Region-based classification potential for land-cover classification with very high spatial resolution satellite data

AbstractSince 1999, Very High spatial Resolution satellite data (Ikonos-2, QuickBird and OrbView-3) represent the surface of the Earth with more detail. However, information extraction by multispectral pixel-based classification proves to have become more complex owing to the internal variability increase in the land-cover units and to the weakness of spectral resolution. Therefore, one possibility is to consider the internal spectral variability of land-cover classes as a valuable source of spatial information that can be used as an additional clue in characterizing and identifying land cover. Moreover, the spatial resolution gap that existed between satellite images and aerial photographs has strongly decreased, and the features used in visual interpretation transposed to digital analysis (texture, morphology and context) can be used as additional information on top of spectral features for the land cover classification.The difficulty of this approach is often to transpose the visual features to digital analysis.To overcome this problem region-based classification could be used. Segmentation, before classification, produces regions that are more homogeneous in themselves than with nearby regions and represent discrete objects or areas in the image. Each region becomes then a unit analysis, which makes it possible to avoid much of the structural clutter and allows to measure and use a number of features on top of spectral features. These features can be the surface, the perimeter, the compactness, the degree and kind of texture. Segmentation is one of the only methods which ensures to measure the morphological features (surface, perimeter.) and the textural features on non-arbitrary neighbourhood. In the pixel-based methods, texture is calculated with mobile windows that smooth the boundaries between discrete land cover regions and create between-class texture. This between-class texture could cause an edge-effect in the classification.In this context, our research focuses on the potential of land cover region-based classification of VHR satellite data through the study of the object extraction capacity of segmentation processes, and through the study of the relevance of region features for classifying the land-cover classes in different kinds of Belgian landscapes; always keeping in mind the parallel with the visual interpretation which remains the reference.Firstly, the results of the assessment of four segmentation algorithms belonging to the two main segmentation categories (contour- and region-based segmentation methods) show that the contour detection methods are sensitive to local variability, which is precisely the problem that we want to overcome. Then, a pre-processing like a filter may be used, at the risk of losing a part of the information. The “region-growing” segmentation that uses the local variability in the segmentation process appears to be the best compromise for the segmentation of different kinds of landscape.Secondly, the features calculated thanks to segmentation seem to be relevant to identify some land-cover classes in urban/sub-urban and rural areas. These relevant features are of the same type as the features selected visually, which shows that the region-based classification gets close to the visual interpretation. The research shows the real usefulness of region-based classification in order to classify the land cover with VHR satellite data. Even in some cases where the features calculated thanks to the segmentation prove to be useless, the region-based classification has other advantages. Working with regions instead of pixels allows to avoid the salt-and-pepper effect and makes the GIS integration easier.The research also highlights some problems that are independent from the region-based classification and are recursive in VHR satellite data, like shadows and the spatial resolution weakness for identifying some land-cover classes.ResumeDepuis 1999, les donnees satellitaires a tres haute resolution spatiale (IKONOS-2, QuickBird and OrbView-3) representent la surface de la terre avec plus de detail. Cependant, l’extraction d’information par une classification multispectrale par pixel devient plus complexe en raison de l’augmentation de la variabilite spectrale dans les unites d’occupation du sol et du manque de resolution spectrale de ces donnees. Cependant, une possibilite est de considerer cette variabilite spectrale comme une information spatiale utile pouvant etre utilisee comme une information complementaire dans la caracterisation de l’occupation du sol. De plus, de part la diminution de la difference de resolution spatiale qui existait entre les photographies aeriennes et les images satellitaires, les caracteristiques (attributs) utilisees en interpretation visuelle transposees a l’analyse digitale (texture, morphologie and contexte) peuvent etre utilisees comme information complementaire en plus de l’information spectrale pour la classification de l’occupation du sol.La difficulte de cette approche est la transposition des caracteristiques visuelles a l’analyse digitale. Pour resoudre ce probleme la classification par region pourrait etre utilisee. La segmentation, avant la classification, produit des regions qui sont plus homogene en elles-memes qu’avec les regions voisines et qui representent des objets ou des aires dans l’image. Chaque region devient alors une unite d’analyse qui permet l’elimination de l’effet « poivre et sel » et permet de mesurer et d’utiliser de nombreuses caracteristiques en plus des caracteristiques spectrales. Ces caracteristiques peuvent etre la surface, le perimetre, la compacite, la texture. La segmentation est une des seules methodes qui permet le calcul des caracteristiques morphologiques (surface, perimetre, …) et des caracteristiques texturales sur un voisinage non-arbitraire. Avec les methodes de classification par pixel, la texture est calculee avec des fenetres mobiles qui lissent les limites entre les regions d’occupation du sol et creent une texture interclasse. Cette texture interclasse peut alors causer un effet de bord dans le resultat de la classification.Dans ce contexte, la recherche s’est focalisee sur l’etude du potentiel de la classification par region de l’occupation du sol avec des images satellitaires a tres haute resolution spatiale. Ce potentiel a ete etudie par l’intermediaire de l’etude des capacites d’extraction d’objet de la segmentation et par l’intermediaire de l’etude de la pertinence des caracteristiques des regions pour la classification de l’occupation du sol dans differents paysages belges tant urbains que ruraux.