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

High-Resolution Remote Sensing Image Classification Using Associative Hierarchical CRF Considering Segmentation Quality

This letter proposes an associative hierarchical conditional random field (AHCRF) model to improve the classification accuracy of high-resolution remote sensing images. It considers segmentation quality of superpixels, avoids a time-consuming selection of optimal scale parameters, and alleviates the problem of classification accuracy sensitive to undersegmentation errors that is present in traditional object-oriented classification methods. The model is built on a graph hierarchy, including the pixel layer as a base layer and multiple superpixel layers derived from a mean shift presegmentation. It extracts clustered features of pixels for superpixels at each layer and then defines the potentials of the AHCRF model. We suggest a weighted version of the interlayer potential using the size of a superpixel as a measure to reflect segmentation quality. In this way, erroneously labeled pixels of a superpixel are penalized. Experiments are presented using a part of the downsampled Vaihingen data from the ISPRS benchmark data set. Results confirm that our model shows more than 80% overall classification accuracy and is superior to the original AHCRF model and comparable to other models. It also alleviates the choosing of suitable segmentation parameters

Automatic Detection of Individual Trees from VHR Satellite Images Using Scale-Space Methods

This research investigates the use of scale-space theory to detect individual trees in orchards from very-high resolution (VHR) satellite images. Trees are characterized by blobs, for example, bell-shaped surfaces. Their modeling requires the identification of local maxima in Gaussian scale space, whereas location of the maxima in the scale direction provides information about the tree size. A two-step procedure relates the detected blobs to tree objects in the field. First, a Gaussian blob model identifies tree crowns in Gaussian scale space. Second, an improved tree crown model modifies this model in the scale direction. The procedures are tested on the following three representative cases: an area with vitellaria trees in Mali, an orchard with walnut trees in Iran, and one case with oil palm trees in Indonesia. The results show that the refined Gaussian blob model improves upon the traditional Gaussian blob model by effectively discriminating between false and correct detections and accurately identifying size and position of trees. A comparison with existing methods shows an improvement of 10–20% in true positive detections. We conclude that the presented two-step modeling procedure of tree crowns using Gaussian scale space is useful to automatically detect individual trees from VHR satellite images for at least three representative cases

Automatic Detection of Individual Trees from VHR Satellite Images Using Scale-Space Methods

This research investigates the use of scale-space theory to detect individual trees in orchards from very-high resolution (VHR) satellite images. Trees are characterized by blobs, for example, bell-shaped surfaces. Their modeling requires the identification of local maxima in Gaussian scale space, whereas location of the maxima in the scale direction provides information about the tree size. A two-step procedure relates the detected blobs to tree objects in the field. First, a Gaussian blob model identifies tree crowns in Gaussian scale space. Second, an improved tree crown model modifies this model in the scale direction. The procedures are tested on the following three representative cases: an area with vitellaria trees in Mali, an orchard with walnut trees in Iran, and one case with oil palm trees in Indonesia. The results show that the refined Gaussian blob model improves upon the traditional Gaussian blob model by effectively discriminating between false and correct detections and accurately identifying size and position of trees. A comparison with existing methods shows an improvement of 10–20% in true positive detections. We conclude that the presented two-step modeling procedure of tree crowns using Gaussian scale space is useful to automatically detect individual trees from VHR satellite images for at least three representative case

Assessment of Forest Above-Ground Biomass Estimation from PolInSAR in the Presence of Temporal Decorrelation

In forestry studies, remote sensing has been widely used to monitor deforestation and estimate biomass, and it has contributed to forest carbon stock management. A major problem when estimating biomass from optical and SAR remote sensing images is the saturation effect. As a solution, PolInSAR offers a high coverage height map that can be transformed into a biomass map. Temporal decorrelation may affect the accuracy of PolInSAR and may also have an effect on the accuracy of the biomass estimates. In this study, we compared three different height estimation models: the Random-Volume-over-Ground (RVoG), Random-Motion-over-Ground (RMoG), and Random-Motion-over-Ground-Legendre (RMoG L ) models. The RVoG model does not take into account the temporal decorrelation, while the other two compensate for temporal decorrelation but differ in structure function. The comparison was done on 214 field plots of the 10 m radius of the BioSAR2010 campaign. Different models relating PolInSAR height and biomass were developed by using polynomial, exponential, power series, and piece-wise linear regression. Different strategies for training and test subset selection were followed to obtain the best possible regression models. The study showed that the RMoG L model provided the most accurate biomass predictions. The relation between RMoG L height and biomass is well expressed by the exponential model with an average RMSE equal to 48 ton ha − 1 and R 2 value equal to 0.62. The relative errors for estimated biomass were equal to 46% for the RVoG model, to 37% for the RMoG, and to 30% for the RMoG L model. We concluded that taking the temporal decorrelation into account for estimating tree height has a significant effect on providing accurate biomass estimates