Detection and Tracking of objects in Analysing of Hyper spectral High-Resolution Imagery and Hyper spectral Video Compression

This paper deals mainly with the performance study and analysis of image retrieval techniques for retrieving unrecognized objects from an image using Hyper spectral camera and high-resolution image and retrieving unrecognized objects from an image using Hyper spectral camera at low light resolution. The main work identified is that efficient retrieval of unrecognized objects in an image will be made possible using spectral analysis and spatial analysis. The methods used above to retrieve unrecognized object from a high-resolution image are found to be more efficient in comparison with the other image retrieval techniques. The detection technique to identify objects in an image is accomplished in two steps: anomaly detection based on the spectral data and the classification phase, which relies on spatial analysis. At the classification step, the detection points are projected on the high-resolution images via registration algorithms. Then each detected point is classified using linear discrimination functions and decision surfaces on spatial features. The two detection steps possess orthogonal information: spectral and spatial. The identification of moving object in a camera is not possible in a low light environment as the object has low reflectance due to lack of lights. Using Hyper spectral data cubes, each object can be identified on the basis of object luminosity. Moving object can be identified by identifying the variation in frame value. The main work identified are that efficient retrieval of unrecognized objects in an image will be made possible using Hyper spectral analysis and various other methods such as Estimation of Reflectance, Feature and mean shift tracker, Traced feature located on image, Band pass filter (Background removal) etc. These methods used above to retrieve unrecognized object from a low light resolution are found to be more efficient in comparison with the other image retrieval techniques. The objects in an image may require that its edges should be smoother in order to make it detect easily by the receiver when it is send from one machine to another. As the image and video may be needed to be send from source to destination, due to huge amount of data that may be required for processing, retrieval and storage, because of the high resolution property of images, compression is a necessity. In order to overcome the problems associated with it, Transcoding technique is used by using filter arrays and lossless compression techniques.