Christopher John Oliver

MUM (Merge Using Moments) segmentation for SAR images

In Synthetic Aperture Radar (SAR) and other systems employing coherent illumination to form high-resolution images, the resulting image is generally corrupted by a form of multiplicative noise, known as coherent speckle, with a signal-to-noise ration of unity. This severe form of noise presents singular problems for image processing software of all kinds. This paper describes a segmentation scheme, Merge Using Moments (MUM), for image corrupted by coherent speckle. The image is initially massively over-segmented. A scheme based on examination of the statistical properties (moments) of adjoining regions is employed to improve an over-fine segmentation by merging regions to produce a coarser segmentation. This scheme is employed iteratively until no remaining merge appears valid, at which time a good segmentation is obtained. Segmentation using μm on SAR imagery are given and the results compared to other segmentation schemes. The results of using it on typical SAR images illustrate its potential.

Rain forest classification based on SAR texture

This paper applies the concepts of optimized texture segmentation to the classification of SAREX-92 data from the Amazon rain forest. Initially, a simple scene is classified using both SAR texture and Band 5 Landsat TM imagery, yielding forest and not-forest joint probabilities of 97.8% and 96.5%, respectively. When the same procedure is applied to a more complicated scene, including regenerating areas, the equivalent results are 93.8% and 67.3%, When predictable corrections for shadowing and the presence of a highway are introduced, the not-forest joint probability is improved to about 78%. The residual discrepancy is then a consequence of the different ways in which the SAR texture and TM intensity respond to regenerating areas in the scene.

A new maximum-likelihood joint segmentation technique for multitemporal SAR and multiband optical images

In this paper, we devise a new technique for the fusion of a sequence of multitemporal single-channel synthetic aperture radar (SAR) images of a given area with a single multiband optical image. Unlike for SAR, the availability of optical images is largely affected by atmospheric conditions, so that this is a case of practical interest. First, a statistical model for the joint distribution of SAR and optical data is provided. Then, a split-merge test based on this model is derived, and its performance is evaluated both analytically and using a Monte Carlo simulation. A new segmentation technique is introduced (OPT MUM), based on the test and on a region-growing scheme. The effectiveness of the proposed technique for the fusion of multitemporal SAR and multiband optical images is tested on synthetic and real images. Results show that the proposed scheme allows to both 1) discriminate characteristics that would be impossible to distinguish using only a single sensor and 2) increase the overall discrimination performance, even when each sensor has its own discrimination capability.

Optimum texture analysis of SAR images

This paper demonstrates the importance of texture in SAR image interpretation. Initilly, a simulated annealing technique for despeckling SAR images is outlined. This yield an optimum estimate of the underlying image cross-section. On attempting to apply the method to distinguishing virgin forest from clearings in the Amazon rain forest, we find that the information is not carried in the intensity itself but in the image texture. We discuss the choice of texture estimator that conveys the maximum amount of information and apply the simulated annealing technique to this optimum texture image. We demonstrate that the two categories (forest/clearing) are now almost completely separated with a low misclassification rate. Finally, we examine the sensitivity of the method to the SAR resolution and show that the texture would generally not be visible in an ERS1 image at the conventional incidence angle whereas it could just be with the maximum available incidence angle.

Optimal classification of polarimetric SAR images using segmentation

The paper presents an optimised polarimetric segmentation technique for synthetic aperture radar (SAR) images, based on a generalised maximum likelihood approach. A full theoretical derivation is presented, together with a closed form analytical performance evaluation. The technique is compared to other known polarimetric segmentation schemes by application to a polarimetric SAR image of agricultural areas. A complete characterisation of the technique is provided in terms of polarimetric sensitivity and memory requirements.

A comparison of simulation techniques for correlated gamma and K-distributed images for SAR applications

Discusses clutter simulation which is an important element in the development of target detection algorithms for radar remote sensing. SAR images are well represented by the K distribution and an important feature of SAR clutter is the autocorrelation function. Methods are described for the generation of realisations from a correlated K distribution with specified correlation properties. Higher order correlations are also considered.<<ETX>>

Optimum detection and segmentation of oil-slicks with polarimetric SAR data

A new polarimetric discriminator, derived by using the generalised likelihood approach, is proposed in this paper for the detection of slicks on the sea surface. A complete analytical expression of the detection performance is derived for the proposed detector and used to compare it to other conventional polarimetric detectors, showing its better performance. In particular, the improvement obtained by using the polarimetric images with respect to the best single channel image is demonstrated. Moreover it is shown that the ML discriminant outperforms conventional polarimetric detectors. The results achieved in the segmentation of the SIR-C/X-SAR image of the experimental set up in the German Bight confirm the results of the theoretical performance analysis.

Comparison of annealing and iterated filters for speckle reduction in SAR

Many of the despeckling filters currently available operate by smoothing over a fixed window, whose size must be decided by two competing factors. Over homogeneous regions large window sizes are needed to improve speckle reduction by averaging. However, a large window size reduces the fundamental resolution of the algorithm, as with multi- looking. For instance, when one of these filters attempt to reconstruct a small bright object it produces artifacts around the object over a distance equal to the filter dimension. This means that the background is badly defined in the neighborhood of bright targets and edges, which is just where one would like it accurate. In this paper, these problems are overcome by introducing a correlated neighborhood model into the MAP filter. This filter operates on a small window and so is able to preserve resolution. The correlation model allows us to describe both the scene heterogeneity and the effects of partial smoothing, which in turn, allows us to iterate the filter, hence, increasing the amount of smoothing that can be achieved with a small window. This gives a filter that is able to adapt to the underlying fluctuations of the scene, preserve detail of still achieve large amounts of smoothing. The final iterated filter is then compared with the current DRA simulated annealing algorithm.

Segmentation and simulated annealing

In this paper we present a new algorithm for segmenting SAR images. A common problem with segmentation algorithms for SAR imagery is the poor placement of the edges of regions and hence of the regions themselves. This usually arises because the algorithm considers only a limited number of placements for regions. The new algorithm circumvents this shortcoming, and produces an optimal segmentation into a prescribed number of regions. An objective function is derived from a statistical model of SAR imagery. This objective function is then minimized by the method of simulated annealing which is, assuming some weak constraints, guaranteed to give the global minimum. Starting with an initial segmentation, the algorithm proceeds by randomly changing the current state. The annealing then decides whether or not to accept the new configuration by calculating the difference between the likelihoods of the data fitting these segmentations. In practice there are many possible implementations of the algorithm. We describe an implementation which uses a free topological model and alters the segmentation on a pixel by pixel basis. This makes it possible to get results of high resolution, as shown in results obtained by applying the new algorithm to both airborne X-band and ERS1 imagery.

Radar cross-section estimation of SAR images

We present an algorithm that is able to smooth out the speckle from many SAR images and which does not suffer from the drawbacks of multilooking. The algorithm is able to preserve the detail and resolution of the original image while producing a smooth, real-valued output. In many cases the quality of the smoothed image is sufficiently high that it may be used with standard optical post-processing algorithms. We use a global optimization method (simulated annealing) and single point gamma statistics to find the MAP solution for the radar cross- section. However, this method may also be regarded as an ideal adaptive filter that is both computationally efficient and highly parallelizable. Results are presented for airborne, ERS-1 and multi-temporal SAR images.