Satellite images often require segmentation in the presence of uncertainly which caused due to factors like environmental condition, poor resolution and poor illumination. Image processing applications depends on the quality of segmentation. This paper proposes a novel methodology namely “Satellite Image Segmentation using Energetic Self Organizing Map” (SIS-ESOM) method. This method can be used to improve the accuracy level of the satellite image segmentation. This segmentation method is also tolerable against noises in satellite images. This paper describes the implementation of two novel algorithms, namely Dynamic Adaptive Threshold based Background Optimization (DATBO) method and Energetic SOM (ESOM). The input image is undergone to fuzzy based noise Removal and DATBO image enhancement method. The optimum training samples of Energetic SOM are gathered by reduction of training vectors using Fuzzy C Means (FCM).The computed weight values of SOM are converted into transformed values using Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). This DWT and DCT transformed weight values are hold much energy. The SOM testing process is applied in an energetic way using DWT & DCT transforms and a new square root(√?) based similarity measurement method. The new SOM method is called Energetic SOM because it uses the energy transforms such as DWT and DCT. The segmented image obtained from this ESOM is further refined to get fine segmentation. Good segmentation performance can be possible in satellite images with higher PSNR.