Remote Sensing II

After the conclusion of this course, students should be able to: 1. discuss fundamentals of digital image processing; 2. carry out pre and post processing exercises on images; 3. demonstrate the understanding of image analysis and remote sensing applications; and 4. integrate remote sensing and GIS in environmental studies.

Analytic digital image processing system. Computer imaging systems, image representation in colour space. Image sampling quantization, quality measurement, data products, storage and retrieval. Photo systems and dip systems. Pre-processing (Encoding and decoding). Sources of image degradation, atmospheric, radiometric and geometric errors, systematic and non- systematic correction and image geometry operations. Image Enhancement, image characters, histogram, scatter plots, statistics and spatial statistics for processing, image models. Spatial transforms, enhancements, radiometric and geometric operators. Fourier transforms, scale space transforms, image fusion and texture analysis. Image classification, spectral discrimination pattern, matching Baye’s theorem- signature and feature extraction and training. Supervised and unsupervised methods, error matrix and accuracy estimates. Image analysis, concept of uncertainty, fuzzy partitioning, neural nets, sub-pixel classification concept, pattern recognition, feature descriptors. Remote sensing applications, Integration of Remote Sensing and GIS.