Integration of multi-seasonal remotely-sensed images for improved landuse classification of a hilly watershed using geographical information systems

1996 ◽  
Vol 17 (9) ◽  
pp. 1679-1688 ◽  
Author(s):  
J. ADINARAYANA ◽  
N. RAMA KRISHNA
2017 ◽  
Vol 43 (2) ◽  
pp. 50-55 ◽  
Author(s):  
Claudio MENEGHINI ◽  
Claudio PARENTE

Nowadays Marine Geographical Information Systems (MGIS) play an essential role in several research activities, the most part of them related to solve Geoscience problems. The nautical maps, containing most of the information used by the marine navigators, are used as cartographic base of MGIS and widely referred to Mercator projection. Remotely sensed images can be introduced in MGIS to improve the study outcomes even if they are in a different cartographic representation (generally Universal Transverse of Mercator, UTM). The adaptation of already georeferred remotely sensed images to Mercator projection requires particular care, moreover when also geodetic data are different (i.e. local datum and global datum). This paper is aimed to offer an easy-to-use work-flow that could be adopted every time remotely sensed images are to be introduced in MGIS and overlaid to nautical maps. Particularly the work addresses the implementation and evaluation of reprojection of Landsat 8 imageries, regarding both the gulfs of Naples and Salerno (Italy): a transformation from UTM WGS84 to Mercator Roma40 is applied. The result accuracy encourages the adoption of the proposed work-flow.


10.1068/b1296 ◽  
2004 ◽  
Vol 31 (1) ◽  
pp. 39-49 ◽  
Author(s):  
Thomas Hatzichristos

This paper presents a methodology for the creation of homogeneous demographic regions with geographical information systems (GIS) and computational intelligence. The proposed method is unsupervised fuzzy classification performed by neural networks using the fuzzy Kohonen algorithm. GIS technology offers a powerful set of tools for the input, management, and output of data, whereas computational intelligence is used for the analysis and the classification of the data. The proposed methodology is applied to the municipality of Athens, in Greece. Finally the advantages and disadvantages of the approach are discussed.


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