibration by extracting more accurate reference samples.

Image Segmentation of Cucumber Seedlings Based on Genetic Algorithm

To solve the problems of the low target-positioning accuracy and weak algorithm robustness of target-dosing robots in greenhouse environments, an image segmentation method for cucumber seedlings based on a genetic algorithm was proposed. Firstly, images of cucumber seedlings in the greenhouse were collected under different light conditions, and grayscale histograms were used to evaluate the quality of target and background sample images. Secondly, the genetic algorithm was used to determine the optimal coefficient of the graying operator to further expand the difference between the grayscale of the target and background in the grayscale images. Then, the Otsu algorithm was used to perform the fast threshold segmentation of grayscale images to obtain a binary image after coarse segmentation. Finally, morphological processing and noise reduction methods based on area threshold were used to remove the holes and noise from the image, and a binary image with good segmentation was obtained. The proposed method was used to segment 60 sample images, and the experimental results show that under different lighting conditions, the average F1 score of the obtained binary images was over 94.4%, while the average false positive rate remained at about 1.1%, and the image segmentation showed strong robustness. This method can provide new approaches for the accurate identification and positioning of targets as performed by target-dosing robots in a greenhouse environment.

Fast detection of dam zone boundary based on Otsu thresholding optimized by enhanced harris hawks optimization.

Earth-rock dams are among the most important and expensive infrastructure projects. A key safety issue is dam zone boundary detection to prevent the intrusion of materials from different zones. However, existing detection methods strongly highly depend on human judgement, which is time consuming and labor intensive. To solve this problem, this work proposes a fast boundary detection method based on the Otsu algorithm optimized by enhanced Harris hawks optimization (HHO). Compared with the original Otsu algorithm, the proposed method has a higher computation speed to meet the time requirements of engineering projects. Particle swarm optimization is adopted to enhance the exploration stage of HHO. In addition, a tangent function and chaotic sine map are used to improve the convergence speed and robustness. The application of the proposed method to a real-life project shows that the calculation time can be reduced to 20 s, which is approximately 18.8% of the original calculation time.

Automatic Thinning Detection through Image Segmentation Using Equivalent Array-Type Lamp-Based Lock-in Thermography.

Among the non-destructive testing (NDT) techniques, infrared thermography (IRT) is an attractive and highly reliable technology that can measure the thermal response of a wide area in real-time. In this study, thinning defects in S275 specimens were detected using lock-in thermography (LIT). After acquiring phase and amplitude images using four-point signal processing, the optimal excitation frequency was calculated. After segmentation was performed on each defect area, binarization was performed using the Otsu algorithm. For automated detection, the boundary tracking algorithm was used. The number of pixels was calculated and the detectability using RMSE was evaluated. Clarification of defective objects using image segmentation detectability evaluation technique using RMSE was presented.

Intercomparison of Automated Near-Real-Time Flood Mapping Algorithms Using Satellite Data and DEM-Based Methods: A Case Study of 2022 Madagascar Flood

Numerous algorithms have been developed to automate the process of delineating water surface maps for flood monitoring and mitigation purposes by using multiple sources such as satellite sensors and digital elevation model (DEM) data. To better understand the causes of inaccurate mapping information, we aim to demonstrate the advantages and limitations of these algorithms through a case study of the 2022 Madagascar flooding event. The HYDRAFloods toolbox was used to perform preprocessing, image correction, and automated flood water detection based on the state-of-the-art Edge Otsu, Bmax Otsu, and Fuzzy Otsu algorithms for the satellite images; the FwDET tool was deployed upon the cloud computing platform (Google Earth Engine) for rapid estimation of flood area/depth using the digital elevation model (DEM) data. Generated surface water maps from the respective techniques were evaluated qualitatively against each other and compared with a reference map produced by the European Union Copernicus Emergency Management Service (CEMS). The DEM-based maps show generally overestimated flood extents. The satellite-based maps show that Edge Otsu and Bmax Otsu methods are more likely to generate consistent results than those from Fuzzy Otsu. While the synthetic-aperture radar (SAR) data are typically favorable over the optical image under undesired weather conditions, maps generated based on SAR data tend to underestimate the flood extent as compared with reference maps. This study also suggests the newly launched Landsat-9 serves as an essential supplement to the rapid delineation of flood extents.

