Hyper Spectral Image Analysisinagriculture - Dr. P. SIVAKUMAR, M. E., Ph.D,; G, Subashree,; V.S halini,; S. Hemadharshini, (The Bioscan, 2026)
Satellite image classification process involves grouping the image pixel values into meaningful categories. Several satellite image classification methods and techniques are available. In existing k-means clusteringtechniqueisusedforclusteringthesatellitedata, with this method not able to cluster accurately all the classes.Inourproposed methodself-organising mapsasa clusteringtechniqueisused.Self-organizingmapslearnto cluster data based on similarity, topology, with a preference of assigning the same number of instances to each class. Self-organizing maps are used both to cluster data and to reduce the dimensionality of data. They are inspired by the sensory and motor mappings in the mammal brain, which also appear to automatically organizing information topologically. CNN Classifiers meld results from many weak learners into one high- quality ensemble model. Our proposed technique is K- Medoid clustering with CNN algorithm for classification ofsatellitedataintowater,Agriculture,Barrenland,Green Land. The proposed method of self-organising map clusteringandCNNclassifierisgivenbestresultcompared to existing ones.