Object based image analysis has gained on the traditional per-pixel multi-spectral based approaches. The main pitfall of using anisotropic diffusion for creating a multi scale representation of a remotely sensed image remains the computational burden. Producing the coarser scales in a multi scale representation or, diffusing spatially large images involves significant time and resources. This paper proposes a fast approach for anisotropic diffusion that overcomes spatial size limitations by distributing the diffusion as individual sub-processes over several overlapping sub-images. The overlap areas are synchronized at specific diffusion time ensuring that the fast approximation does not deviate too much from its single process equivalent. This demonstrated for an image, which can be diffused using a traditional sequential approach. In addition, experimental data for very large images that can not efficiently be processed using a sequential approach is illustrated.

Land Use and Land Cover (LULC) are characterized by a large variety of spectrally distinct LULC classes. The diagnostic and evaluation of the spectral separability measure yields the potential for automated identification and mapping of these classes. This study proposes a new cascaded fuzzy C-mean classification method for a rough classification of (LULC) classes. The method is based on the use of spectral indices as innovative features to provide a coarse classification of remotely sensed data. The robustness and accuracy of the defined classification schema is performed based on the computation of confusion matrices and Kappa coefficient. The Kappa statistic ranges from 0.90 to 0.98 for the set of the evaluated images which infer the good accuracy of the new rough classification scheme.

In this paper, image segmentation is addressed within the framework of nonlinear multiscale watersheds in combination with graph theory. First, a graph is created which decomposes the image in scale and space using the concept of multiscale watersheds. In the subsequent step the obtained graph is partitioned using recursive graph cuts in a coarse to fine manner. In this way, we combine scale and feature measures in a flexible way. The dissimilarity between graph-nodes is estimated by using the Earth Mover's Distance on a featureset that combines color, scale and contrast. Experimental results demonstrate the efficiency of the proposed method for natural scene images.

We propose a novel, Bayesian formulation of the edge-stopping (diffusivity) function in a nonlinear diffusion scheme in terms of edge probability under a marginal prior on noise-free gradient. This formulation differs from the existing probabilistic diffusion approaches that give stochastic formulations for the conductivity but not for the diffusivity function of the gradient. In particular, we impose a Laplacian prior for the ideal gradient, but the proposed formulation is general and can be used with other marginal distributions. We also make links to related works that treat correspondences between nonlinear diffusion and wavelet shrinkage.

We propose a new classification method for vector-valued images, based on: 1) a causal Markovian model, defined on the hierarchy of a multiscale region adjacency tree (MRAT), and 2) a set of nonparametric dissimilarity measures that express the data likelihoods. The image classification is treated as a hierarchical labeling of the MRAT, using a finite set of interpretation labels (e.g., land cover classes). This is accomplished via a noniterative estimation of the modes of posterior marginals (MPM), inspired from existing approaches for Bayesian inference on the quadtree. The paper describes the main principles of our method and illustrates classification results on a set of artificial and remote sensing images, together with qualitative and quantitative comparisons with a variety of pixel-based techniques that follow the Bayesian-Markovian framework either on hierarchical structures or the original image lattice.

This paper presents a segmentation scheme for images containing both smooth regions and textures. It is based on a vector-valued anisotropic diffusion on a combined color/Gabor feature space, followed by a hierarchical segmentation using dynamics of multiscale 'generalized gradient' watersheds. The proposed method gives good segmentation results and is shown to be more effective than its counterpart, which uses only multiscale color information.

This paper investigated a probabilistic method using likelihood and granularity measures for scale selection in the case of anisotropic diffusion filtering. A multi-scale stack of diffused images is created, from which the optimal scale for subsequent analysis is extracted. For each pixel at each scale, a likelihood measure based upon the minimal description length principles is attached. By maximizing the likelihood that each pixel is optimal represented at a certain scale, a local scale map can be defined. The latter is subsequently augmented by a MRF- related granularity measure (Ising potential). This ensures the retainment of certain localized details. The global scale is defined as the scale at which the difference between the corresponding diffused image and the local scale image is minimal. The proposed approach is applied as a preprocessing step for the classification of high resolution air-borne multi-spectral images. Initial results show that the probabilistic scale selection identifies a suitable ideal scale.

We present a new framework for the hierarchical segmentation of color images. The proposed scheme comprises a nonlinear scale-space with vector-valued gradient watersheds. Our aim is to produce a meaningful hierarchy among the objects in the image using three image components of distinct perceptual significance for a human observer, namely strong edges, smooth segments and detailed segments. The scale-space is based on a vector-valued diffusion that uses the Additive Operator Splitting numerical scheme. Furthermore, we introduce the principle of the dynamics of contours in scale-space that combines scale and contrast information. The performance of the proposed segmentation scheme is presented via experimental results obtained with a wide range of images including natural and artificial scenes.

A multi-resolution segmentation approach for color images is proposed. The scale space is generated using the Perona-Malik diffusion approach and the watershed algorithm is employed to produce the regions in each scale. The dynamics of contours and the relative entropy of color region distribution are estimated as region dissimilarity features across the scale-space stack, and combined using a fuzzy rule based system. A minima-linking process by downward projection is carried out and subsequently the region dissimilarity, combining color, scale and homogeneity is estimated for the finer scale (localization scale). The final segmentation is derived using a previously presented merging process. To validate its performance qualitative and quantitative results are provided.