MTF Measurement by Slanted-Edge Method Based on Improved Zernike Moments.

The modulation transfer function (MTF) is an important parameter for performance evaluation of optical imaging systems in photogrammetry and remote sensing; the slanted-edge method is one of the main methods for measuring MTF. To solve the problem of inaccurate edge detection by traditional methods under the conditions of noise and blur, this paper proposes a new method of MTF measurement with a slanted-edge method based on improved Zernike moments, which firstly introduces the Otsu algorithm to automatically determine the Zernike moment threshold for sub-pixel edge detection to precisely locate the edge points, then obtains LSF through edge point projection, ESF sampling point acquisition, smoothing, fitting, taking ESF curve differential and Gaussian fitting, and finally, accurately obtaining MTF by LSF Fourier transform and modulo normalization. Based on simulation experiments and outdoor target experiments, the reliability of the proposed algorithm is verified by the deviations of slanted-edge angle and MTF measurement, and the tolerance degree of edge detection to noise and ambiguity are analyzed. The results show that compared with ISO 12233, OMNI-sine method, Hough transform method and LSD method, this algorithm has the highest edge detection accuracy, the maximum tolerance of noise and ambiguity, and also improves the accuracy of MTF measurement.

Sugarcane Node Detection Method Based on Photoelectric Sensor Vertical Projection Signal Processing

Highlights A linear array CCD sensor is utilized to obtain the contour signal of the vertical projection of the sugarcane. A method is provided for continuously identifying and locating sugarcane nodes. Examines the impact of scan speed and illumination on the accuracy of identification. The method performs well regarding identification rate, precision, and efficiency. Abstract. In order to achieve continuous and dynamic detection of sugarcane nodes, improve the automatic production efficiency of pre-cut sugarcane seed, and lower the cost of mechanized sugarcane production, a detection method based on linear array charge-coupled device (CCD) photoelectric sensor signal processing was developed. Firstly, the mechanical drive unit was controlled to drive the photoelectric detection system to acquire the signal of the vertical projection of the sugarcane profile. The projection information was then binarized into profile information using the Otsu algorithm. The profile signal was then decomposed using a variable mode decomposition algorithm optimized based on the sparrow search algorithm, and the component reflecting the node content was regarded as the feature signal. Finally, the position of the wave peaks above the judgment threshold in the normalized feature signal was considered the position of the sugarcane nodes. One-way and two-way experiments were conducted to investigate the effects of scan speed and illuminance on identification precision. The results showed that the identification rate, average response time, and average error values were 98.40%, 0.13 s, and 1.36 mm at a scan speed of 75 mm/s and an illuminance of 91.91 lx. Compared to other node identification methods discussed in this article, the proposed method has a high identification rate and accuracy with a high response speed, which can improve the automation efficiency of sugarcane seed production. Keywords: Identification accuracy, Non-contact detection, Photoelectric sensor, Precision agriculture, Seed production, Signal processing, Sugarcane node, Variational mode decomposition.

The use of multi-sensor satellite imagery to analyze flood events and land cover changes using change detection and machine learning techniques in the Barito watershed

Indonesia is one of the countries in the world that is frequently affected by floods. Flood disasters can have various negative impacts and therefore need to be analyzed to determine prevention and mitigation measures. This study examined land cover change, flood detection, and flood distribution using multitemporal Sentinel-1 and Landsat-8 satellite imagery in the Barito watershed. A combination of change detection and the application of the Otsu algorithm was used to detect floodplains from Sentinel-1 imagery. Land use/land cover (LULC) changes are detected using a combination of change detection and machine learning in the form of a random forest algorithm. The overlay technique was used to analyze the distribution of floodplains. In this study, the floodplain in the study area was mapped to 109,623 ha. The change detection method detects a decrease in the areas of primary forest, secondary forest, fields, rice fields, shrubs and ponds, respectively, by 13,020 ha, 116,235 ha, 259 ha, 146,696 ha, 47,308 ha, and 9,601 ha. Settlements, bare land, plantations and water bodies increase by 14,879 ha, 64,830 ha, 218,916 ha, and 34,768 ha, respectively. Flooding was mainly found in the classes of rice fields, water bodies and primary forests.

