New advances in digital image processing

Abstract

Enhancement of noisy image data is a very challenging issue in many research and application areas. In the last few years, non-linear filters, feature extraction, high dynamic range imaging methods based on soft computing models have been shown to be very effective in removing noise without destroying the useful information contained in the image data. In this paper new image processing techniques are introduced in the above mentioned fields, thus contributing to the variety of advantageous possibilities to be applied. The main intentions of the presented algorithms are (1) to improve the quality of the image from the point of view of the aim of the processing, (2) to support the performance, and parallel with it (3) to decrease the complexity of further processing using the results of the image processing phase.

The dynamics of land use-land cover changes for the years 1984, 1992, 2001 and 2014 in Mutasa district, Zimbabwe

“…Land use, by contrast, describes activities that take place on the land and represent the current use of the land resource (Fonji and Taff, 2014). Land use and land cover (LULC) change has been recognized as an important driver of global environmental change at spatial and temporal scales (Varkonyi-Koczy, 2010;Zewdie and Csaplovies, 2015). Land use and land cover changes are intertwined in many ways with other environmental issues, such as climate change, carbon cycle, loss of biodiversity, sustainability of agriculture and provision of safe drinking water (Ademiluyi, 2008;Lambin and Meyfroidt, 2011).…”

“…Maximum likelihood supervised image classification in ENVI 4.7 software was used to determine the LULC classes for the different years. A false colour composite with band combination(Awotwi, 2009;Guo et al, 2010;Varkonyi- Koczy, 2010) in the Red; Green; Blue format was used for the classification. Accuracy assessment of the classified maps was evaluated using at least 100 sample points randomly spread around the study area for each image by computing an Error Matrix.…”