Threshold Segmentation Algorithm Research Articles

Threshold segmentation based on information fusion for object shadow detection in remote sensing images

In the shadow detection task, the shadow model is usually consistent with the approximate contour of ontology semantics, it is difficult to extract the features of land covered objects or ground pixels, and easy to be confused into foreground objects in gray scale. Therefore, we present to formulate and apply one new threshold segmentation method based on information fusion for object shadow detection in remote sensing images. Firstly, object shadow pixels are screened using intensity and chromaticity information in HSI color space. Secondly, the remote sensing image is carried out by principal component analysis (PCA) to obtain the first principal component. A new shadow index is constructed using the results obtained from HSI and the first principal component. Thirdly, based on the results of the above two information fusion, a threshold segmentation model is established using the improved threshold segmentation algorithm between the maximum and the minimum threshold segmentation algorithm, so as to obtain the final object shadow detection results. Finally, affluent experiments are conducted on the datasets collected from Google Earth. The results show that the proposed object shadow detection algorithm in remote sensing images can achieve better segmentation and detection (more than 95%) effect compared with state-of-the-art methods.

Multilayer grid XG Boost architecture based automatic osteosarcoma classification

The application of machinelearning(ML) and deep learning (DL)methods might reduce the amount of time required by clinicians while simultaneously enhancing patient outcomes. The classification algorithm has to be provided with a huge amount of data in order to improve its accuracy. In this study, a combination of ML and DL is utilized to differentiate between images of normal and necrotic tissues utilizing a public database of osteosarcoma histological images. The data was initially preprocessed, and contour based threshold segmentation algorithms were performed. Next, the anomalous features are extracted using stochastic linear embedding-based feature extraction. Finally, stained photos are used to train the proposed multilayer grid XG Boost classifier, which improves output accuracy. The results of the experiments show that the suggested classifier is the most accurate one presently in use for categorizing illnesses. With H and E stained images, our improved model performed better at detecting osteosarcoma malignancy.

Detection of early bruises on apples using near‐infrared camera imaging technology combined with adaptive threshold segmentation algorithm

AbstractApples are highly susceptible to bruising during picking, packing, and transportation, and early detection of apple bruises allows timely screening and treatment to reduce food safety risks. However, early bruises on apples, especially bruises of 30 min or less, with similar appearance to sound tissue, making manual sorting difficult. To address this problem, a near‐infrared (NIR) camera imaging technology combined with adaptive threshold segmentation algorithm for early apple bruise detection is proposed in this article. First, a NIR camera imaging system is built to acquire full‐band images of fruit face, fruit stem face and calyx face of sound apples and apples with single bruise and multiple bruises at 30 min of occurrence. Then, an adaptive threshold segmentation algorithm is proposed and an image processing algorithm, which includes high‐pass filtering, feature extraction, adaptive threshold segmentation and Hough circle detection, is developed and applied to full‐band images in response to the interference of apple stem and calyx on early bruising detection in this article. The results show that the proposed algorithm has an accuracy of 95.56% for the detection of sound apple samples and an F1‐score of 94.70% for the detection of early bruised samples, which is better than the commonly used image segmentation algorithms such as Otsu and fixed thresholding. This study shows that the combination of NIR camera imaging and the adaptive threshold segmentation algorithm proposed in this article can effectively detect early bruises on apples, providing a new idea for rapid and nondestructive detection of agricultural product quality.Practical applicationsDetecting early bruises on apples can achieve effective monitoring and control of apple supply chain, and guarantee the quality and safety of agricultural products. Most studies are based on conventional machine vision with RGB camera or hyperspectral imaging technology, while conventional machine vision based on RGB camera is difficult to detect apple early bruises that are not visible to the naked eye, and hyperspectral imaging has a large amount of data and redundant image data among wavelengths. Compared with previous studies, apple early bruise detection method proposed in this article combining NIR camera imaging technology and adaptive threshold segmentation algorithm can avoid the interference of bright spots, fruit stem and calyx on bruise segmentation while capturing a single channel image, providing a new method for accurate and rapid detection of early bruises on apples.

Comparison of Multiple Segmentation Methods for Volumetric Delineation of Primary Prostate Cancer with Prostate-Specific Membrane Antigen-Targeted 18F-DCFPyL PET/CT.

