Abstract A framework for encoding the boundary of any arbitrary binary shape in a high dimensional digital image is described, with special attention to the 3D case. The voxels of the outer shell of the shape are treated as vertices in a connected subgraph of an undirected cubic grid graph which has at least a spanning tree. A traversal of this spanning tree visits each voxel in a repeatable order. The sequence of traversed edges is coded into a chain that constitutes a high dimensional chain code, analogous to the classical Freeman chain code for 2D. The outer surface of the shape can be unambiguously reconstructed from this chain code. The framework is general in the sense that it can be used under any connectivity rule in any practical number of dimensions and also with noncubic voxels. Explicit algorithms for the computation of the chain codes and the reconstruction of the digital shapes in 3D and 4D are detailed. These are based on a hybrid graph traversal algorithm. The algorithms are illustrated with some simple digital shapes and tested with a benchmark dataset of medical images of intervertebral discs.

We present an evolutionary method for Ground Control Pointbased nonlinear registration of airborne pushbroom imagery, based on an implementation of Particle Swarm Optimization (PSO). The proposed method uses previous work for real-time GPU-based geocorrection of airborne pushbroom imagery. By projecting each acquired line onto a Digital Terrain Model (DTM) from the position and attitude of the camera at the time of acquisition, an orthoimage is generated. Using geocorrection as optimization function, the speed achieved allows using evolutionary methods in feasible time, enabling hundreds of repeated approximations during rectification, in contrast to classical geocorrection methods. In our approach, taking advantage of the speed and parallelization of Graphic Processing Units (GPU) by means of CUDA, PSO is used to find the best match between the projected pixels and a number of Ground Control Points, compensating any systematic errors in the navigation data used for the generation of the orthoimage.

Airborne pushbroom sensors produce images by acquiring scenes on a line-per-line basis. Depending on the motions of the aircraft carrying the sensor, the line integration time, and the targeted spatial resolution, missing areas may appear on geocorrected images. Missing pixels in geocorrected images are usually tackled by means of interpolation methods, such as nearest neighbor, but these cause visible artifacts that affect the visual quality of the result and also the performance of processing methods working on geocorrected images. We propose the use of an anisotropic diffusion inpainting method specifically devised for hyperspectral images, show some extreme examples, and discuss its convenience.

We present a method for real-time geocorrection of images from airborne pushbroom sensors using the hardware acceleration and parallel computing characteristics of modern graphics processing units. This allows very fast geocorrection without accuracy loss with respect to traditional direct methods, with very little computational load for the central processor. Our method is based on a projective texture technique originally developed for fast shadow rendering. By combining the image data with inertial navigation and positioning ancillary data, we correct the image to a digital terrain model (DTM) and produce a geocorrected and georeferenced image. The method works independently of the number of channels of the sensor. Results with an ultralight hyperspectral system show that the speed achieved with standard hardware for a 1-m grid DTM gives better than real-time performance. This allows in-flight inspection of the geocorrected image during acquisition, for more efficient coverage of large target areas. In regard to accuracy, differences with standard ray tracing direct geocorrection remained subpixel.

Semi-implicit schemes have been recently shown to speed up nonlinear diffusion in hyperspectral images while increasing the accuracy of subsequent classifiers in thematic mapping. Here, we show how semi-implicit schemes can be used to implement a truly anisotropic diffusion method for hyperspectral images, and we test the performance of different implementations in terms of computational overhead, speed, numerical accuracy, and thematic mapping performance. In addition, truly anisotropic trace-based diffusion formulations, besides a more precise steering of the diffusion processes, also allow implementation by means of local oriented Gaussian masks. We show how the implementations with the highest numerical accuracy can be also the simplest and fastest while still increasing the classification performance.

We show how a tensor-driven anisotropic diffusion denoising method affects the performance of a classifier trained to discriminate among vine varieties in noisy hyper spectral images. We compare the classification statistics on the original and denoised images and discuss the convenience of this kind of preprocessing for classification in hyperspectral images.

Anisotropic diffusion in the hypercube is a powerful image restoration and enhancement tool for hyperspectral imagery. Processing hyperspectral images requires special attention to accuracy, in order to preserve the high spectral precision that characterizes this kind of data. Here, we propose alternative implementations of the method with specific regard to the numerical accuracy of the solution in strict sense. We propose and describe how to apply rotationally invariant finite differences and a local filtering scheme. We also test and discuss their performance on several hyperspectral images.

In their paper, Gaoestimate the additive noise in hyperspectral images by computing the standard deviation of regression residuals. They perform multiple linear regression within spectrally homogeneous regions, which they obtain by means of an “object-seeking” algorithm. This letter shows that the segmentation algorithm is not correct, discusses how this affects the noise estimate, and proposes a correct algorithm that, additionally, decreases the computational load of the method.

Denoising of hyperspectral images in the domain of imaging spectroscopy by vectorial anisotropic diffusion is addressed. Anisotropic diffusion has shown to be a powerful denoising technique with many applications in several fields of image processing, and in the recent years, some significant advances have been published. However, these had not yet been specifically adapted to hyperspectral imagery. This paper reviews recent advances in anisotropic diffusion for multivalued images, analyzes their application to hyperspectral images, and proposes a new diffusion method which takes advantage of the recent improvements and conforms to the specificities of hyperspectral remote sensing. Some examples are provided using both a noisy image and a clean image with added noise

A method for the numeric estimation of the effective permittivity of any nonhomogeneous medium that admits the effective medium approximation under the longwave approximation is presented. Media are modeled as inclusions embedded in a continuous matrix. We show how the potentials at the inclusion boundaries are sufficient information for the estimation of the effective permittivity. We also show efficient implementation techniques to estimate them computationally, either by Monte Carlo random walk or by relaxation. We provide numerical results for several regular two- and three-dimensional structures and show the dependence of the effective response on the shape of the inclusions and their spatial arrangement, and the influence of the percolation threshold.