Semi-Automatic Extraction and Mapping of Farmlands based on High-Resolution Remote Sensing Images

Extraction of agricultural parcels from high-resolution satellite imagery is an important task in precision agriculture. Here, we present a semi-automatic approach for agricultural parcel detection that achieves high accuracy and efficiency. Unlike the techniques presented in previous literatures, this method is pixel based, and it exploits the properties of a spectral angle mapper (SAM) to develop customized operators to accurately derive the parcels. The main steps of the method are sample selection, textural analysis, spectral homogenization, SAM, thresholding, and region growth. We have systematically evaluated the algorithm proposed on a variety of images from Gaofen-1 wide field of view (GF-1 WFV), Resource 1-02C (ZY1-02C), and Gaofen-2 (GF-2) to aerial image; the accuracies are 99.09% of GF-1 WFV, 84.42% of ZY1-02C, 96.51% and 92.18% of GF-2, and close to 100% of aerial image; these results demonstrated its accuracy and robustness.

Noise-immune image blur detection via sequency spectrum truncation

Blur detection is aimed to differentiate the blurry and sharp regions from a given image. This task has attracted much attention in recent years due to its importance in computer vision with the integration of image processing and artificial intelligence. However, blur detection still suffers from problems such as the oversensitivity to image noise and the difficulty in cost–benefit balance. To deal with these issues, we propose an accurate and efficient blur detection method, which is concise in architecture and robust against noise. First, we develop a sequency spectrum-based blur metric to estimate the blurriness of each pixel by integrating a re-blur scheme and the Walsh transform. Meanwhile, to eliminate the noise interference, we propose an adaptive sequency spectrum truncation strategy by which we can obtain an accurate blur map even in noise-polluted cases. Finally, a multi-scale fusion segmentation framework is designed to extract the blur region based on the clustering-guided region growth. Experimental results on benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance and the best balance between cost and benefit. It offers an average F1 score of 0.887, MAE of 0.101, detecting time of 0.7 s, and training time of 0.5 s. Especially for noise-polluted blurry images, the proposed method achieves the F1 score of 0.887 and MAE of 0.101, which significantly surpasses other competitive approaches. Our method yields a cost–benefit advantage and noise immunity that has great application prospect in complex sensing environment.

Research on the Region-Growing and Segmentation Technology of Micro-Particle Microscopic Images Based on Color Features

Silkworm microparticle disease is a legal quarantine standard in the detection of silkworm disease all over the world. The current common detection method, the Pasteur manual microscopy method, has a low detection efficiency all over the world. The low efficiency of the current Pasteur manual microscopy detection method makes the application of machine vision technology to detect microparticle spores an important technology to advance silkworm disease research. For the problems of the low contrast, different illumination conditions and complex image background of microscopic images of the ellipsoidal symmetrical shape of silkworm microparticle spores collected in the detection solution, a region growth segmentation method based on microparticle color and grayscale information is proposed. In this method, the fuzzy contrast enhancement algorithm is used to enhance the color information of micro-particles and improve the discrimination between the micro-particles and background. In the HSV color space with stable color, the color information of micro-particles is extracted as seed points to eliminate the influence of light and reduce the interference of impurities to locate the distribution area of micro-particles accurately. Combined with the neighborhood gamma transformation, the highlight feature of the micro-particle target in the grayscale image is enhanced for region growing. Mea6nwhile, the accurate and complete micro-particle target is segmented from the complex background, which reduces the background impurity segmentation caused by a single feature in the complex background. In order to evaluate the segmentation performance, we calculate the IOU of the microparticle sample image segmented by this method with its corresponding true value image, and the experiments show that the combination of color and grayscale features using the region growth technique can accurately and completely segment the microparticle target in complex backgrounds with a segmentation accuracy IOU as high as 83.1%.

Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis

In view of the intelligent demand of tennis line examination, this paper performs a systematic analysis on the intelligent recognition of tennis line examination. Then, a tennis line recognition method based on machine vision is proposed. In this paper, the color region of the image recognition region is divided based on the region growth, and the rough estimation of the court boundary is realized. In order to achieve the effect of camera calibration, a fast camera calibration method which can be used for a variety of court types is proposed. On the basis of camera calibration, a tennis line examination and segmentation system based on machine vision analysis is constructed, and the experimental results are verified by design experiments. The results show that the machine vision analysis-based intelligent segmentation system of tennis line examination has high recognition accuracy and can meet the actual needs of tennis line examination.

