scholarly journals Extraction of Yardang Characteristics Using Object-Based Image Analysis and Canny Edge Detection Methods

2020 ◽  
Vol 12 (4) ◽  
pp. 726 ◽  
Author(s):  
Weitao Yuan ◽  
Wangle Zhang ◽  
Zhongping Lai ◽  
Jingxiong Zhang
Keyword(s):  
Image Analysis ◽  
Edge Detection ◽  
Spatial Resolution ◽  
Google Earth ◽  
Kappa Coefficient ◽  
Detection Methods ◽  
Canny Edge Detection ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Canny Edge

Parameters of geomorphological characteristics are critical for research on yardangs. However, methods which are low-cost, accurate, and automatic or semi-automatic for extracting these parameters are limited. We present here semi-automatic techniques for this purpose. They are object-based image analysis (OBIA) and Canny edge detection (CED), using free, very high spatial resolution images from Google Earth. We chose yardang fields in Dunhuang of west China to test the methods. Our results showed that the extractions registered an overall accuracy of 92.26% with a Kappa coefficient of agreement of 0.82 at a segmentation scale of 52 using the OBIA method, and the exaction of yardangs had the highest accuracy at medium segmentation scales (138, 145). Using CED, we resampled the experimental image subset to a series of lower spatial resolutions for eliminating noise. The total length of yardang boundaries showed a logarithmically decreasing (R2 = 0.904) trend with decreasing spatial resolution, and there was also a linear relationship between yardang median widths and spatial resolutions (R2 = 0.95). Despite the difficulty of identifying shadows, the CED method achieved an overall accuracy of 89.23% with a kappa coefficient of agreement of 0.72, similar to that of the OBIA method at medium segmentation scale (138).

PEMBUATAN PETA TUTUPAN LAHAN BERDASARKAN HASIL KLASIFIKASI DENGAN METODE OBJECT BASED IMAGE ANALYSIS (OBIA) PADA FOTO UDARA (Studi Kasus di Desa Wates, Kecamatan Wates, Kabupaten Kulon Progo)

2021 ◽  
pp. 355
Author(s):  
Galuh Qori’ah Fahmah Suratno ◽  
Anindya Sricandra Prasidya
Keyword(s):  
Image Analysis ◽  
Kappa Coefficient ◽  
Spectral Difference ◽  
Object Based Image Analysis ◽  
Object Based

Manusia berperan penting dalam adanya alih fungsi lahan. Dengan adanya alih fungsi lahan dibutuhkan media yang bisa menampilkan tutupan lahan suatu daerah, yaitu peta tutupan lahan. Peta tutupan lahan dibuat dari data ortofoto dengan metode klasifikasi Object Based Image Analysis (OBIA). Metode segmentasi yang digunakan adalah multiresolution dan spectral difference. Kelas tutupan lahan yang mendominasi Desa Wates adalah vegetasi sebesar 31,49% dengan luas 1.296.311,80 m2, pemukiman sebesar 30,03% dengan luas 1.236.325,31 m2, sawah sebesar 25,16% dengan luas 1.035.923,43 m2, jalan sebesar 6,19% dengan luas 254.774,38 m2, lahan terbuka sebesar 3,55% dengan luas 146.090,24 m2, sungai sebesar 2% dengan luas 82.387,70 m2, dan rel kereta api sebesar 1,59% dengan luas 65.331,42 m2. Dari hasil perhitungan, didapatkan nilai overall accuracy sebesar sebesar 74,2857% dan nilai kappa coefficient sebesar 0,7. Nilai tersebut tidak memenuhi syarat batas nilai minimum sebesar 85% karena perbedaan spasial dan temporal media uji yang tidak sebanding.

scholarly journals The Impact of the Implementation of Edge Detection Methods on the Accuracy of Automatic Voltage Reading

2017 ◽  
Vol 17 (2) ◽  
pp. 93-99 ◽  
Author(s):  
Kamil Sidor ◽  
Anna Szlachta
Keyword(s):  
Image Analysis ◽  
Edge Detection ◽  
Detection Methods ◽  
Reading Accuracy ◽  
Vision Development ◽  
Sobel Operator ◽  
Canny Edge ◽  
Automatic Reading ◽  
Edge Detection Method ◽  
The Impact

AbstractThe article presents the impact of the edge detection method in the image analysis on the reading accuracy of the measured value. In order to ensure the automatic reading of the measured value by an analog meter, a standard webcam and the LabVIEW programme were applied. NI Vision Development tools were used. The Hough transform was used to detect the indicator. The programme output was compared during the application of several methods of edge detection. Those included: the Prewitt operator, the Roberts cross, the Sobel operator and the Canny edge detector. The image analysis was made for an analog meter indicator with the above-mentioned methods, and the results of that analysis were compared with each other and presented.

