Satellite images are collected from different sensors. The image pixel values and the objects located in the pixels were different in the reference and the target image. The reference image has mainly intensity variations and object location difference. The registration process transforms the target image as the reference image which helps in remote sensing process. The input reference image and sensed image are preprocessed by applying gaussian filter. The feature points are extracted from the preprocessed image using SIFT algorithm. The feature points refers to the location of the image pixels present in the images that have the same coordinate vales in different orientations. The extracted feature points were the same for the different images. Correspondence is established between features detected in the sensed and the reference image. The correspondence process refers to the matching of the pixels. The reference and the clustered regions were clustered. The clustering process is employed in both target and the reference image. The clustered portions in both the reference and the target portions which are in common is were identified. The identification of the correct matching pixels will give the pixels that are need to be modified in the sensed image. The Sensed image is transformed similar to the target image by means piecewise transformation of the pixels from the sensed image to the reference image is employed. Performance is analysed by measuring the accuracy and the error rate. The error rate gives the rate of number of pixels modified in the sensed image corresponding to the target image. The error rate is the rate of number of pixels modified in the sensed images corresponding to the target image. The proposed method has overcome difficulties in the existing complex HSTC and other methods and the accuracy is also improves in the proposed process.