Abstract—Now a days unsupervised image classification and segmentation increasingly popular. Existing information system classification tools used the same method for years. These basic classification methods do not provide satisfactory results when it applied on wide database of images. This paper describes the implementation of two algorithms, namely Back Propagation Algorithm of ANN and K-Means Algorithm on wide database of images. It provides the tool for segmentation and classification of remote sensing images. This classified image is given to K-Means Algorithm and Back Propagation Algorithm of ANN to calculate the density count. The density count is stored in database for future reference and for other applications. This tool also has the capability to show the comparison of the results of both the algorithms. High resolution basically means that an image is reproduced with a high level of detail. Usually it is referring to an image that is of very high quality, where there is a lot of detail.
Index Terms—ANN: Artificial Neural Network
P. Sathya is with II- ME (Computer Science and Engineering) Vivekanandha College of Engineering for Woman Tiruchencode, Tamilnadu, India,(e-mai: sathyavenkat81@yahoo.com)
L. Malathi is with Lecturer (Computer Science and Engineering) Vivekanandha College of Engineering for Woman Tiruchencode, Tamilnadu, India
Cite: P. Sathya and L. Malathi, "Classification and Segmentation in Satellite Imagery Using Back Propagation Algorithm of ANN and K-Means Algorithm," International Journal of Machine Learning and Computing vol. 1, no. 4, pp. 422-426, 2011.