This study aimed to assess the accuracy of intraprostatic tumor volume measurements on prostate-specific membrane antigen-targeted 18F-DCFPyL PET/CT made with various segmentation methods. An accurate understanding of tumor volumes versus segmentation techniques is critical for therapy planning, such as radiation dose volume determination and response assessment. Methods: Twenty-five men with clinically localized, high-risk prostate cancer were imaged with 18F-DCFPyL PET/CT before radical prostatectomy. The tumor volumes and tumor-to-prostate ratios (TPRs) of dominant intraprostatic foci of uptake were determined using semiautomatic segmentation (applying SUVmax percentage [SUV%] thresholds of SUV30%-SUV70%), adaptive segmentation (using adaptive segmentation percentage [A%] thresholds of A30%-A70%), and manual contouring. The histopathologic tumor volume (TV-Histo) served as the reference standard. The significance of differences between TV-Histo and PET-based tumor volume were assessed using the paired-sample Wilcoxon signed-rank test. The Spearman correlation coefficient was used to establish the strength of the association between TV-Histo and PET-derived tumor volume. Results: Median TV-Histo was 2.03 cm3 (interquartile ratio [IQR], 1.16-3.36 cm3), and median TPR was 10.16%. The adaptive method with an A40% threshold most closely determined the tumor volume, with a median difference of +0.19 (IQR, -0.71 to +2.01) and a median relative difference of +7.6%. The paired-sample Wilcoxon test showed no significant difference in PET-derived tumor volume and TV-Histo using A40%, A50%, SUV40%, and SUV50% threshold segmentation algorithms (P > 0.05). For both threshold-based segmentation methods, use of higher thresholds (e.g., SUV60% or SUV70% and A50%-A70%) resulted in underestimation of tumor volumes, and use of lower thresholds (e.g., SUV30% or SUV40% and A30%) resulted in overestimation of tumor volumes relative to TV-Histo and TPR. Manual segmentation overestimated the tumor volume, with a median difference of +2.49 (IQR, 0.42-4.11) and a median relative difference of +130%. Conclusion: Segmentation of intraprostatic tumor volume and TPR with an adaptive segmentation approach most closely approximates TV-Histo. This information might be used to guide the primary treatment of men with clinically localized, high-risk prostate cancer.

A novel method for seed cotton color measurement based on machine vision technology

Color of seed cotton is one of the key indexes of seed cotton quality, which greatly affects the price, grading, storage, and processing of seed cotton. Currently, there are shortage of mature color measurement methods and equipment specifically for seed cotton. Therefore, a color measurement method for seed cotton based on machine vision technology was proposed in this research. To solve the problem of color difference in images, a color difference correction algorithm based on multiple linear regression was proposed, which significantly reduced the color difference by 54.19%. To segment large impurities and large hard particles (cotton seeds, cotton stalks, and boll shells) that are easy to produce shadows from seed cotton images, a quadratic dynamic thresholding segmentation algorithm based on multi-channel fusion was proposed, which significantly improved the segmentation accuracy. The verification results showed that the average value of the intersection over union was 0.9. In the calculation of the color indexes of seed cotton, a correction algorithm based on the BP neural network was used to correct the indexes by taking standard tiles as a reference to reduce the difference caused by system error. The results of the machine vision method were compared with those of the detection of corresponding lint by HVI 1000 and spectrophotometer HX-410. The coefficients of determination (R2) of the Reflectance degree (Rd) and Yellowness (+b) measured by HVI 1000 were 0.790 and 0.865, respectively. The R2 for Rd and +b measured by HX-410 were 0.809 and 0.879, respectively. In addition, the analysis results of the effect of impurities and shadows on seed cotton color showed that both impurities and shadows had a negative effect on Rd. However, the effect of shadows on +b was negative and the effect of impurities was positive. This study indicated that it was feasible to detect seed cotton color using machine vision method.

Research on Obstacle Detection and Avoidance of Autonomous Underwater Vehicle Based on Forward-Looking Sonar.

Due to the complexity of the ocean environment, an autonomous underwater vehicle (AUV) is disturbed by obstacles when performing tasks. Therefore, the research on underwater obstacle detection and avoidance is particularly important. Based on the images collected by a forward-looking sonar on an AUV, this article proposes an obstacle detection and avoidance algorithm. First, a deep learning-based obstacle candidate area detection algorithm is developed. This algorithm uses the You Only Look Once (YOLO) v3 network to determine obstacle candidate areas in a sonar image. Then, in the determined obstacle candidate areas, the obstacle detection algorithm based on the improved threshold segmentation algorithm is used to detect obstacles accurately. Finally, using the obstacle detection results obtained from the sonar images, an obstacle avoidance algorithm based on deep reinforcement learning (DRL) is developed to plan a reasonable obstacle avoidance path of an AUV. Experimental results show that the proposed algorithms improve obstacle detection accuracy and processing speed of sonar images. At the same time, the proposed algorithms ensure AUV navigation safety in a complex obstacle environment.

