Demand for up-to-date geographic data is increasing due to the fast growth of Geographic Information System (GIS). The process of updating GIS database can be subdivided into three steps. In the first step, changes of the landscape must be detected. In the second step, different data sources must be used to add further attributes. In the last step, the changes with all additional information have to be stored into a GIS database. In this paper, a new approach of change detection for updating GIS database by using remote sensing image is presented. This approach of change detection is based on post-classification comparison and spatial reasoning. Firstly, two different classification methods of satellite imagery, one of pixel-based classification and other of object-oriented classification, have been used. The already existing GIS data are used for deriving the training areas for the classification. The fusion of the results of classification methods yields a good comparison of classification to realizing the change detection between the remote sensing data and GIS data. Secondly, a qualitative approach using spatial reasoning system (Region Connection Calculus RCC8) to representing GIS data and satellite image content in addition to the quantitative information is performed. After a step of segmentation of the satellite image, the spatial reasoning system RCC8 is used to extract and identify the spatial relations (topological and orientation) between the GIS objects and the objects of the results of segmentation. The topological relation describes how the boundaries of two objects relate to each other, while the orientation relation describes where the objects are placed relative to one another. The fusion of the results of post-classification comparison and the spatial reasoning system can detect changes at the level of thematic data, geometric and spatial relations between objects in satellite image and the GIS data. Finally, the changes with all information are returned and stored into the GIS database. This approach is tested with Corine Land Cover (CLC) database and SPOT satellite images.

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Multi-spectral images are crucial to detect and to understand phenomena in marine observation. However, in coastal areas, these phenomena are complex and their analyze requires multi-spectral images with both a high spatial and spectral resolution. Unfortunately, no satellite is able to provide both at the same time. As a consequence, multi-sharpening techniques-a.k.a. fusion or superresolution of multi-spectral and/or hyper-spectral images-were proposed and consist of combining information from at least two multi-spectral images with different spatial and spectral resolutions. The fused image then combines their best characteristics. Various methods-based on different strategies and tools-have been proposed to solve this problem. This article presents a comparative review of fusion methods applied to Sentinel-2 MSI (13 spectral bands with a spatial resolution ranging from 10 to 60 m) and Sentinel-3 OLCI (21 spectral bands with a spatial resolution of 300 m) images. Indeed, both satellites are extensively used in marine observation and, to the best of the authors' knowledge, the fusion of their data was partially investigated (and not in the way we aim to do in this paper). To that end, we provide both a quantitative analysis of the performance of some state-of-the-art methods on simulated images, and a qualitative analysis on real images.

Topological relations have played important roles in spatial query, analysis and reasoning in Geographic Information Systems (GIS) and geospatial databases. The topological relations between crisp, uncertain and fuzzy spatial regions based upon the 9-intersections model have been identified. The research issue of topological relations, particularly, between spatial regions with uncertainties, has gained a lot of attention during the past two decades. However, the formal representation and calculation of the topological relations between uncertain regions is still an open issue and needs to be further developed. The paper provides a theoretical framework for modeling topological relations between uncertain spatial regions based upon a new uncertain topological model called the Uncertain Intersection and Difference (UID) Model. In order to derive all topological relations between two spatial regions with uncertainties, the spatial object of type Region (A) is decomposed in four components: the Interior, the Interior's Boundary, the Object's Boundary, and the Exterior's Boundary of A. By use of this definition of spatial region with uncertainties, new 4*4-Intersection and Uncertain Intersection and Difference (UID) models are proposed as a qualitative model for the identification of all topological relations between two spatial regions with uncertainties. These two new models are compared with other models studied in the literature. 152 binary topological relations can be identified by these two models. Then, the topological complexity and distance of the 152 relations will be study in details by using the UID model. Based upon this study of topological complexity and distance, a conceptual neighborhood graph for the 152 relations can be obtained. Examples are provided to illustrate the utility of these two models presented in this paper with results which can be applied for modeling GIS, geospatial databases and satellite image processing.

One of the necessary basic concepts for the spatial data analysis in GIS is to determine the spatial relations between arbitrary geographical objects. In a two-dimensional space (IR 2 ), most existing topological models can distinguish the eight topological relations between two spatial regions A and B. These eight relations are written in the traditional form of the spatial reasoning system RCC8: DC, EC, EQ, PO, TPP, TPPi, NTPP and NTPPi. Because of the complexity of topological relations between geographic regions, it is difficult for these models to describe in detail the topological relations by defining the separation number of lines and points that characterize these relations, and which is very important to enrich the spatial relations of system RCC8. To overcome the insufficiency in existing models, the extension of the Intersection and Difference (ID) model has the ability to describe in detail the topological relations of system RCC8. In our study, we focus our work on the four relations EC, PO, TPP and TPPi which can be described by the boundary-boundary intersection operator ∂A∩∂B. The main contributions are these four detailed relations which are written and described in the general form EC mL, nP, kR , PO mL, nP, jRI, kR , TPP mLT, nPT, kR and TPPi mLT, nPT, kR . Then, we develop definitions for the generalization of these detailed relations. Finally, examples are provided to illustrate the generalization of these new detailed spatial relations.