Land cover classification: a comparative analysis of clustering techniques using Sentinel-2 data

Sharma Mayuri; Kumar Chandan Jyoti; Deka Aniruddha

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Land cover classification: a comparative analysis of clustering techniques using Sentinel-2 data

机译：土地覆盖分类:比较分析使用Sentinel-2数据集群技术

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Automated land use land cover (LULC) classification may provide an authentic database of information to the policy makers in various fields like agro-climatic zone planning, waste land inventory projects, vegetation cover analysis, etc. It has a tremendous potential to contribute towards effective policy formulation. This article considers various unsupervised machine learning techniques: K-means, FCM, SOM, meanshift, GMM and HMM for land cover (LC) classification of Sentinel-2 data in the context of Assam, India. These models showed good performance in distinguishing vegetation cover area from the rest of the regions. K-means and FCM showed better performance in comparison to all the considered models. Meanshift correctly classified the continuous stretch of vegetation in study area 2. However, it misclassified between the vegetation and fallow land in study area 1. Similarly, in identifying built-up areas for study area 2 SOM covered the maximum but misclassified severely with other classes in study area 1.

机译：自动土地利用土地覆盖(LULC)分类可能会提供一个真正的数据库决策者在不同的信息农业气候区规划、浪费库存项目,土地植被分析等。有助于实现有效的政策制定。本文认为不同的无监督机器学习技术:k - means, FCM, SOM,土地覆盖meanshift GMM,嗯(LC)分类Sentinel-2上下文中的数据印度的阿萨姆邦。在区分植被的表现区域的其他地区。FCM相比,更好的性能所有考虑的模型。连续的植被分类在研究区域2中。在研究植被和休闲之间的土地区域1。研究区2 SOM最大但覆盖与其他类分类错误的严重研究区1。

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来源
《International Journal of Sustainable Agricultural Management and Informatics》 |2021年第4期|321-342|共22页
作者
Sharma Mayuri; Kumar Chandan Jyoti; Deka Aniruddha;
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作者单位

Assam Royal Global Univ;

Cotton Univ;

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原文格式 PDF
正文语种英语
中图分类农业科学;
关键词
Hidden Markov models; Machine learning algorithms; land coverLand Use/Land Cover Classification;

机译：隐马尔可夫模型;机器学习算法;土地覆盖土地利用/土地覆盖分类;

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2. Integrating TM and Ancillary Geographical Data with Classification Trees for Land Cover Classification of Marsh Area [J] . NA Xiaodong, ZHANG Shuqing, ZHANG Huaiqing, 中国地理科学：英文版 . 2009,第2期
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4. A comparative evaluation of state-of-the-art ensemble learning algorithms for land cover classification using WorldView-2, Sentinel-2 and ROSIS imagery [J] . Colkesen Ismail, Ozturk Muhammed Yusuf Arabian journal of geosciences . 2022,第10期

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6. A Review of Unsupervised Band Selection Techniques: Land Cover Classification for Hyperspectral Earth Observation Data [J] . Ram Narayan Patro, Subhashree Subudhi, Pradyut Kumar BiswalFabio Dell’acqua Geoscience and remote sensing . 2021,第3期

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7. Land Use/Land Cover Change Geo-Informative Tupu of Nujiang River in Northwest Yunnan Province [C] . WANG Jin-liang, YANG Yue-yuan, HUANG You-ju, 第16届国际地理信息科学与技术大会（16th International Conference on GeoInformatics and the Joint Conference）论文集 . 2008

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机译：Influence of Discharge Patterns and Land Use Land Cover of a Watershed on the Water Quality of a Reservoir =流域出水方式和土地利用覆盖对水库水质的影响
9. Remote sensing-based artificial surface cover classification in Asia and spatial pattern analysis [O] . Kuang W. H., Chen, L. J., Liu, J. Y., 2016

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Land cover classification: a comparative analysis of clustering techniques using Sentinel-2 data

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