Automated identification of land cover type using multispectral satellite images

• Construction of a multi-spectral descriptor based on reflectance values is proposed.
• Data set formation is semi-automatized with software developed for data labelling.
• Classification methods for automatic landscape type recognition are compared.
• Accuracies of landscape classification on evaluation set are higher than 92%.
• Logistic regression provides the highest accuracy and fastest execution.

Detection of specific terrain features and vegetation, referenced as a landscape classification, is an important component in the management and planning of natural resources. The different land types, man-made materials in natural backgrounds and vegetation cultures can be distinguished by their reflectance. Although remote sensing technology has great potential for acquisition of detailed and accurate information of landscape regions, the determination of land-use data with high accuracy is generally limited by the availability of adequate remote sensing data, in terms of spatial and temporal resolution, and digital image analysis techniques. Therefore, remote sensing with multi-spectral or/and hyper-spectral data derived from various satellites in combination with topographic variables is a valuable tool in landscape type classification. The different methods based on reflectance data from multi-spectral Landsat satellite image sets are used for automatic landscape type recognition. In order to characterize reflectance of landscape types represented in an image, construction of a multi-spectral descriptor, as a vector of acquired reflectance values by wavelength bands, is proposed. The applied algorithms for landscape type classification (artificial neural network, support vector machines and logistic regression) have been analysed and results are compared and discussed in terms of accuracy and time of execution.