Multi-model fusion based satellite image classification using versatile unsupervised vector zone (VUVZ) fusion and intensive pragmatic blossoms (IPB) technique

Abstract

To get the result if satellite image classification in precisely the image fusion method is widely used. The satellite images at different spectral and spatial resolutions with the guide of image handling strategies can improve the idea of data. For the most part image fusion is important to expel the spatial data from two images of different spatial, spectral and worldly images of a comparable zone. A task of image examination, for instance, image order on fused images gives better results in contrast with one of a kind data. In this work versatile unsupervised vector zone (VUVZ) method have been used for image fusion. The resultant images have been classified using the supervised classification with Intensive Pragmatic Blossoms (IPB) rule for information extraction and comparison between them regarding their accuracy. In This work describes a case study of micro seepage signals detection in the land, using multi-sensor satellite time series based on linear iterative segmentation (LIS) and Intensive Pragmatic Blossoms (IPB). Results show that the spectral anomalies identified from a satellite are closely correlated to the known oilfields and that the micro seepage maps can produce new high-quality data to reduce exploration risk. The simulation work classification accuracy of 94.12% which is much better than past outcomes in this engaged field of research. The execution of the usage is analyzed, an examination is likewise influenced concerning the clustering precision, time complexity, and false proportion are introduced.