This paper presents the results of a preliminary comparison of two methods which are based on the mathematical approach, Mathematical Morphology and the Local Multifractal Description. Both methods are characterized by the need for input parameters defining. Those parameters are engaged in the model development, allowing to study the objects presented on the images in respect to the intended purpose of the analysis. Due to the initial visual interpretation and image objects descriptors definition, both methods are considered as a semi-automatic. Tests were performed based on the selected Brodatz textures and artificially generated noisy images, aiming at the indication of strong and weak points of both methods while applied to the edge/object detection tasks.
Post-processing is the last and often optional stage of land cover (LC) classification from satellite images. In the traditional approach, it is usually applied to remove the effect of “salt and pepper” from the classified image and also to standardize the image details according to the defined minimum mapping unit (MMU). The proposed post-processing method presented in this paper, has been used in the Sentinel-2 Global Land Cover (S2GLC) project. Its main goal is to remove or minimize typical classification errors that can appear in the classification output. Therefore, a set of functions that are able to improve the result of LC classifications has been developed. These include relatively simply defined rules that operate based on predefined threshold values of selected spectral channels, spectral indexes or auxiliary data. Additionally, logical relations between certain LC classes have been implemented. The proposed post-processing has been applied to the classification results of the S2GLC project and helped to improve LC classification in all test sites representing different parts of the globe.
Senitnel-2 satellites, as a part of European initiative - Copernicus - provide unique opportunity for Earth surface monitoring with high spatial, spectral and temporal resolution. Urban areas mapping is one of the domains where this kind of data may be very useful. In this research, usability of Sentinel-2 data for buildings detection has been analyzed using One-class SVM algorithm. For selected area of interest, number of issues have been analyzed, including automatic training data preparation, optimum classifier parameters and date of image acquisition selection.
Supervised classification of satellite images is performed based on utilization of reference training data. Therefore, the availability and quality of reference data highly influences the results and the course of the entire classification process. In the Sentinel-2 Global Land Cover (S2GLC) project Sentinel-2 images are classified using Random Forest (RF) algorithm powered by training points selected from existing low resolution land cover databases. This approach allows to perform the classification process in a highly automatic manner without much intervention of an operator. An alternative method for creating training dataset has been developed in order to ensure the implementation of the S2GLC classification in case of limited access to the required land cover databases or their low quality. The proposed method is a semi-automatic process initiated by an operator, who by a visual interpretation, indicates only several starting samples for the classes of interest. Afterwards, utilizing this limited set of initial training samples, hundreds or thousands of training samples with similar spectral characteristics are automatically selected from the image. Such a set of data, can be further used as an alternative source of training data for land cover classification on much greater scale. Comparing to the traditional approach, in which all samples or training areas are manually indicated, the developed method is very effective and also allows for processing data more rapidly. The semi-automatic training can be used as an alternative or supplement the training dataset applied in the S2GLC classification approach.
The general aim of this work was to elaborate efficient and reliable aggregation method that could be used for creating a land cover map at a global scale from multitemporal satellite imagery. The study described in this paper presents methods for combining results of land cover/land use classifications performed on single-date Sentinel-2 images acquired at different time periods. For that purpose different aggregation methods were proposed and tested on study sites spread on different continents. The initial classifications were performed with Random Forest classifier on individual Sentinel-2 images from a time series. In the following step the resulting land cover maps were aggregated pixel by pixel using three different combinations of information on the number of occurrences of a certain land cover class within a time series and the posterior probability of particular classes resulting from the Random Forest classification. From the proposed methods two are shown superior and in most cases were able to reach or outperform the accuracy of the best individual classifications of single-date images. Moreover, the aggregations results are very stable when used on data with varying cloudiness. They also enable to reduce considerably the number of cloudy pixels in the resulting land cover map what is significant advantage for mapping areas with frequent cloud coverage.
In this paper we present the results of research carried out to assess the usefulness of wavelet-based measures of image texture for classification of panchromatic VHR satellite image content. The study is based on images obtained from EROS-A satellite. Wavelet-based features are calculated according to two approaches. In first one the wavelet energy is calculated for each components from every level of decomposition using Haar wavelet. In second one the variance and kurtosis are calculated as mean values of detail components with filters belonging to the D, LA, MB groups of various lengths. The results indicate that both approaches are useful and complement one another. Among the most useful wavelet-based features are present not only those calculated with short or long filters, but also with the filters of intermediate length. Usage of filters of different type and length as well as different statistical parameters (variance, kurtosis) calculated as means for each decomposition level improved the discriminative properties of the feature vector consisted initially of wavelet energies of each component.