Extraction and spatiotemporal changes of open-pit mines during 1985-2020 using Google Earth Engine: A case study of Qingzhou City, Shandong Province, China.

The global use of mineral resources has increased exponentially for decades and will continue to grow for the foreseeable future, resulting in increasingly negative impacts on the surrounding environment. However, to date, there are a lack of historical and current spatial extent datasets with high accuracy for mining areas in many parts of the world, which has hindered a more comprehensive understanding of the environmental impacts of mining. Using the Google Earth Engine cloud platform and the Landsat normalized difference vegetation index (NDVI) datasets, the spatial extent data of open-pit mining areas for eight years (1985, 1990, 1995, 2000, 2005, 2010, 2015, and 2020) was extracted by the Otsu algorithm. The limestone mining areas in Qingzhou, Shandong Province, China, was selected as a case study. The annual maximum NDVI was first derived from the Landsat NDVI datasets, and then the Otsu algorithm was used to segment the annual maximum NDVI images to obtain the extent of the mining areas. Finally, the spatiotemporal characteristics of the mining areas in the study region were analyzed in reference to previous survey data. The results showed that the mining areas were primarily located in Shaozhuang Town, Wangfu Street and the northern part of Miaozi Town, and the proportion of mining areas within these three administrative areas has increased annually from 88% in 1985 to more than 98% in 2010. Moreover, the open-pit mining areas in ​​Qingzhou gradually expanded from a scattered, point-like distribution to a large, contiguous distribution. From 1985 to 2020, the open-pit mining area expanded to more than 10 times its original size at a rate of 0.5 km2/year. In 2015, this area reached its maximum size of 19.7 km2 and slightly decreased in 2020. Furthermore, the expansion of the mining areas in Qingzhou went through three stages: a slow growth period before 1995, a rapid expansion period from 1995 to 2005, and a shutdown and remediation period after 2005. A quantitative accuracy assessment was performed by calculating the Intersection over Union (IoU) of the extraction results and the visual interpretation results from Gaofen-2 images with 1-m spatial resolution. The IoU reached 72%. The results showed that it was feasible to threshold the Landsat annual maximum NDVI data by the Otsu algorithm to extract the annual spatial extent of the open-pit mining areas. Our method will be easily transferable to other regions worldwide, enabling the monitoring of mine environments.

CNV-PCC: An efficient method for detecting copy number variations from next-generation sequencing data.

Copy number variations (CNVs) significantly influence the diversity of the human genome and the occurrence of many complex diseases. The next-generation sequencing (NGS) technology provides rich data for detecting CNVs, and the read depth (RD)-based approach is widely used. However, low CN (copy number of 3-4) duplication events are challenging to identify with existing methods, especially when the size of CNVs is small. In addition, the RD-based approach can only obtain rough breakpoints. We propose a new method, CNV-PCC (detection of CNVs based on Principal Component Classifier), to identify CNVs in whole genome sequencing data. CNV-PPC first uses the split read signal to search for potential breakpoints. A two-stage segmentation strategy is then implemented to enhance the identification capabilities of low CN duplications and small CNVs. Next, the outlier scores are calculated for each segment by PCC (Principal Component Classifier). Finally, the OTSU algorithm calculates the threshold to determine the CNVs regions. The analysis of simulated data results indicates that CNV-PCC outperforms the other methods for sensitivity and F1-score and improves breakpoint accuracy. Furthermore, CNV-PCC shows high consistency on real sequencing samples with other methods. This study demonstrates that CNV-PCC is an effective method for detecting CNVs, even for low CN duplications and small CNVs.

