Most Suitable SNIC Parameters and Classifier Algorithm for Object-Based Classification of Rice Crop Area using Remote Sensing Images in Google Earth Engine

W Khamnoi; S Homhuan  lowast; C Suwanprasit; Shahnawaz

doi:10.53989/bu.ga.v13i2.57

Article

VIEWS 41
PDF 15

Geographical analysis

DOI: 10.53989/bu.ga.v13i2.57

Year: 2024, Volume: 13, Issue: 2, Pages: 71-78

Original Article

Most Suitable SNIC Parameters and Classifier Algorithm for Object-Based Classification of Rice Crop Area using Remote Sensing Images in Google Earth Engine

W Khamnoi¹, S Homhuan^1∗, C Suwanprasit¹, Shahnawaz²

¹ Department of Geography, Faculty of Social Sciences, Chiang Mai University, 50200, Thailand
² Department of Geoinformatics–Z_GIS, University of Salzburg, 5020, Salzburg, Austria

*Corresponding Author
Email: [email protected]

Received Date:03 September 2024, Accepted Date:24 November 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This study aims at identifying rice crop area in Chae Chang and Buak Kang subdistricts, of San Kamphaeng District, Chiang Mai Province, Thailand through Object Based Image Analysis approach. It investigates the best fit of three elements: (1) most suitable band combination of Sentinel-1 and Sentinel-2 satellite images; (2) optimum parameters for image segmentation; and (3) best performing classifier algorithm in Google Earth Engine [GEE]. The six bands of Sentinel-1 [VV & VH from ascending and descending orbits] and Sentinel-2 [B2, B3, B4 & B8] were used in different combinations. Simple Non-Iterative Clustering [SNIC] method was applied for image segmentation. Three algorithms, Support Vector Machine [SVM], Gradient Boosting Trees [GBT] and Random Forest [RF] were tested for classification and validation. The study analyzed the outcomes of five different band combinations, thirty sets of SNIC parameters - including Compactness [Co], Connectivity [Cn], Neighborhood Size [Ns], Segment Size [Ss] - and three classifier algorithms in GEE. The highest overall accuracy of 97% and a Kappa coefficient of 0.94 was achieved by using all the six bands of the images of the two satellites [Ascending orbit of Sentinel-1] with the SNIC parameter set including Co=0.1, Cn=8, Ns=10, and Ss=5 in RF classifier algorithm. The study reveals that higher Co levels lead to more circle like segments thus unsuitable for rectangular agricultural fields. The results validated against Regions of Interest [ROI] indicate that the optimized SNIC parameters effectively delineated the big and small rice fields covering 34.64 km² [73%] of the total 47.23 km² area of the two sub-districts. The study offers a pathway for improving classification accuracy in various contexts, particularly in rice area classification.

Keywords

Object-Based Image Analysis, Simple Non-Iterative Clustering, Sentinel-1 and Sentinel-2 images, Support Vector Machine Classifier, Gradient Boosting Trees Classifier, Random Forest Classifier

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Copyright

© 2024 Khamnoi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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