Open Access

ARTICLE

Object Detection in Remote Sensing Images Using Picture Fuzzy Clustering and MapReduce

by Tran Manh Tuan^*, Tran Thi Ngan, Nguyen Tu Trung

Faculty of Computer Science and Engineering, Thuyloi University, 175 Tay Son, Dong Da, Hanoi, 010000, Vietnam

* Corresponding Author: Tran Manh Tuan. Email:

Computer Systems Science and Engineering 2022, 43(3), 1241-1253. https://doi.org/10.32604/csse.2022.024265

Received 11 October 2021; Accepted 07 December 2021; Issue published 09 May 2022

Abstract

In image processing, one of the most important steps is image segmentation. The objects in remote sensing images often have to be detected in order to perform next steps in image processing. Remote sensing images usually have large size and various spatial resolutions. Thus, detecting objects in remote sensing images is very complicated. In this paper, we develop a model to detect objects in remote sensing images based on the combination of picture fuzzy clustering and MapReduce method (denoted as MPFC). Firstly, picture fuzzy clustering is applied to segment the input images. Then, MapReduce is used to reduce the runtime with the guarantee of quality. To convert data for MapReduce processing, two new procedures are introduced, including Map_PFC and Reduce_PFC. The formal representation and details of two these procedures are presented in this paper. The experiments on satellite image and remote sensing image datasets are given to evaluate proposed model. Validity indices and time consuming are used to compare proposed model to picture fuzzy clustering model. The values of validity indices show that picture fuzzy clustering integrated to MapReduce gets better quality of segmentation than using picture fuzzy clustering only. Moreover, on two selected image datasets, the run time of MPFC model is much less than that of picture fuzzy clustering.

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Keywords

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