Shoreline Detection Using TerraSAR-X Quad Polarization Mode

In the Netherlands, the coastal zone is a dynamic area because of the geographic position. Economic activities and effects of global warming demand a frequent, accurate and detailed update of the coastline information. For this study, TerraSAR-X quad polari-zation was obtained at 6.6 m azimuth resolution during the Dual Receive Antenna (DRA) campaign. The coastline is detected by decomposing the polarimetric SAR components in three different scattering mechanisms: volume scatter, double bounce scatter, and surface scatter. This composite scattering model allows to classify the image based on these dif-ferent scattering mechanisms. After the decomposition, region growing segmentation is applied to group neighboring pixels with similar values to identify the coastline as the boundary between land and sea. Scheveningen beach has been chosen as case study. The primary methodology is the Freeman and Durden decomposition followed by two clas-sifications (1) Wishart supervised with Maximum Likelihood and without supervised classi-fication and region growing segmentation or (2) with segmentation applied directly to the decomposition results. The output segmentation vector is validated by comparing with nautical charts. After the decomposition and classification of the scatter mechanism, statis-tics showed good signature separability. The region growing segmentation gives good out-puts according to the difference in group pixels related to the land and those related to the sea.En los Países Bajos, la zona costera es una zona dinámica a causa de la posición geográfica. Las actividades económicas y los efectos del calentamiento mundial exigen una actualización frecuente, precisa y detallada de la información de la línea de costa. Para este estudio, se obtuvo la polarización cuadrangular TerraSAR-X a una resolución en acimut de 6,6 m durante la campaña DRA (Antena de Doble Recepción). La línea de costa es detectada mediante la descomposición de los componentes polarimétricos SAR en tres mecanismos diferentes de dispersión: dispersión de volumen, dispersión de doble rebote, y dispersión de superficie. Este modelo de dispersión compuesto permite clasifi-car la imagen basándose en estos mecanismos de dispersión diferentes. Después de la descomposición , la segmentación de crecimiento de regiones se aplica a los píxeles colindantes agrupados con valores similares para identificar la línea de costa como límite entre tierra y mar. Se ha elegido como estudio de caso la playa de Scheveningen. La me-todología principal es la descomposición de Freeman y Durden, seguida de dos clasifica-ciones: (1) la clasificación de Wishart, supervisada por un máximo de probabilidad y la segmentación por enfoque de “región” o (2) la segmentación aplicada directamente a los resultados de la descomposición. El vector de salida de la segmentación se valida me-diante la comparación de las cartas náuticas. Tras la descomposición y la clasificación del mecanismo de dispersión, las estadísticas mostraron una buena separabilidad de distinti-vos. La segmentación por enfoque de “región” proporciona buenos resultados según la diferencia observada entre los grupos de píxeles relativos a la tierra y los relativos al mar.Aux Pays-Bas, la bande côtière est une zone dynamique de par sa position géographi-que. Les activités économiques et les effets du réchauffement climatique requièrent une mise à jour fréquente, précise et détaillée des informations relatives au trait de côte. Dans le cadre de cette étude, des images en polarisation quadruple de TerraSAR-X ont été obtenues avec une résolution en azimut de 6,6 m pendant la campagne « Dual Receive Antenna » (DRA – antenne en mode de réception double). Le trait de côte est détecté par la décomposition des composantes SAR polarimétriques selon trois mécanismes de diffu-sion : diffusion volumique, diffusion double-bonds, et diffusion surfacique. Ce modèle de diffusion composite permet la classification de l’image à partir de ces différents mécanis-mes de diffusion. Après la décomposition, la segmentation par approche « région » est appliquée à un groupe de pixels voisins ayant des valeurs similaires pour identifier le trait de côte en tant que frontière entre la terre et la mer. La plage de Scheveningen a été choisie pour une étude de cas. La principale méthode est la décomposition de Freeman et Durden suivie de deux classifications (1) la classification de Wishart, supervisée par maximum de vraisemblance et sans supervision, et la segmentation par approche « région » ou (2) l’application directe de la segmentation aux résultats de la décomposi-tion. Le vecteur de segmentation en sortie est validé par comparaison avec les cartes marines. Après décomposition et classification du mécanisme de diffusion, les statistiques ont montré une séparabilité des signatures satisfaisante. La segmentation par approche « région » donne de bons résultats d’après la différence observée entre les groupes de pixels relatifs à la terre et ceux relatifs à la mer

Change Vector Analysis to Monitor the Changes in Fuzzy Shorelines

Mapping of shorelines and monitoring of their changes is challenging due to the large variation in shoreline position related to seasonal and tidal patterns. This study focused on a flood-prone area in the north of Java. We show the possibility of using fuzzy-crisp objects to derive shoreline positions as the transition zone between the classes water and non-water. Fuzzy c-means classification (FCM) was used to estimate the membership of pixels to these classes. A transition zone between the classes represents the shoreline, and its spatial extent was estimated using fuzzy-crisp objects. In change vector analysis (CVA) applied to water membership of successive shorelines, a change category was defined if the change magnitude between two years, T1 and T2, differed from zero, while zero magnitude corresponded to no-change category. Over several years, overall change magnitude and change directions of the shoreline allowed us to identify the trend of the fluctuating shoreline and the uncertainty distribution. The fuzzy error matrix (FERM) showed overall accuracies between 0.84 and 0.91. Multi-year patterns of water membership changes could indicate coastal processes such as: (a) high change direction and high change magnitude with a consistent positive direction probably corresponding to land subsidence and coastal inundation, while a consistent negative direction probably indicates a success in a shoreline protection scheme; (b) low change direction and high change magnitude indicating an abrupt change which may result from spring tides, extreme waves and winds; (c) high change direction and low change magnitude which could be due to cyclical tides and coastal processes; and (d) low change direction and low change magnitude probably indicating an undisturbed environment, such as changes in water turbidity or changes in soil moisture. The proposed method provided a way to analyze changes of shorelines as fuzzy objects and could be well-suited to apply to coastal areas around the globe

Comparing Fuzzy Sets and Random Sets to Model the Uncertainty of Fuzzy Shorelines

This paper addresses uncertainty modelling of shorelines by comparing fuzzy sets and random sets. Both methods quantify extensional uncertainty of shorelines extracted from remote sensing images. Two datasets were tested: pan-sharpened Pleiades with four bands (Pleiades) and pan-sharpened Pleiades stacked with elevation data as the fifth band (Pleiades + DTM). Both fuzzy sets and random sets model the spatial extent of shoreline including its uncertainty. Fuzzy sets represent shorelines as a margin determined by upper and lower thresholds and their uncertainty as confusion indices. They do not consider randomness. Random sets fit the mixed Gaussian model to the image histogram. It represents shorelines as a transition zone between water and non-water. Their extensional uncertainty is assessed by the covering function. The results show that fuzzy sets and random sets resulted in shorelines that were closely similar. Kappa (κ) values were slightly different and McNemar’s test showed high p-values indicating a similar accuracy. Inclusion of the DTM (digital terrain model) improved the classification results, especially for roofs, inundated houses and inundated land. The shoreline model using Pleiades + DTM performed better than that of using Pleiades only, when using either fuzzy sets or random sets. It achieved κ values above 80%