Yıl: 2019 Cilt: 27 Sayı: 6 Sayfa Aralığı: 4188 - 4202 Metin Dili: İngilizce DOI: 10.3906/elk-1901-190 İndeks Tarihi: 22-05-2020

Evaluating the attributes of remote sensing image pixels for fast k-means clustering

Öz:
Clustering process is an important stage for many data mining applications. In this process, data elementsare grouped according to their similarities. One of the most known clustering algorithms is the k-means algorithm.The algorithm initially requires the number of clusters as a parameter and runs iteratively. Many remote sensing imageprocessing applications usually need the clustering stage like many image processing applications. Remote sensingimages provide more information about the environments with the development of the multispectral sensor and lasertechnologies. In the dataset used in this paper, the infrared (IR) and the digital surface maps (DSM) are also suppliedbesides the red (R), the green (G), and the blue (B) color values of the pixels. However, remote sensing images come withvery large sizes (6000 × 6000 pixels for each image in the dataset used). Clustering these large-size images using theirmultiattributes consumes too much time if it is used directly. In the literature, some studies are available to acceleratethe k-means algorithm. One of them is the normalized distance value (NDV)-based fast k-means algorithm that benefitsfrom the speed of the histogram-based approach and uses the multiattributes of the pixels. In this paper, we evaluatedthe effects of these attributes on the correctness of the clustering process with different color space transformations anddistance measurements. We give the success results as peak signal-to-noise ratio and structural similarity index valuesusing two different types of reference data (the source images and the ground-truth images) separately. Finally, we givethe results based on accuracy measurement for evaluating both the success of the clustering outputs and the reliabilityof the NDV-based measurement methods presented in this paper.
Anahtar Kelime:

Konular: Mühendislik, Elektrik ve Elektronik Bilgisayar Bilimleri, Yazılım Mühendisliği Bilgisayar Bilimleri, Sibernitik Bilgisayar Bilimleri, Bilgi Sistemleri Bilgisayar Bilimleri, Donanım ve Mimari Bilgisayar Bilimleri, Teori ve Metotlar Bilgisayar Bilimleri, Yapay Zeka
Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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APA SAĞLAM A, Baykan N (2019). Evaluating the attributes of remote sensing image pixels for fast k-means clustering. , 4188 - 4202. 10.3906/elk-1901-190
Chicago SAĞLAM Ali,Baykan Nurdan Evaluating the attributes of remote sensing image pixels for fast k-means clustering. (2019): 4188 - 4202. 10.3906/elk-1901-190
MLA SAĞLAM Ali,Baykan Nurdan Evaluating the attributes of remote sensing image pixels for fast k-means clustering. , 2019, ss.4188 - 4202. 10.3906/elk-1901-190
AMA SAĞLAM A,Baykan N Evaluating the attributes of remote sensing image pixels for fast k-means clustering. . 2019; 4188 - 4202. 10.3906/elk-1901-190
Vancouver SAĞLAM A,Baykan N Evaluating the attributes of remote sensing image pixels for fast k-means clustering. . 2019; 4188 - 4202. 10.3906/elk-1901-190
IEEE SAĞLAM A,Baykan N "Evaluating the attributes of remote sensing image pixels for fast k-means clustering." , ss.4188 - 4202, 2019. 10.3906/elk-1901-190
ISNAD SAĞLAM, Ali - Baykan, Nurdan. "Evaluating the attributes of remote sensing image pixels for fast k-means clustering". (2019), 4188-4202. https://doi.org/10.3906/elk-1901-190
APA SAĞLAM A, Baykan N (2019). Evaluating the attributes of remote sensing image pixels for fast k-means clustering. Turkish Journal of Electrical Engineering and Computer Sciences, 27(6), 4188 - 4202. 10.3906/elk-1901-190
Chicago SAĞLAM Ali,Baykan Nurdan Evaluating the attributes of remote sensing image pixels for fast k-means clustering. Turkish Journal of Electrical Engineering and Computer Sciences 27, no.6 (2019): 4188 - 4202. 10.3906/elk-1901-190
MLA SAĞLAM Ali,Baykan Nurdan Evaluating the attributes of remote sensing image pixels for fast k-means clustering. Turkish Journal of Electrical Engineering and Computer Sciences, vol.27, no.6, 2019, ss.4188 - 4202. 10.3906/elk-1901-190
AMA SAĞLAM A,Baykan N Evaluating the attributes of remote sensing image pixels for fast k-means clustering. Turkish Journal of Electrical Engineering and Computer Sciences. 2019; 27(6): 4188 - 4202. 10.3906/elk-1901-190
Vancouver SAĞLAM A,Baykan N Evaluating the attributes of remote sensing image pixels for fast k-means clustering. Turkish Journal of Electrical Engineering and Computer Sciences. 2019; 27(6): 4188 - 4202. 10.3906/elk-1901-190
IEEE SAĞLAM A,Baykan N "Evaluating the attributes of remote sensing image pixels for fast k-means clustering." Turkish Journal of Electrical Engineering and Computer Sciences, 27, ss.4188 - 4202, 2019. 10.3906/elk-1901-190
ISNAD SAĞLAM, Ali - Baykan, Nurdan. "Evaluating the attributes of remote sensing image pixels for fast k-means clustering". Turkish Journal of Electrical Engineering and Computer Sciences 27/6 (2019), 4188-4202. https://doi.org/10.3906/elk-1901-190