Nearest Clustering Algorithm for Satellite Image Classification in Remote Sensing Applications
نویسندگان
چکیده
Classification of satellite images plays a vital role in remote sensing applications. Numerous algorithms have been developed and tested to classify a satellite image. The main purpose of these algorithms is to lessen the human efforts and errors in minimum time. Classification is performed on satellite images for various purposes. This paper presents a framework to classify a satellite image based on Nearest Clustering algorithm. This paper discusses the Nearest Clustering Algorithm in detail. Nearest Clustering algorithm is a supervised image classification algorithm which works using training dataset. It is a good algorithm having non parametric in nature. The algorithm is applied on testing dataset to get confusion matrix and also applied on satellite images to generate thematic map as output. The accuracy assessment has been done using confusion matrix, kappa coefficient and domain expert interpretations of images. Keywords—Nearest Clustering Algorithm, Image Classification, Image Processing, Confusion Matrix, Satellite Image, Training Dataset
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