This chapter focuses on the problem of recovering a hyperspectral image descriptor based upon harmonic analysis. It departs from the use of integral transforms to model hyperspectral images in terms of probability distributions. This provides a link between harmonic analysi and affine geometric transformations between object surface planes in the scene. Moreover, the use of harmonic analysis pe...

Content-Based Image Retrieval (CBIR) systems are powerful search tools in image databases that have been little applied to hyperspectral images. Relevance Feedback (RF) is an iterative process that uses machine learning techniques and user’s feedback to improve the CBIR systems performance. We pursued to expand previous research in hyperspectral CBIR systems built on dissimilarity functions def...

Hyperspectral remote sensing allows for the detailed analysis of the surface of the Earth by providing high-dimensional images with hundreds of spectral bands. Hyperspectral image classification plays a significant role in hyperspectral image analysis and has been a very active research area in the last few years. In the context of hyperspectral image classification, supervised techniques (whic...

The idea of using artificial neural network has proved useful for hyperspectral image classification. However, the high dimensionality of hyperspectral images usually leads to the failure of constructing an effective neural network classifier. To improve the performance of neural network classifier, wavelet-based feature extraction algorithms are applied to extract useful features for hyperspec...

Hyperspectral imaging, due to providing high spectral resolution images, is one of the most important tools in the remote sensing field. Because of technological restrictions hyperspectral sensors has a limited spatial resolution. On the other hand panchromatic image has a better spatial resolution. Combining this information together can provide a better understanding of the target scene. Spec...

Hyperspectral imaging, due to providing high spectral resolution images, is one of the most important tools in the remote sensing field. Because of technological restrictions hyperspectral sensors has a limited spatial resolution. On the other hand panchromatic image has a better spatial resolution. Combining this information together can provide a better understanding of the target scene. Spec...

We describe a proof of concept for class knowledge transfer from a labeled hyperspectral image to an unlabeled image, captured with a different (hyper-/multi-spectral) sensor, when the spatial extents of the images partially overlap. By defining a set of spatio-spectral correspondences between the labeled source image and the unlabeled target image, we create a mapping between the images we can...

Hyperspectral image classification plays a vital role in today’s era. Identifying the suitable classifier and dimensionality reduction method is a chronic task for the Hyperspectral Image Classification on par with high accuracy and reliability. Image classification using multiple classifier system has been said to be evolving method for improving the accuracy and the reliability. The problems ...

This paper presents a new kernel framework for hyperspectral images classification. In this paper, a new feature extraction algorithm based on wavelet kernel non-negative matrix factorization (WKNMF) for hyperspectral remote sensing images is proposed. By using the feature of multi-resolution analysis, the new method can improve the nonlinear mapping capability of kernel non-negative matrix fac...

In this paper, we extend the Chan-Vese model for image segmentation in [1] to hyperspectral image segmentation with shape and signal priors. The use of the Split Bregman algorithm makes our method very efficient compared to other existing segmentation methods incorporating priors. We demonstrate our results on aerial hyperspectral images.