Maturation of the anterior petrous apex: surgical relevance for performance of the middle fossa transpetrosal approach in pediatric patients

OBJECTIVE The middle fossa transpetrosal approach to the petroclival and posterior cavernous sinus regions includes removal of the anterior petrous apex (APA), an area well studied in adults but not in children. To this end, the authors performed a morphometric analysis of the APA region during pediatric maturation. METHODS Measurements of the distance from the clivus to the internal auditory canal (IAC; C-IAC), the distance of the petrous segment of the internal carotid artery (petrous carotid; PC) to the mesial petrous bone (MPB; PC-MPB), the distance of the PC to the mesial petrous apex (MPA; PC-MPA), and the IAC depth from the middle fossa floor (IAC-D) were made on thin-cut CT scans from 60 patients (distributed across ages 0–3, 4–7, 8–11, 12–15, 16–18, and > 18 years). The APA volume was calculated as a cylinder using C-IAC (length) and PC-MPB (diameter). APA pneumatization was noted. Data were analyzed by laterality, sex, and age. RESULTS APA parameters did not differ by laterality or sex. APA pneumatization was seen on 20 of 60 scans (33.3%) in patients ≥ 4 years. The majority of the APA region growth occurred by ages 8–11 years, with PC-MPA and PC-MPB increasing 15.9% (from 9.4 to 10.9 mm, p = 0.08) and 23.5% (from 8.9 to 11.0 mm, p < 0.01) between ages 0–3 and 8–11 years, and C-IAC increasing 20.7% (from 13.0 to 15.7 mm, p < 0.01) between ages 0–3 and 4–7 years. APA volume increased 79.6% from ages 0–3 to 8–11 years (from 834.3 to 1499.2 mm3, p < 0.01). None of these parameters displayed further significant growth. Finally, IAC-D increased 51.1% (from 4.3 to 6.5 mm, p < 0.01) between ages 0–3 and adult, without significant differences between successive age groups. CONCLUSIONS APA development is largely complete by the ages of 8–11 years. Knowledge of APA growth patterns may aid approach selection and APA removal in pediatric patients.

STUDY OF CONDITIONS OF GULLY FORMATION IN MOUNTAIN REGIONS OF SOUTH-EAST KAZAKHSTAN WITH USE OF GIS-TECHNOLOGIES

In the article the results of field and laboratory researches of gully erosion in the mountains of Zhetysu Alatau of south-east Kazakhstan are considered. Mountain ridge Malaisary was chosen for study of gully erosion. Malaisary ridge is the western ridge of Zhetysu Alatau mountains in the south-east Kazakhstan. Foothills and plain territories of southeast Kazakhstan are characterized by favorable conditions for the development of erosion processes. There was conducted stationary monitoring (yearly in October from 2013 to 2018) of gully erosion development on the Malaisary ridge from 2013 to 2018. Most of gullies of studied ridge show development in the top part and extend in the width mainly due to fluvial processes. There were studied the natural-anthropogenic factors influencing development of gully erosion; the morphometric characteristic received using the modern devices and satellite images are provided. The determined factors of development of gullies on Malaisary ridge are mechanical substratum composition, atmospheric precipitation (spring runoff, summer rainfalls), steepness and length of the slopes. The received materials allow concluding that gully erosion is the most active factor of the ridge relief transformation. Intensive gully erosion development increases ecological tension of natural-anthropogenic environment in the region. Growth of gully net and its active development deteriorates quality of agricultural lands and create threat to road objects and residential area infrastructure in the region.

An Improved Image Segmentation Algorithm CT Superpixel Grid Using Active Contour

The traditional CT image segmentation algorithm is easy to ignore image contour initialization, which leads to the problem of long time consuming and low accuracy. A superpixel mesh CT image improved segmentation algorithm using active contour was proposed. CT image superpixel gridding was carried out first; secondly, on the basis of gridding, the region growth criterion was improved by superpixel processing, the region growth graph was established, the image edge salient graph was calculated based on the growth graph, and the target edge was obtained as the initial contour; finally, the Mumford-Shah model in the active contour model was improved; the energy functional was constructed based on the improved model and transformed into the symbol distance function. The results show that the proposed algorithm takes less time to mesh superpixels, the accuracy of image edge calculation is high, the correct classification coefficient is as high as 0.9, and the accuracy of CT image segmentation is always higher than 90%, which has superiority.

Crack Extraction for Polycrystalline Solar Panels

Crack extraction of solar panels has become a research focus in recent years. The cracks are small and hidden. In addition, there are particles of irregular shape and size on the surface of the polycrystalline solar panel, whose reflection position and direction are random. Therefore, there is a complex and uneven texture background on the solar panel image, which makes the crack extraction more difficult. In this paper, a crack extraction method combining image texture and morphological features is proposed. Firstly, the background texture and multi-scale details are suppressed by the linear filter and the Laplace pyramid decomposition method. Secondly, the edge can be extracted based on the modulus maximum method of the wavelet transform. Finally, cracks were extracted by using the improved Fuzzy C-means (FCM) clustering combining the morphological and texture features of the cracks. To make the extraction results more accurate and reasonable, an improved region growth algorithm is proposed to optimize the extraction results. All of the above research is closely centered on the accuracy and stability requirements of the solar cell crack detection, which is also the key point of this paper. The experimental results show that various improved or innovative algorithms proposed in this paper can accurately extract the position of cracks and obtain better extraction results. The detection results have good stability and can be faithful to the actual situation, which will promote the application of solar cells in more fields.