Hyperspectral image resolution offers limited spectral bands within a continual spectral spectrum, creating one of the spectra of most pixels inside the sequence which contains huge volume of data. Data transmission and storage is a challenging task. Compression of hyperspectral images are inevitable. This work proposes a Hyperspectral Image (HSI) compression using Hybrid Transform. First the HSI is decomposed into 1D and it is clustered and tiled. Each cluster is applied with Integer Karhunen–Loeve Transform (IKLT) and as such it is applied for whole image to get IKLT bands in spectral dimension. Then IKLT bands are applied with Integer Wavelet Transform (IDWT) to decorrelate the spatial data in spatial dimension. The combination of IKLT and IDWT is known as Hybrid transform. Second, the decorrelated wavelet coefficients are applied to Spatial-oriention Tree Wavelet (STW), Wavelet Difference Reduction (WDR) and Adaptively Scanned Wavelet Difference Reduction (ASWDR). The experimental result shows STW algorithm using Hybrid Transform gives better PSNR (db) and bits per pixel per band (bpppb) for hyperspectral images. The comparison between STW, WDR and ASWDR with Hybrid Transform for Indian Pines, Salinas, Botswana, Botswana and KSC images is experimented.

Target Detection involves the task of identifying and zeroing in on those set of pixels of an image that contain the required information (target). It has potential applications in diverse fields including automatic surveillance of large areas, illegal vehicle movement tracking in remote areas etc. This technique poses many challenges in terms of retaining only the target pixels by identifying and removal of noise pixels efficiently. In the case of detecting stationary targets concealed by foliage, traditional imaging techniques in the visible domain fail and Synthetic Aperture RADAR (SAR) imagery in the UWB-VHF (20-90 MHz) band comes to rescue. However, these foliage penetrating frequencies are also prone to high frequency speckle noise which asks for a robust target detecting technique. In this paper, one such algorithm based on Incoherent Change Detection and adaptive thresholding by Stein's Unbiased Risk Estimate (SURE) is proposed. The images used for testing were a set of 24 CARABAS-II VHF SAR images taken during a flight campaign in Sweden in the year 2002. The code has been written in MATLAB Platform and is able to successfully locate all the regions where the target is present and the False Alarm Rate (FAR) is also minimal. The execution times of the code for various image sets are also very promising and lie in the range 16-26 seconds for all the images chosen.

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