Multilevel Thresholding Segmentation based on Levy-Horse Optimization Algorithm: A Review

Abstract: A novel multi-level thresholding segmentation algorithm is proposed concerning machine learning and a metaheuristic algorithm is used to select multiple threshold value for segmenting the image. Here for the analysis, two, three and five-level of threshold value for the segmentation process is made. The threshold values for two, three and five level is generated by Levy horse optimized support vector machine (LHSVM). This optimized machine learning approach selects a reduced error threshold value to segment every object or region of interest in an image. Then the method is evaluated using Berkeley segmentation dataset and benchmark (BSDS300) dataset. This kind of multi-level threshold segmentation splits the image into class and thereby considers several classes for segmenting each object in the image

A Method for Extracting Contours of Building Facade Hollowing Defects Using Polarization Thermal Images Based on Improved Canny Algorithm

During the service process of high-rise buildings, hollowing defects may be produced in the decorative layer, which not only affect the appearance, but also create a safety hazard of wall covering and shattered plaster peeling. Numerous studies have shown that hollowing can be detected using infrared thermal imagery under normal conditions. However, it is difficult to detect the edge and calculate the area of the hollowing on an exterior facade accurately because of the low contrast and fuzzy boundaries of the obtained infrared thermal images. To address these problems, a method for extracting the contours of building facade hollowing defects using polarization thermal images based on an improved Canny algorithm has been proposed in this paper. Firstly, the principle of thermal polarization imaging was introduced for hollowing detection. Secondly, considering the shortcomings of the Canny edge detection algorithm and the features of polarization thermal images, an improved Canny edge detection algorithm is proposed, including adaptive bilateral filtering to improve noise reduction ability while ensuring defect edges are not virtualized, Laplacian sharpening and histogram equalization to achieve contour sharpening and contrast enhancement, and eight-direction gradient templates for calculating image gradients, which make interpolation with non-maximum suppression more accurate, and the Tsallis entropy threshold segmentation algorithm based on the OTSU algorithm verification makes the image contour information more complete and accurate. Finally, a long-wave infrared polarization thermal imaging experimental platform was established and validation experiments were conducted. The experimental results demonstrate that the distinct, smooth, and precise location edges of the hollowing polarization infrared thermal images can be obtained, and the average error of the detected hollowing area is about 10% using the algorithm proposed in this paper.

Molten pool image processing and quality monitoring of laser cladding process based on coaxial vision

Aiming at the problems of the traditional detection method of cladding quality in laser cladding process, which relies on experience, has low accuracy and long recognition time, a method based on the image area of molten pool is proposed to detect cladding quality. Firstly, the median filter algorithm with dynamic adjustment of filter window is used to reduce noise and the iterative threshold segmentation algorithm is used to separate the molten pool from the boundary, then the edge of the molten pool is extracted by Canny algorithm which can adaptively change the high and low thresholds and finally the area value of the molten pool is obtained according to the correspondence between the pixel value and the actual value. The relationship between three key factors (nozzle angle, scanning speed and laser power) and molten pool area is analyzed to achieve favorable cladding quality. The results show that the influence of nozzle angle on the molten pool area is anisotropic. And the molten pool area decreases monotonically with the increase of scanning speed, while the higher velocity will destroy the formation of the molten pool. In addition, with the increase of laser power, the expansion of molten pool area is intensified. At the same time, the analysis of the change of molten pool area over time can quickly and accurately detect the three defects (protrusion, depression, and slag inclusion) in the process of cladding.

Drought recognition based on feature extraction of multispectral images for the soybean canopy