Improved Mask R-CNN Combined with Otsu Preprocessing for Rice Panicle Detection and Segmentation

Rice yield is closely related to the number and proportional area of rice panicles. Currently, rice panicle information is acquired with manual observation, which is inefficient and subjective. To solve this problem, we propose an improved Mask R-CNN combined with Otsu preprocessing for rice detection and segmentation. This method first constructs a rice dataset for rice images in a large field environment, expands the dataset using data augmentation, and then uses LabelMe to label the rice panicles. The optimized Mask R-CNN is used as a rice detection and segmentation model. Actual rice panicle images are preprocessed by the Otsu algorithm and input into the model, which yields accurate rice panicle detection and segmentation results using the structural similarity and perceptual hash value as the measurement criteria. The results show that the proposed method has the highest detection and segmentation accuracy for rice panicles among the compared algorithms. When further calculating the number and relative proportional area of the rice panicles, the average error of the number of rice panicles is 16.73% with a minimum error of 5.39%, and the error of the relative proportional of rice panicles does not exceed 5%, with a minimum error of 1.97% and an average error of 3.90%. The improved Mask R-CNN combined with Otsu preprocessing for rice panicle detection and segmentation proposed in this paper can operate well in a large field environment, making it highly suitable for rice growth monitoring and yield estimation.

Underwater acoustic signal denoising model based on secondary variational mode decomposition

Due to the complexity of marine environment, underwater acoustic signal will be affected by complex background noise during transmission. Underwater acoustic signal denoising is always a difficult problem in underwater acoustic signal processing. To obtain a better denoising effect, a new denoising method of underwater acoustic signal based on optimized variational mode decomposition by black widow optimization algorithm (BVMD), fluctuation-based dispersion entropy threshold improved by Otsu method (OFDE), cosine similarity stationary threshold (CSST), BVMD, fluctuation-based dispersion entropy (FDE), named BVMD-OFDE-CSST-BVMD-FDE, is proposed. In the first place, decompose the original signal into a series of intrinsic mode functions (IMFs) by BVMD. Afterwards, distinguish pure IMFs, mixed IMFs and noise IMFs by OFDE and CSST, and reconstruct pure IMFs and mixed IMFs to obtain primary denoised signal. In the end, decompose primary denoising signal into IMFs by BVMD again, use the FDE value to distinguish noise IMFs and pure IMFs, and reconstruct pure IMFs to obtain the final denoised signal. The proposed mothod has three advantages: (i) BVMD can adaptively select the decomposition layer and penalty factor of VMD. (ii) FDE and CS are used as double criteria to distinguish noise IMFs from useful IMFs, and Otsu algorithm and CSST algorithm can effectively avoid the error caused by manually selecting thresholds. (iii) Secondary decomposition can make up for the deficiency of primary decomposition and further remove a small amount of noise. The chaotic signal and real ship signal are denoised. The experiment result shows that the proposed method can effectively denoise. It improves the denoising effect after primary decomposition, and has good practical value.

Nighttime Cloud Cover Estimation Method at the Saishiteng 3850 m Site

Cloud cover is critical for astronomical sites because it can be used to assess the observability of the local sky and further the fractional photometric time. For cloud monitoring in site-testing campaigns with all-sky cameras, previous studies have mainly focused on moonless images, while the automatic processing methods for moonlight images are explored quite few. This paper proposes an automatic estimation method for cloud cover, which takes all cases of nighttime gray-scale all-sky images into account. For moonless images, the efficient Otsu algorithm is directly used to detect clouds. For moonlight images, they are transformed into cloud feature image using a colorization procedure, and then the Otsu algorithm is used to distinguish cloud pixels from sky pixels on the cloud feature image. The reliability of this method was evaluated on manually labeled images. The results show that the cloud cover error of this method is less than 9% in all scenarios. The fractional photometric time derived from this method is basically consistent with the published result of the Lenghu site.