Machine Learning in Hybrid Environment for Information Identification with Remotely Sensed Image Data

Multi sensor image data used in diverse applications for Earth observation has portrayed immense potential as a resourceful foundation of information in current context. The scenario has kindled the requirement for efficient content-based image identification from the archived image databases to provide increased insight to the remote sensing platform. Machine learning is the buzzword for contemporary data driven decision making in the domain of emerging trends in computer science. Diverse applications of machine learning have exhibited promising outcomes in recent times in the areas of autonomous vehicles, natural language processing, computer vision and web searching. An important application of machine learning is to extract meaningful signatures from the unstructured data. The process facilitates identification of important information in the hour of need. In this work, the authors have explored the application of machine learning for content based image classification with remotely sensed image data. A hybrid approach of machine learning is implemented in this work for enhancing the classification accuracy and to use classification as a pre cursor of retrieval. Further, the approaches are compared with respect to their classification performances. Observed results have revealed the superiority of the hybrid approach of classification over the individual classification results. The feature extraction techniques proposed in this work have ensured higher accuracy compared to state-of-the-art feature extraction techniques.