The integration of spatial and spectral responses in hyperspectral image data analysis has been identified as a desirable objective by the remote sensing community. However, most available attempts are based on the consideration of spectral information separately from spatial information, and thus the two types of information are not treated simultaneously. In this paper, we describe our backgr...

Hyperspectral imagery is playing more and more important role in many fields such as geology, agriculture, environment, military, atmosphere and so on. We need to register Hyperspectral imagery so that we can build coherent image cubes and get coherent spectrum of pixels. Our concern in this paper is the Fourier-Mellin Transform (FMT) for image registration process and its related techniques fo...

This paper introduces a new supervised Bayesian approach to hyperspectral image segmentation, with two main steps: (a) learning, for each class label, the posterior probability distributions, based on a multinomial logistic regression model; (b) segmenting the hyperspectral image, based on the posterior probability distribution learnt in step (a) and on a multi-level logistic prior encoding the...

The availability of hyperspectral images expands the capability of using image classification to study detailed characteristics of objects, but at a cost of having to deal with huge data sets. This work studies the use of the principal component analysis as a preprocessing technique for the classification of hyperspectral images. Two hyperspectral data sets, HYDICE and AVIRIS, were used for the...

The advent of hyperspectral image technology is a major leap in recent years, it obtains the surface of the earth image contains rich space, radiation and spectral information, Mixed pixels not only effects identification and classification precision of object, but also greatly hinder the development of quantitative remote sensing, so effectively interpret mixed pixels is an important problem f...

There has been significant research on pan-sharpening multispectral imagery with a high resolution image, but there has been little work extending the procedure to high dimensional hyperspectral imagery. We present a wavelet-based variational method for fusing a high resolution image and a hyperspectral image with an arbitrary number of bands. To ensure that the fused image can be used for task...

Due to inadequate energy captured by the hyperspectral camera sensor in poor illumination conditions, low-light images (HSIs) usually suffer from low visibility, spectral distortion, and various noises. A range of HSI restoration methods have been developed, yet their effectiveness enhancing HSIs is constrained. This work focuses on enhancement task, which aims reveal spatial-spectral informati...

With the development of deep learning, the performance of hyperspectral image (HSI) classification has been greatly improved in recent years. The shortage of training samples has become a bottleneck for further improvement of performance. In this paper, we propose a novel convolutional neural network framework for the characteristics of hyperspectral image data, called HSI-CNN. Firstly, the spe...

The use of non-destructive methods to detect egg hatching properties could increase efficiency in commercial hatcheries by saving space, reducing costs, and ensuring hatching quality. For this purpose, a hyperspectral imaging system was built to detect embryo development and vitality using spectral and morphological information of hatching eggs. A total of 150 green shell eggs were used, and hy...