No abstract

The popularity of Artificial Neural Network (ANN) methodology has been growing in a wide variety of areas in geodesy and geospatial sciences. Its ability to perform coordinate transformation between different datums has been well documented in literature. In the application of the ANN methods for the coordinate transformation, only the train-test (hold-out cross-validation) approach has usually been used to evaluate their performance. Here, the data set is divided into two disjoint subsets thus, training (model building) and testing (model validation) respectively. However, one major drawback in the hold-out cross-validation procedure is inappropriate data partitioning. Improper split of the data could lead to a high variance and bias in the results generated. Besides, in a sparse dataset situation, the hold-out cross-validation is not suitable. For these reasons, the K-fold cross-validation approach has been recommended. Consequently, this study, for the first time, explored the potential of using K-fold cross-validation method in the performance assessment of radial basis function neural network and Bursa-Wolf model under data-insufficient situation in Ghana geodetic reference network. The statistical analysis of the results revealed that incorrect data partition could lead to a false reportage on the predictive performance of the transformation model. The findings revealed that the RBFNN and Bursa-Wolf model produced a transformation accuracy of 0.229 m and 0.469 m, respectively. It was also realised that a maximum horizontal error of 0.881 m and 2.131 m was given by the RBFNN and Bursa-Wolf. The obtained results per the cadastral surveying and plan production requirement set by the Ghana Survey and Mapping Division are applicable. This study will contribute to the usage of K-fold cross-validation approach in developing countries having the same sparse dataset situation like Ghana as well as in the geodetic sciences where ANN users seldom apply the statistical resampling technique.

Prior any satellite technology developments, the geodetic networks of a country were realized from a topocentric datum, and hence the respective cartography was performed. With availability of Global Navigation Satellite Systems-GNSS, cartography needs to be updated and referenced to a geocentric datum to be compatible with this technology. Cartography in Ecuador has been performed using the PSAD56 (Provisional South American Datum 1956) systems, nevertheless it's necessary to have inside the system SIRGAS (SIstema de Referencia Geocéntrico para las AmericaS). This transformation between PSAD56 to SIRGAS use seven transformation parameters calculated with the method Helmert. These parameters, in case of Ecuador are compatible for scales of 1:25 000 or less, that does not satisfy the requirements on applications for major scales. In this study, the technique of neural networks is demonstrated as an alternative for improving the processing of UTM planes coordinates E, N (East, North) from PSAD56 to SIRGAS. Therefore, from the coordinates E, N, of the two systems, four transformation parameters were calculated (two of translation, one of rotation, and one scale difference) using the technique bidimensional transformation. Additionally, the same coordinates were used to training Multilayer Artifi cial Neural Network -MANN, in which the inputs are the coordinates E, N in PSAD56 and output are the coordinates E, N in SIRGAS. Both the two-dimensional transformation and ANN were used as control points to determine the differences between the mentioned methods. The results imply that, the coordinates transformation obtained with the artifi cial neural network multilayer trained have been improving the results that the bidimensional transformation, and compatible to scales 1:5000.

No abstract

No abstract

No abstract

No abstract

Multispectral satellite images are tools that allow the analysis of phenomena developed on the Earth's surface without being in contact. It is a raster model so it is possible to decompose it into a digital signal. There is a certain data that presents alterations (noise) due to errors caused by the sensors, atmospheric conditions, among others. Such examples affect its use and its derived products. Satellite images by their nature present difficulty in their processing and handling due to the considerable weight they have; whose problem justified the present work. The objective is to minimize white noise and to compress the image with the least possible loss of information through the Multiresolution Analysis (MRA) technique and Wavelet transformation. The images worked belong to the National Recreation Area "El Boliche" (Ecuador) that is next to the Cotopaxi volcano. Through a standard deviation evaluation of the obtained wavelet coefficients, the order of the "Discrete Wavelet Transform" (DWT) was established in the Daubechies (db) and Haar families. With db3 level 4, obtained a compression of 11.268% in respect to the original weight and with Haar level 4 11.288% as the best results. The wavelet db is more effective than the Haar type for the treatment of multispectral satellite images in the elimination of white noise and compression by means of the MRA, with a reconstruction of the signal without loss of information due to the type of wavelet used, which is evidenced in the image.