Seagrass and seaweed beds are very important for fish’s growth, but the beds are sensitive to environmental changes and human activities. Coastal zone monitoring is necessary for the conservation of seagrass and seaweed beds. Satellite data are used for the coastal zone monitoring. There are large atmospheric effects at the satellite data, and low altitude observations using UAV have the benefi...

A spectral linear prediction compression scheme for lossless compression of hyperspectral images is proposed in this paper. Since hyperspectral images have a great deal of correlation from band to band, spectral linear prediction algorithm, which utilizes information from several bands, is very efficient for compression purposes. The proposed algorithm is compared to JPEG-LS and CALIC encoding ...

Hyperspectral images (HSIs) has become very popular area of research. This paper deals with the compression and classification of Hyperspectral images using Discrete Wavelet Technique in conjunction with Non negative Tucker Decomposition. This algorithm exploits both the spectral and the spatial information of the images. The core idea behind the proposed technique is to apply TD on the DWT coe...

We attempted a method of fusing a coarse resolution hyperspectral image and a high spatial resolution image with a few spectral bands to produce a high resolution hyperspectral image, and to extract the spectra of the end-members. The method is based on the linear spectral mixing model and an iterative maximum-likelihood algorithm is used to invert the mixing equation. The effects of noise and ...

In our previous works, the wavelet-based feature extraction algorithms have been developed to explore the useful information for the hyperspectral image classification. On the other hand, the idea of using artificial neural network (ANNs) has also proved useful for hyperspectral image classification. To combine the advantages of ANNs with wavelet-based feature extraction methods, the wavelet ne...

In this paper, we propose a hyperspectral image lossy-tolossless compression using three-dimensional Embedded ZeroBlock Coding (3D EZBC) algorithm based on Karhunen-Loève transform (KLT) and wavelet transform (WT). Furthermore, an improved Hao’s matrix factorization method for integer KLT is also presented, which can reduce not only the computational complexity but also the memory requirements....

An effective and lossy compression technique for multispectral and hyperspectral image data minimizes both the spatial and spectral correlations while preserving the spectral characteristics of the data. In this paper, we use two-dimensional wavelet transform and propose an encoding technique for wavelet coefficients. We use KroneckerProduct Gain-Shape Vector Quantization coupled with the gener...

Supervised classification of hyperspectral image is considered to be the method of choice for improved land use/cover mapping. However, there is no single classifier which offers acceptable results across various study sites, sensors and resolutions. Further, the use of dimensionality reduction methods further increase the permutations and combinations required for selecting the appropriate cla...

Latest generation remote sensing instruments (called hyperspectral imagers) are now able to generate hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. In previous work, we have reported that the scalability of parallel processing algorithms dealing with these high-dimensional data volumes is affected by the amount of data to be ex...

Classification of hyperspectral images always suffers from high dimensionality and very limited labeled samples. Recently, the spectral-spatial classification has attracted considerable attention and can achieve higher classification accuracy and smoother classification maps. In this paper, a novel spectral-spatial classification method for hyperspectral images by using kernel methods is invest...