Preparation of land cover / land use maps for large areas, based on automatic classification of high-resolution satellite data is the objective of many application programmes, e.g. GSE Land Monitoring Services. The crucial point for this kind of activity is to apply optimal classification approach, which will ensure high class recognition accuracy and classification repeatability. Among differe...

Land cover of Finnish Lapland was classified to 16 land cover classes using optical IRS LISS, Spot XS and MODIS satellite images, ancillary GIS data and decision tree classifier. The aim of this study was to test decision tree classifier for land cover classification and study the effects of its parameters to classification result. In the best case, the overall accuracy was about 68% for all 16...

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data,...

Study deals with the land cover dynamics analysis using remote sensing data over urban, suburban area in northern Belarus (Polotsk and Novopolotsk cities and surroundings) over the period 1994 – 2002. SPOT 3 and 5 images are used for the study. Land cover change detection is conducted using image differencing and post-classification comparison methods. Several classification methods are tested ...

Automatic image classification often fails at separating a large number of land cover classes that punctually may present similar spectral reflectances. To improve the classification accuracy in such situations, multi-temporal satellite data has proven to be valuable auxiliary information. In this paper, we present a study exploring the usefulness of intra-annual satellite images timeseries for...

The European CORINE land cover mapping scheme is a standardized classification system with 44 land cover and land use classes. It is used by the European Environment Agency to report large-scale land cover change with a minimum mapping unit of 5 ha every six years and operationally mapped by its member states. The most commonly applied method to map CORINE land cover change is by visual interpr...

Given the advances in remotely sensed imagery and associated technologies, several global land cover maps have been produced in recent times including IGBP DISCover, UMD Land Cover, Global Land Cover 2000 and GlobCover 2009. However, the utility of these maps for specific applications has often been hampered due to considerable amounts of uncertainties and inconsistencies. A thorough review of ...

The savannas of Southern Africa are an important dryland ecosystem as they cover up to 54% of the landscape and support a rich variety of biodiversity. This paper evaluates landscape change in savanna vegetation along Chobe Riverfront within Chobe National Park Botswana, from 1982 to 2011 to understand what change may be occurring in land cover. Classifying land cover in savanna environments is...

Ensemble classification is an emerging approach to land cover mapping whereby the final classification output is a result of a ‘consensus’ of classifiers. Intuitively, an ensemble system should consist of base classifiers which are diverse i.e. classifiers whose decision boundaries err differently. In this paper ensemble feature selection is used to impose diversity in ensembles. The features o...

Satellite image classification is one of the most significant applications in remote sensing. Remote sensing data obtained from different optical sensors have been commonly used to characterize and quantity land information. However, conventional optical remote sensing is limited by weather conditions. Synthetic aperture radar (SAR), with the allweather and all-time advantages, is important in ...