Satellite remote sensing is a universal tool to investigate the different areas of Earth and environmental sciences. The advancement of the implementation capabilities of the optoelectronic devices which are long-term-tested in the laboratory and the field and are mounted on-board of the remote sensing platforms further improves the capability of instruments to acquire information about the Earth and its resources in global, regional and local scales. With the start of new high-spatial and spectral resolution satellite and aircraft imagery new applications for large-scale mapping and monitoring becomes possible. The integration with Geographic Information Systems (GIS) allows a synergistic processing of the multi-source spatial and spectral data. Here we present the results of a joint project DFNI I01/8 funded by the Bulgarian Science Fund focused on the algorithms of the preprocessing and the processing spectral data by using the methods of the corrections and of the visual and automatic interpretation. The objects of this study are lineaments. The lineaments are basically the line features on the earth's surface which are a sign of the geological structures. The geological lineaments usually appear on the multispectral images like lines or edges or linear shapes which is the result of the color variations of the surface structures. The basic geometry of a line is orientation, length and curve. The detection of the geological lineaments is an important operation in the exploration for mineral deposits, in the investigation of active fault patterns, in the prospecting of water resources, in the protecting people, etc. In this study the integrated approach for the detecting of the lineaments is applied. It combines together the methods of the visual interpretation of various geological and geographical indications in the multispectral satellite images, the application of the spatial analysis in GIS and the automatic processing of the multispectral images by Canny algorithm, Directional Filter and Neural Network. Landsat multispectral images of the Eastern Rhodopes in Bulgaria for carrying out the procedure are used. Canny algorithm for extracting edges represents series of filters (Gaussian, Sobel, etc.) applied to all bands of the image using the free IDL source. Directional Filter is applied to sharpen the image in a specific preferred direction. Another method is the Neural Network algorithm for recognizing lineaments. The lineaments are effectively extracted using different methods of automatic. The results from the above mentioned methods are compared to the results derived from the visual interpretation of satellite images and from the geological map. In conclusion, the rose diagrams of the distribution of the geological lineaments and the maps of their density are completed.
In the present work we applied a recently developed procedure for multidimensional data clustering to multispectral
satellite images. The core of our approach lays in projection of the multidimensional image to a two dimensional space.
For this purpose we used extensively investigated family of recurrent artificial neural networks (RNN) called “Echo state
network” (ESN). ESN incorporates a randomly generated recurrent reservoir with sigmoid nonlinearities of neurons
outputs. The procedure called Intrinsic Plasticity (IP) that is aimed at reservoir output entropy maximization was applied
for adapting of reservoir steady states to the multidimensional input data. Next we consider all possible combinations
between steady states of each two neurons in the reservoir as two-dimensional projections of the original
multidimensional data. These low dimensional projections were subjected to subtractive clustering in order to determine
number and position of data clusters. Two approaches to choose a proper projection among the all possible combinations
between neurons were investigated. The first one is based on the calculation of two-dimensional density distributions of
each projection, determination of number of their local maxima and choice of the projections with biggest number of
these maxima. The second one applies clustering to all projections and chooses those with maximum number of clusters.
Multispectral data from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) instrument are used in this work. The
obtained number and position of clusters of a multi-spectral image of a mountain region in Bulgaria is compared with the
regional landscape classification.