Comparison between mathematical morphology and Canny Edge Detection methods for image post processing techniques in segmenting microcalcification

2018 ◽  
Author(s):  
Aminah Abdul Malek ◽  
Ummu Mardhiah Abdul Jalil ◽  
Dayangku Nur Faizah Pg Mohamad ◽  
Nurul Ain Muhamad ◽  
Sharifah Syafiyah Syed Hashim
Keyword(s):  
Edge Detection ◽  
Mathematical Morphology ◽  
Detection Methods ◽  
Post Processing ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
Processing Techniques

scholarly journals Implementing canny edge detection algorithm for noisy image

2020 ◽  
Vol 9 (4) ◽  
pp. 1404-1410
Author(s):  
Ehsan Akbari Sekehravani ◽  
Eduard Babulak ◽  
Mehdi Masoodi
Keyword(s):  
Edge Detection ◽  
Median Filter ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Canny Edge Detection ◽  
Edge Detection Algorithm ◽  
Canny Edge ◽  
Significant Stage ◽  
Salt And Pepper ◽  
Canny Edge Detection Algorithm

Edge detection is a significant stage in different image processing operations like pattern recognition, feature extraction, and computer vision. Although the Canny edge detection algorithm exhibits high precision is computationally more complex contrasted to other edge detection methods. Due to the traditional Canny algorithm uses the Gaussian filter, which gives the edge detail represents blurry also its effect in filtering salt-and-pepper noise is not good. In order to resolve this problem, we utilized the median filter to maintain the details of the image and eliminate the noise. This paper presents implementing and enhance the accuracy of Canny edge detection for noisy images. Results present that this proposed method can definitely overcome noise disorders, preserve the edge useful data, and likewise enhance the edge detection precision.

scholarly journals Processing of Drone’s Digital Image for Determining Border of Rice Fields with Edge Detection Method

2019 ◽  
Vol 2 (2) ◽  
pp. 139-144
Author(s):  
Suhardiman Diman ◽  
Zahir Zainuddin ◽  
Salama Manjang
Keyword(s):  
Edge Detection ◽  
Rice Field ◽  
Detection Method ◽  
Computing Time ◽  
Threshold Value ◽  
Error Rates ◽  
Detection Methods ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
Edge Detection Method

Edge detection was the basic thing used in most image processing applications to get information from the image frame as a beginning for extracting the features of the segmentation object that will be detected. Nowadays, many edge detection methods create doubts in choosing the right edge detection method and according to image conditions. Based on the problems, a study was conducted to compare the performance of edge detection using methods of canny, Sobel and laplacian by using object of rice field. The program was created by using the Python programming language on OpenCV.  The result of the study on one image test that the Canny method produces thin and smooth edges and did not omit the important information on the image while it has required a lot of computing time. Classification is generally started from the data acquisition process; pre-processing and post-processing. Canny edge detection can detect actual edges with minimum error rates and produce optimal image edges. The threshold value obtained from the Canny method was the best and optimal threshold value for each method. The result of a test by comparing the three methods showed that the Canny edge detection method gives better results in determining the rice field boundary, which was 90% compared to Sobel 87% and laplacian 89%.

scholarly journals IMPLEMENTASI METODE DETEKSI TEPI CANNY PADA OBJEK SEBAGAI MODEL KEAMANAN APLIKASI PADA SMARTPHONE ANDROID

Petir ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 16-20
Author(s):  
Redaksi Tim Jurnal
Keyword(s):  
Edge Detection ◽  
Error Rate ◽  
Detection Methods ◽  
Canny Edge Detection ◽  
Minimum Error ◽  
Detection Of Objects ◽  
The Face ◽  
Canny Edge ◽  
Data Objects ◽  
Shape And Size

The development of technology push security system applications on android smartphone to develop one of its features that is detection of the object. The detection of Objects is a technology that allows us to identify or verify an object through a digital image by matching the texture of the object with the curve of the data objects stored in the database. For example, to match the curve of the face such as the nose, eyes and chin. There are several methods to support the work of object detection among which edge detection. Edge detection can represent the objects contained in the image of the shape and size as well as information about the texture of an object. the best method of edge detection is canny edge detection which has the minimum error rate compared with other edge detection methods. Canny edge detection will generate the image that has been processed into a new image. The new image will be stored on a database that will be matched to the image of a new object that is used as the opening applications on android smartphone.

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