Drought stress is a major environmental factor in soybean growth. Accurate and rapid detection of phenotypic traits during drought condition is important for water-saving irrigation and variety selection for the drought-resistant plant. The traditional manual measurement of phenotypes is time-consuming and error-prone, thus, an accurate and rapid drought recognition method was proposed based on feature extraction of multispectral images for the soybean canopy. First, the Suinong26 soybean variety was selected as the research object. The multispectral sensor (Parrot Drone SAS, Paris, France) was used to acquire the soybean canopies' multispectral image in four channels (NIR, RED, REG, and GRE). Second, after de-noising the original image using median filtering method, the target image of the soybean canopy in the NIR channel was extracted using a thresholding segmentation algorithm. Third, the segmented image was set as an image template to perform canopy segmentation on the rest of the channel images, the soybean canopy images were obtained in the RED, REG, and GRE channels. In addition, soybean canopies' 9 vegetation indexes (NDVI, GNDVI, NDGI, RVI, DVI, GCI, RECI, RDVI, NLI) and each channel's 7 texture features (mean, standard deviation, smoothness, third moment, information entropy, average gradient, fractal dimension) were calculated by using image fusion and processing technologies. Finally, based on the 37-dimensional features of the extracted multispectral images of the soybean canopy, a drought recognition model was established using a support vector machine (SVM) with a radial basis as the kernel function. The models' recognition accuracy reached 96.87%, and the running time was only 0.493441 s. The proposed model has improved the recognition accuracy by 8.33% on average compared with Genetic Algorithm-Backpropagation (GA-BP) neural network, radial basis neural network (RBF) and random forest (RF) method. The proposed method can provide a theoretical basis and technical support for precise regulation and scientific decision-making of breeding and cultivation management for drought-resistant soybean varieties.

Magnesium Ingot Stacking Segmentation Algorithm for Industrial Robot Based on the Correction of Image Overexposure Area.

This paper proposes an adaptive threshold segmentation algorithm for the magnesium ingot stack based on image overexposure area correction (ATSIOAC), which solves the problem of mirror reflection on the surface of magnesium alloy ingots caused by external ambient light and auxiliary light sources. Firstly, considering the brightness and chromaticity information of the mapped image, we divide the exposure probability threshold into weak exposure and strong exposure regions. Secondly, the saturation difference between the magnesium ingot region and the background region is used to obtain a mask for the magnesium ingot region to eliminate interference from the image background. Then, the RGB average of adjacent pixels in the overexposed area is used as a reference to correct the colors of the strongly exposed and weakly exposed areas, respectively. Furthermore, in order to smoothly fuse the two corrected images, pixel weighted average (WA) is applied. Finally, the magnesium ingot sorting experimental device was constructed and the corrected top surface image of the ingot pile was segmented through ATSIOAC. The experimental results show that the overexposed area detection and correction algorithm proposed in this paper can effectively correct the color information in the overexposed area, and when segmenting ingot images, complete segmentation results of the top surface of the ingot pile can be obtained, effectively improving the accuracy of magnesium alloy ingot segmentation. The segmentation algorithm achieves a segmentation accuracy of 94.38%.

Multi-Difference Image Fusion Change Detection Using a Visual Attention Model on VHR Satellite Data

For very-high-resolution (VHR) remote sensing images with complex objects and rich textural information, multi-difference image fusion has been proven as an effective method to improve the performance of change detection. However, errors are superimposed during this process and a single spectral feature cannot fully utilize the correlation between pixels, resulting in low robustness. To overcome these problems and optimize the performance of multi-difference image fusion in change detection, we propose a novel multi-difference image fusion change detection method based on a visual attention model (VA-MDCD). First, we construct difference images using change vector analysis (CVA) and spectral gradient difference (SGD). Second, we use the visual attention model to calculate multiple color, intensity and orientation features of the difference images to obtain the difference saliency images. Third, we use the wavelet transform fusion algorithm to fuse two saliency images. Finally, we execute the OTSU threshold segmentation algorithm (OTSU) to obtain the final change detection map. To validate the effectiveness of VA-MDCD on VHR images, two datasets of Jilin 1 and Beijing 2 are selected for experiments. Compared with classical methods, the proposed method has a better performance with fewer missed alarms (MA) and false alarms (FA), which proves that the method has a strong robustness and generalization ability. The F-measure of the two datasets is 0.6671 and 0.7313, respectively. In addition, the results of ablation experiments confirm that the three feature extraction modules of the model all play a positive role.

