Raffaele Gaetano

Hierarchical Texture-Based Segmentation of Multiresolution Remote-Sensing Images

In this paper, we propose a new algorithm for the segmentation of multiresolution remote-sensing images, which fits into the general split-and-merge paradigm. The splitting phase singles out clusters of connected regions that share the same spatial and spectral characteristics. These clusters are then regarded as atomic elements of more complex structures, particularly textures, that are gradually retrieved during the merging phase. The whole process is based on a recently developed hierarchical model of the image, which accurately describes its textural properties. In order to reduce the computational burden and preserve contours at the highest spatial definition, the algorithm works on the high-resolution panchromatic data first, using low-resolution full spectral information only at a later stage to refine the segmentation. It is completely unsupervised, with just a few parameters set at the beginning, and its final product is not a single segmentation map but rather a sequence of nested maps which provide a hierarchical description of the image, at various scales of observations. The first experimental results, obtained on a remote-sensing Ikonos image, are very encouraging and confirm the algorithm potential.

Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation

In this paper, we present a novel multiscale texture model and a related algorithm for the unsupervised segmentation of color images. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions. Such interactions are modeled, in turn, by means of a set of Markov chains, one for each direction, whose parameters are collected in a feature vector that synthetically describes the texture. Based on the feature vectors, the texture are then recursively merged, giving rise to larger and more complex textures, which appear at different scales of observation: accordingly, the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, addresses the unsupervised segmentation problem based on the H-MMC model. The ldquofragmentationrdquo step allows one to find the elementary textures of the model, while the ldquoreconstructionrdquo step defines the hierarchical image segmentation based on a probabilistic measure (texture score) which takes into account both region scale and inter-region interactions. The performance of the proposed method was assessed through the Prague segmentation benchmark, based on mosaics of real natural textures, and also tested on real-world natural and remote sensing images.

Marker-Controlled Watershed-Based Segmentation of Multiresolution Remote Sensing Images

A new technique for the segmentation of single- and multiresolution (MR) remote sensing images is proposed. To guarantee the preservation of details at fine scales, edge-based watershed is used, with automatically generated markers that help in limiting oversegmentation. For MR images, the panchromatic and multispectral components are processed independently, extracting both the edge maps and the morphological and spectral markers that are eventually fused at the highest resolution, thus avoiding any information loss induced by pansharpening. Numerical results on object layer extraction and simple classification tasks prove the proposed techniques to provide accurate segmentation maps, which preserve fine details and, contrary to state-of-the-art products, can single out objects equally well at very different scales.

Exploration of Multitemporal COSMO-SkyMed Data via Interactive Tree-Structured MRF Segmentation

We propose a new approach for remote sensing data exploration, based on a tight human-machine interaction. The analyst uses a number of powerful and user-friendly image classification/segmentation tools to obtain a satisfactory thematic map, based only on visual assessment and expertise. All processing tools are in the framework of the tree-structured MRF model, which allows for a flexible and spatially adaptive description of the data. We test the proposed approach for the exploration of multitemporal COSMO-SkyMed data, that we appropriately registered, calibrated, and filtered, obtaining a performance that is largely superior, in both subjective and objective terms, to that of comparable noninteractive methods.

Detection of environmental hazards through the feature-based fusion of optical and SAR data: a case study in southern Italy

The use of remote-sensing images is becoming common practice in the fight against environmental crimes. However, the challenge of exploiting the complementary information provided by radar and optical data, and by more conventional sources encoded in geographic information systems, is still open. In this work, we propose a new workflow for the detection of potentially hazardous cattle-breeding facilities, exploiting both synthetic aperture radar and optical multitemporal data together with geospatial analyses in the geographic information system environment. The data fusion is performed at a feature-based level. Experiments on data available for the area of Caserta, in southern Italy, show that the proposed technique provides very high detection capability, up to 95%, with a very low false alarm rate. A fast and easy-to-use system has been realized based on this approach, which is a useful tool in the hand of agencies engaged in the protection of territory.

Optical-Driven Nonlocal SAR Despeckling

We propose a new synthetic aperture radar (SAR) despeckling technique based on nonlocal filtering and driven by a coregistered optical image. A preliminary homogeneous versus heterogeneous classification of the image is used to decide where the optical guide can be safely used, thus preventing any distortion of the SAR geometry. Even in regions where the use of optical data is enabled, despeckling is carried out exclusively in the SAR domain, and the optical guide is used only to improve the predictor selection in nonlocal filtering and, hence, in the estimation process. Experiments on real-world imagery confirm the potential of the proposed approach.

Parallel implementations of a disparity estimation algorithm based on a Proximal splitting method

The Parallel Proximal Algorithm (PPXA+) has been recently introduced as an efficient tool for solving convex optimization problems. It has proved particularly effective in the context of stereo vision, used as the methodological core of a novel disparity estimation technique. In this work, the main methodological issues limiting the efficient parallelization of this technique are addressed, and further modifications are proposed to enable and optimize the design of parallel implementations. Finally, actual implementations that fit both the multi-core CPU and GPU devices are provided and tested to validate the performance potential of the proposed technique.

Recursive Texture Fragmentation and Reconstruction segmentation algorithm applied to VHR images

The Texture Fragmentation and Reconstruction (TFR) algorithm, recently proposed for the segmentation of textured images, has been applied with promising results to highresolution remote-sensing images. The algorithm provides a sequence of nested segmentation maps which allow the analysis at various scales of observation. However, the performance which is very good at large scales, with complex semantic areas retrieved with remarkable accuracy, becomes less satisfactory at finer scales. In this paper we propose to use the TFR in a recursive fashion, segmenting the image in just two regions, initially, with each region further segmented only if relevant subre-gions emerge. The recursive TFR allows one to better adapt to local statistics and to extract significant textures also at finer scales. Early experimental results validate the effectiveness of the new algorithm.

A marker-controlled watershed segmentation: Edge, mark and fill

The segmentation of very high resolution (VHR) images portraying complex urban scenarios is a rather challenging problem. In particular, great attention must be devoted to preserve fine man-made details, of major interest for most user applications. For this reason, edge-based segmentation methods are likely preferable to region-based methods. The latter, in fact, e.g. [1], [2], succeed in taking into account long range interactions and hence perform typically well in terms of “global” accuracy, but exhibit a lower “local” accuracy with respect to former, [3].

OpenCL implementation of motion estimation for cloud video processing

With the raise of cloud computing infrastructures on one side and the increased accessibility of parallel computational devices on the other, such as GPUs and multi-core CPUs, parallel programming has recently gained a renewed interest. This is particularly true in the domain of video coding, where the complexity and time consumption of the algorithms tend to limit the access to the core technology. In this work, we focus on the motion estimation problem, well-known to be the most time consuming step of a majority of video coding techniques. By relying on the use of the OpenCL standard, which provides a cross-platform framework for parallel programming, we propose here a scalable CPU/GPU implementation of the full search motion estimation algorithm (FSBM), and study its performances also with respect to the issues raised by the use of OpenCL.