A Comparative
Study of Unsupervised Machine Learning Algorithms for Recognition of Change in Vegetation,
Water and Built up Land Areas
Kumar Santosh, Singh Pitam, Das Bharti, Gaur Monika and Singh Priyamvada
Disaster Advances; Vol. 18(4); 1-8;
doi: https://doi.org/10.25303/184da01008; (2025)
Abstract
Change detection is a trendy and versatile research topic in the area of application
of remote sensing that includes disaster assessment, forest monitoring, urban sprawl
and many more. In a growing era, it has become necessary to introduce more and more
machines to the industry for better efficiency and accuracy. Each and every task
of image processing is tedious and needs a lot of concentration for better accuracy.
In remote sensing while finding the Land cover change detection, generally manual
process is applied which consumes too much time and unworthy effort. Therefore,
here arises the need of automatic change detection which originates different algorithms
for image analysis. In this study we have tried to analyze the effectiveness of
two type of unsupervised machine learning algorithm and to detect the change in
major classified classes in satellite imagery.
The image has been classified in three major classes of vegetation, Built up and
water bodies. A comparative study of unsupervised machine learning algorithms has
been carried out. Landsat satellite imageries of Allahabad have been used to fulfill
the required objectives. Indices majorly NDWI, MSAVI2 and NDBI have been calculated
for dominant classes. Two different algorithm K-means and FCM based on machine learning
concept of partition and fuzzy have been used. Different types of Land cover (vegetation,
Built up and water) have been identified while implementing in MATLAB. The percentage
change has been observed and compared with finding decreased percentage trend of
vegetation and water while increased percentage of Built up class.