Research on nondestructive detection of pine nut quality based on terahertz imaging

ObjectivePine nuts are of great nutritional and medicinal value, but they cannot avoid such defects as mildew and insect-eating during storage. Because of their hard shells, the internal quality detection of pine nuts is a major problem for industrial sorting. For this reason, it is of great significance to carry out rapid nondestructive detection of the internal quality of pine nuts. MethodsIn this paper, a rapid and nondestructive detection of pine nuts for mildew and plumpness based on terahertz transmission imaging technology was carried out. Firstly, the terahertz transmission images of pine nut samples were acquired and the terahertz spectral signals of four different regions of interest were extracted for analysis. Secondly, in order to reduce the interference of external environment on the acquisition of terahertz spectra, the terahertz spectra were pre-processedby several methods, such as ALS, AirPLS, BEADS and SNV + Detrending, and then three qualitative discriminant models, namely, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and XGBoost integration learning, were established respectively to explore the optimal qualitative discriminant model for the detection of pine nut quality. Finally, the terahertz transmission image of pine nuts was subjected to image processing. The main feature gamut extraction combined with channel separation strategy were adopted. and then the automatic threshold segmentation algorithm was applied to perform binary threshold segmentation on the separated image, thus the plumpness of the pine nuts was calculated by calculating the ratio of the pixel points of the shell and the kernel. ResultsThe prediction set of BEADS + XGBoost model was established after data preprocessing with the optimal effect and the accuracy of 98.61%. The acquired terahertz images of pine nuts were extracted by the main feature gamut and the images of channel B were extracted by using channel separation. Finally, the automatic threshold segmentation of channel B was performed by using the maximum one-dimensional entropy, which can well realize the visual detection of the inner shell kernel of pine nuts. ConclusionTerahertz imaging technology can achieve rapid and nondestructive detection of pine mildew as well as pine nut plumpness. This study provides a new rapid and nondestructive effective method for pine nut quality detection, which can provide technical reference for other shelled nut quality detection and has significant practical value.

A novel voxel-particle energy approach to predict 3D microscopic fracture surface of porous geomaterials and fracture permeability modeling

Characterizing and predicting 3D microscopic fractures are rarely reported due to the challengeable difficulty to determine its spatial trajectories. In this study, a double threshold segmentation algorithm is proposed to obtain 3D microstructure models of porous geomaterials by X-ray computing tomography (CT) images. A novel voxel-particle energy (VPE) approach is proposed to predict 3D fracture characteristics, and fracture permeability modeling is conducted. Results show that excellent agreements are found between the experimental and numerical results, showing the accuracy of proposed VPE method. As dynamic viscosity increases, microscopic fracture permeability increases. Microscopic fracture permeability decreases with increasing input pressure, and it increases with increasing voxel scale. The proposed VPE method provides an effective tool to predict 3D cracks and its spatial trajectories, which is helpful for the deep resource and underground space development.

Study on tortuosity from 3D images of nuclear graphite grades IG-110 by Dijkstra's algorithm and fast marching algorithm

The nuclear-grade graphite is a kind of granular material, and tortuosity is a key parameter to predict its performance. However, there are few research about analyzing the tortuosity of nuclear graphite materials. In this study, two methods were employed to calculate the tortuosity of graphite grades IG-110. In the first method, the X-ray computed tomography (X-CT) images were transferred into binary ones using three different threshold segmentation algorithms separately, and then the tortuosity was calculated by the Dijkstra's algorithm. In the second method, the tortuosity was calculated by fast marching algorithm based on the grayscale images, in which some characters are intensified. The results indicate that the Dijkstra's algorithm and fast marching algorithm, which are frequently used for tortuosity in other fields, can also be applied to graphite grades IG-110. Overall, it is expected that this work may provide insight on the tortuosity analysis of similar particulate materials.

A Self-Adaptive Thresholding Approach for Automatic Water Extraction Using Sentinel-1 SAR Imagery Based on OTSU Algorithm and Distance Block

As an indispensable material for animals, plants and human beings, obtaining accurate water body information rapidly is of great significance to maintain the balance of ecosystems and ensure normal production and the life of human beings. Due to its independence of the time of day and the weather conditions, synthetic aperture radar (SAR) data have been increasingly applied in the extraction of water bodies. However, there is a great deal of speckle noise in SAR images, which seriously affect the extraction accuracy of water. At present, most of the processing methods are filtering methods, which will cause the loss of detailed information. Based on the characteristic of side-looking SAR, this paper proposed a self-adaptive thresholding approach for automatic water extraction based on an OTSU algorithm and distance block. In this method, the whole images were firstly divided into uniform image blocks through a distance layer which was produced by the distance to the orbit. Then, a self-adaptive processing was conducted for merging blocks. The OTSU algorithm was used to obtain a threshold for classification and the Jeffries–Matusita (JM) distance was calculated with the classification result. The merge processing continued until the separability of image blocks reached the maximum. Subsequently, we started from the next block to repeat the merger, and so on until all blocks were processed. Ten study areas around the world and the local Dongting Lake area were applied to test the feasibility of the proposed method. In comparison with five other global threshold segmentation algorithms such as the traditional OTSU, MOMENTS, MEAN, ISODATA and MINERROR, the proposed method obtains the highest overall accuracy (OA) and kappa coefficient (KC), while this approach also demonstrates greater robustness in the analysis of time series. The findings of this study offer an effective method to improve water detection accuracy as well as reducing the influence of speckle noise and retaining details in the image.

