Hyper Spectral Image Segmentation and Classification Using Least Square Clustering Based on FODPSO

The spatial analysis of the image detected and acquired by a satellite provides less accurate information on a remote location. Hyperspectral images are one of the images detected remotely, they are superior to multispectral images that provide spectral information. detailed information is one of the important requirements in many areas, such as military, agriculture, etc. The FODPSO classifier algorithm is used with the grouping technique of least squares for image segmentation. The 2D adaptive filter is proposed to eliminate the noise of the hyperspectral image detected and captured in order to eliminate the noise of the spot. Denoising the hyperspectral image (HSI) is an essential pre-processing step to improve the performance of subsequent applications

A Survey on: Hyper Spectral Image Segmentation and Classification Using FODPSO

The Spatial analysis of image sensed and captured from a satellite provides less accurate information about a remote location. Hence analyzing spectral becomes essential. Hyper spectral images are one of the remotely sensed images, they are superior to multispectral images in providing spectral information. Detection of target is one of the significant requirements in many are assuc has military, agriculture etc. This paper gives the analysis of hyper spectral image segmentation using fuzzy C-Mean (FCM)clustering technique with FODPSO classifier algorithm. The 2D adaptive log filter is proposed to denoise the sensed and captured hyper spectral image in order to remove the speckle noise