An iterative spectral-spatial bayesian labeling approach for unsupervised robust change detection on remotely sensed multispectral imagery

kogs-www.informatik.uni-hamburg.de/projects/Censis.html Abstract In multispectral remote sensing, change detection is a central task for all kinds of monitoring purposes. We suggest a novel approach where the problem is formulated as a Bayesian labeling problem. Considering two registered images of the same scene but different recording time, a Bayesian probability for ' Change ' and ' NoChange ' is determined for each pixel from spectral as well as spatial features. All necessary parameters are estimated from the image data itself during an iterative clustering process which updates the current probabilities. The contextual spatial features are derived from Markov random field modeling. We define a potential as a function of the probabilities of neighboring pixels to belong to the same class. The algorithm is robust against spurious change detection due to changing recording conditions and slightly misregistered high texture areas. It yields successful results on simulated and real multispectral multitemporal aerial imagery.

The Impact of Motion Correction on Lesion Characterization in DCE Breast MR Images

ABSTRACT In the context of dynamic contrast enhanced breast MR imaging we analyzed the effect of motion compensating registration on the characterization of lesions. Two registration techniques were applied: 1) rigid registration and 2) elastic registration based on the Navier-Lamé equation. Interpreting voxels that exhibit a decline in image intensity after contrast injection (compared to the non-contrasted native image) as motion outliers, it can be shown that the rate of motion outliers can be largely reduced by both rigid and elastic registration. The performance of lesion features, including maximal signal enhancement ratio and variance of the signal enhancement ratio, was measured by area under the ROC curve as well as Cohen's κ and showed significant improvement for elastic registration, whereas features derived from rigidly registered images did not in general exhibit a significant improvement over the level of unregistered data

The Color Constancy Problem: An Illumination Invariant Mapping Approach

Abstract. We suggest a novel approach to the Color Constancy Problem for multispectral imagery. Our approach is based on a dichromatic illumination model and lters out all spectral information which possibly stems from the illumination rather than from the re ectance of a given surface. Instead of recovering the re ectance signal, the suggested mapping produces a new only surface re ectance-dependent descriptor which isinvariant against varying illumination. Sole input is the relative direct to di use illumination spectrum, no assumptions about the possible re ectance spectra are made. The mapping is a purely pixel based, fast, one-pass matrix operation and can preprocess multispectral images in order to segment them into regions of homogeneous re ectance, unperturbed by varying illumination conditions.

Surface Orientation Invariant Matching Of Spectral Signatures

: For monitoring purposes, change detection and segmentation of remotely sensed multispectral images it is necessary to normalize the images in order to yield spectral signatures independent of illumination, atmospheric conditions and surface orientations. For small scale artificial objects in high resolution images however, the surface orientation is not known, and hence comparison of spectral signatures is prone to error due to varying illumination. We assess the relative contribution of direct versus diffuse illumination, and present a spectral distance measure and a spectral transformation which provide color constancy and are independent of the degree of direct versus diffuse illumination and thus surface orientation. KURZFASSUNG: Fur Monitoring, Anderungserkennung in und Segmentation von multispektralen Fernerkundungsbildern ist zunachst eine Normalisierung der spektralen Signaturen notig, um diese invariant gegen Beleuchtung, atmosph arische Bedingungen und Oberflachenorient..

The Color Constancy Problem in Multispectral Remote Sensing - On the Impact of Surface Orientation on Spectral Signatures

We suggest a novel approach to the surface orientation related color constancy problem for multispectral imagery. The basic problem addressed in this thesis is just how the observed spectral signature of a Lambertian target surface varies when its surface orientation changes.Our approach is based on a dichromatic illumination model which we have verified by several thousands in situ measured spectra of a dozen samples of different surface materials. The two principal components of daylight illumination are direct sun light and diffuse sky light, which show distinctively different spectral characteristics. The observed spectrum of a given surface with a specific Lambertian reflectance spectrum varies with its surface orientation, since each orientation leads to different contributions of direct solar and indirect diffuse illumination. The ambiguity about the actual illumination of a surface, causing uncertainty about its spectral reflectance, has been recognized as the color constancy problem. In multispectral remote sensing, this leads to erroneous classification and segmentation as well as spurious results of change detection.We introduce a transformation which is invariant against surface orientation. The suggested invariant is a linear mapping in the logarithmic feature space and filters out all spectral information which can possibly stem from an illumination change rather than from the reflectance of a given surface. Instead of recovering the reflectance signal, the suggested mapping produces a new only surface reflectance-dependent descriptor which is invariant against varying illumination. Sole input is the relative direct to diffuse illumination spectrum. No assumptions about the possible reflectance spectra are made. Error propagation through the transform is well understood. The mapping is a purely pixel-based, one-pass matrix operation and can preprocess multispectral images in order to segment them into regions of homogeneous reflectance, unperturbed by varying illumination conditions.Apart from simulated and in situ measured data, the suggested transform has been successfully applied to experimental multispectral imagery. The quantitative results and example clippings from the imagery show significant improvements in the multispectral classification of target surfaces under varying surface orientation. Although the transformed data may not completely supersede the original spectral data, the suggested transformation is shown to be a powerful early processing step, allowing subsequent orientation invariant classification, edge detection and segmentation

Registration of Airborne Scanner Imagery Using Akima Local Quintic Polynomial Interpolation

Evaluation and analysis of most remotely sensed data requires careful image-to-image registration or ortho-rectification (geocoding). Photogrammetric practice has shown that polynomial transformation functions are useful for registration of aerial photographs. However, multi- and hyperspectral remotely sensed data are often recorded by airborne line scanners. Then ortho-rectification by conventional global coordinate transforms is not satisfactory, particularly due to the non-instantaneous image formation process. It is rather necessary to allow for local corrections of distortions. We have implemented two interpolation techniques for the computation of the resampling coordinates, local AKIMA and HARDY multiquadric interpolation, and applied them to experimental image data. Comparison shows that resampling based on AKIMA interpolation is much faster than on HARDY multiquadric. Moreover, AKIMA interpolation in the limit corresponds to conventional second degree polynomial transformation..