Deep learning combined with Balance Mixup for the detection of pine wilt disease using multispectral imagery

Pine wilt disease (PWD) is one of the most destructive infectious diseases affecting coniferous forests. This research proposes a detection method based on deep learning combined with Balance Mixup. At the beginning, regions of interest (ROIs) on multispectral images are automatically generated using normalized difference vegetation index threshold segmentation algorithm. Multispectral data of these ROIs are extracted and labeled as positive (represented by 1) or negative (represented by 0) depending on whether they contained infected trees. During training, the Balance Mixup algorithm selects two multispectral data from a batch of training data according to a certain random strategy. The two data will be mixed into a new multispectral data whose label is the maximum value of the labels (0 or 1) of the original data. The above process will be repeated many times until a new batch of data is generated. A one-dimensional convolutional neural network called pine wilt disease net (PWDNet) is trained by the mixed data and output feature vectors, which are used as input data by a logistic model to determine whether there are PWD in mixed data. By combining Balance Mixup and PWDNet, our method achieved a recall of 1.00 and a precision of 0.90 in testing set. The satisfactory results indicates that the method can provide technical support for the prevention and control of PWD.

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.

Remote-Sensing Extraction of Small Water Bodies on the Loess Plateau

The mixed pixel of low-resolution remote-sensing image makes the traditional water extraction method not effective for small water body extraction. This study takes the Loess Plateau with complex terrain as the research area and develops a multi-index fusion threshold segmentation algorithm (MFTSA) for a large-scale small water body extraction algorithm based on GEE (Google Earth Engine). MFTSA uses the AWEI (automated water extraction index), MNDWI (modified normalized difference water index), NDVI (normalized difference vegetation index) and EVI (enhanced vegetation index) for multi-index synergy to extract small water bodies. It also uses slope data generated by the SRTM (Shuttle Radar Topography Mission digital elevation model) and NIR band reflectance to eliminate suppressing high reflectivity noise and shadow noise. An MFTSA algorithm was proposed and the results showed that: (1) The overall extraction accuracy of the MFTSA algorithm on the Loess Plateau was 98.14%, and the correct extraction rate of small water bodies was 92.82%. (2) Compared with traditional water index methods and classification methods, the MFTSA algorithm could extract small water bodies with higher integrity and clearer and more accurate boundaries. (3) The MFTSA algorithm was used to extract a total of 69,900 small water bodies on the Loess Plateau, accounting for 97.63% of the total water bodies, and the area was 482.11 square kilometers, accounting for 16.50% of the total water bodies.

Polarization Orientation Method Based on Remote Sensing Image in Cloudy Weather

Autonomous navigation technology is a core technology for intelligent operation, allowing the vehicles to perform tasks without relying on external information, which effectively improves the concealability and reliability. In this paper, based on the previous research on the bionic compound eye, a multi-channel camera array with different polarization degrees was used to construct the atmospheric polarization state measurement platform. A polarization trough threshold segmentation algorithm was applied to study the distribution characteristics and characterization methods of polarization states in atmospheric remote sensing images. In the extracted polarization feature map, the tilting suggestion box was obtained based on the multi-direction window extraction network (similarity-based region proposal networks, SRPN) and the rotation of the suggestion box (Rotation Region of interests, RRoIs). Fast Region Convolutional Neural Networks (RCNN) was used to screen the suggestion boxes, and the Non-maximum suppression (NMS) method was used to select the angle, corresponding to the label of the suggestion box with the highest score, as the solar meridian azimuth in the vehicle coordinate system. The azimuth angle of the solar meridian in the atmospheric coordinate system can be calculated by the astronomical formula. Finally, the final heading angle can be obtained according to the conversion relationship between the coordinate systems. By fitting the measured data based on the least Square method, the slope K value is −1.062, RMSE (Root Mean Square Error) is 6.984, and the determination coefficient R-Square is 0.9968. Experimental results prove the effectiveness of the proposed algorithm, and this study can construct an autonomous navigation algorithm with high concealment and precision, providing a new research idea for the research of autonomous navigation technology.