Automatic Satellite Image Enhancement Approach to Extract the Features Based on a Morphological Building Index

The satellite imagery and its importance are growing in the modern digital image processing. The implementation of the enhancement techniques in the satellite image processing is commonly observed practice and a new investigation is carried out in this study to investigate the morphological characteristics and the sensitivity of contrast-based textural measurements to derive the high resolution satellite imagery. The investigation is done after the deployment of the diverse image enhancement techniques to get the statistical analysis and the idea of the proposed study is to build an application framework for the detection of the built-up/non built in reluctant manner. The earlier method utilizes a low-resolution reference layer which works based on the supervised learning and the approach is successful in reducing the uncertainty and has ability to optimize the quality of the reference layer. The goal is to achieve the best computation of the contrast-based textural/morphological features by adjusting the image histogram in a suitable manner. An optimized case study is presented in this work to test the image enhancement operations such as linear and de-correlation stretching. The performance of the image enhancement operations is tested through ROC and compared with the earlier methods the proposed work achieves better performance and high efficiency.