Segmentation

There is a need to automate terrain feature mapping so that to make the process more objective and less time consuming by using proper feature extraction techniques. The objective of this study was the use of object-oriented image analysis methods for the automatic extraction of alluvial fan terrain units. The study area was located in the Death Valley, Nevada, USA. The data used included an ASTER L1 satellite image and the 1 o Digital Elevation Model. The methodology developed for alluvial fan extraction included preprocessing of the digital data: filtering of the Digital Elevation Model (DEM) for noise removal, a Fourier Transform Wedge filter for the elimination of striping in the ASTER data and geometric co-registration of the satellite and DEM data. A multiresolution segmentation technique was then developed, delivering object primitives at four resolution levels. At the first and finest level, three physiographic feature types (basins, piedmonts and mountains) were extracted from the DEM to be used in the rule-based fuzzy classification of the following levels. Then, a knowledge base including definitions of Alluvial materials, Mountains, Basin floor salt deposits and Basin floor sediments was implemented. The second level was classified by the nearest neighbour classifier using spectral information for the first iteration of the classification procedure. For a second iteration, the knowledge base was further expanded primarily with heuristics concerning contextual information of the alluvial materials related to the geomorphological features extracted at the first level. Finally, in the last level, a projection was made, classifying the image into two classes: Alluvial Fans and Not Alluvial fans. The method gave good results in detecting alluvial fan units, working best for large shape alluvial fans. Some minor problems were encountered for the smaller alluvial fans, due to the difficulty of their boundar

A Region Merging Segmentation with Local Scale Parameters: Applications to Spectral and Elevation Data

Region merging is the most effective method for the segmentation of remote sensing data. The quality and the size of the resulted image objects is controlled by a global heterogeneity threshold, termed as the scale parameter. However, the multidimensional nature of the visible features in a scene defies the use of an even optimum single global scale parameter. In this study, a novel region merging segmentation method is proposed, where a local scale parameter is defined for each image object by its internal and external heterogeneity measures (i.e., local variance and Moran’s I). This method allows image objects with low internal and external heterogeneity to be further merged with higher scale parameter values, since they are more likely to be a part of an adjacent object, than objects with high internal and external heterogeneity. The proposed method was applied in spectral and elevation data and its results were evaluated visually and with supervised and unsupervised evaluation methods. The comparison with multi-resolution segmentation (MRS) showed that the proposed region merging method can produce improved segmentation results in terms of maximizing intra-object homogeneity and inter-object heterogeneity as well as in the delimitation of specific target objects, present in spectral and elevation data. The unsupervised evaluation results of the (1) Côte d’Azur, (2) Manchester, and (3) Szada images from the SZTAKI-INRIA building detection dataset showed that the proposed method (overall goodness, OGf (1): 0.7375, (2): 0.7923, (3): 0.7967) performs better than MRS (OGf (1): 0.7224, (2): 0.7648, (3): 0.7823). The higher values of OGf indicate their ability to produce segmentation results with reduced over-segmentation effects and without the need of presegmented input data, in contrast to the objective heterogeneity and relative homogeneity (OHRH) hybrid segmentation method (OGf (1): 0.5864, (2): 0.5151, (3): 0.6983)

Comparing morphological levelings constrained by different markers

Morphological levelings are powerful operators and possess a number of desired properties for the construction of nonlinear scale space image representations. In this paper, a comparison between levelings constrained by different multiscale markers -namely, reconstruction openings, alternate sequential, isotropic and anisotropic diffusion filters- was performed. For such a comparison a relation between the scales of each marker was established. The evaluation of the simplified images was performed by both qualitative and quantitative measures. Results indicate the characteristics of each scale space representation.Pages: 113-12

INTEGRATING TEXTURE FEATURES INTO A REGION-BASED MULTI-SCALE IMAGE SEGMENTATION ALGORITHM

The objective of this research was the design and development of a region-based multi-scale segmentation algorithm with the integration of complex texture features, in order to provide a low level processing tool for object-oriented image analysis. The implemented algorithm is called Texture-based MSEG and can be described as a region merging procedure. The algorithm is composed of two profiles. In the simple profile, the main part of the segmentation algorithm was included. The first object representation is the single pixel of the image. Through iterative pair-wise object fusions, which are made at several iterations, called passes, the final segmentation is achieved. The criterion for object merging is a homogeneity cost measure, defined as object heterogeneity, and computed based on spectral and shape features for each possible object merge. The heterogeneity is then compared to a user defined threshold, called scale parameter, in order for the decision of the merge to be determined. The processing order of the primitive objects is defined through a procedure (Starting Point Estimation), which is based on image partitioning, statistical indices and dithering algorithms. The advanced profile was implemented as an extension of the simple profile and was designed to include multi-resolution functionality and a global heterogeneity heuristic module for improving the segmentation capabilities. As part of the advanced profile, an integration of texture features to the region merging segmentation procedure was implemented through an Advanced Texture Heuristics module. Towards this texture-enhanced segmentation method, complex statistical measures of texture had to be computed based on objects, however, and not on orthogonal image regions. For each image object the grey level co-occurrence matrices and their statistical features were computed. The Advanced Texture Heuristics module

Mapping of forest tree distribution and estimation of forest biodiversity using Sentinel-2 imagery in the University Research Forest Taxiarchis in Chalkidiki, Greece

The aim of this study is to investigate the potential of Sentinel-2 imagery for the identification and determination of forest patches of particular interest, with respect to ecosystem integrity and biodiversity and to produce a relevant biodiversity map, based on Simpson’s diversity index in Taxiarchis university research forest, Chalkidiki, North Greece. The research is based on OBIA being developed on to bi-temporal summer and winter Sentinel-2 imagery. Fuzzy rules, which are based on topographic factors, such as terrain elevation and slope for the distribution of each tree species, derived from expert knowledge and field observations, were used to improve the accuracy of tree species classification. Finally, Simpson’s diversity index for forest tree species, was calculated and mapped, constituting a relative indicator for biodiversity for forest ecosystem organisms (fungi, insects, birds, reptiles, mammals) and carrying implications for the identification of patches prone to disturbance or that should be prioritized for conservation