As the number of spectral bands of high spectral resolution data increases, the capability to detect more detailed classes should also increase, and the classification accuracy should increase as well. Often the number of labeled samples used for supervised classification techniques is limited, thus limiting the precision with which class characteristics can be estimated. As the number of spectral bands becomes large, the limitation on performance imposed by the limited number of training samples can become severe. A number of techniques for case-specific feature extraction have been developed to reduce dimensionality without loss of class separability. Most of these techniques require the estimation of statistics at full dimensionality in order to extract relevant features for classification. If the number of training samples is not adequately large, the estimation of parameters in high dimensional data will not be accurate enough. As a result, the estimated features may not be as effective as they could be. This suggests the need for reducing the dimensionality via a preprocessing method that takes into consideration high dimensional feature space properties. Such reduction should enable the estimation of feature extraction parameters to be more accurate. Using a technique referred to as Projection Pursuit, such an algorithm has been developed. This technique is able to bypass many of the problems of the limitation of small numbers of training samples by making the computations in a lower dimensional space, and optimizing a function called the projection index. A current limitation on this method is that as the number of dimensions increases, it is highly probable to find a local maximum 1 Work reported herein was funded in part by NASA Grant NAGW-3924.

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The university learning classroom, in addition to a space for activities and architectural object, has a direct impact on the academic motivation, well-being and social relationships of the students. Thus, the link between the university classroom and the management of the socio-educational well-being of the student, in accordance with the principles of well-being theory, is a challenge that the current university must manage. The progress of worldwide research on this topic has been studied during the period 2004–2018. For this aim, a bibliometric study of 1982 articles has been applied. The results provide data of the scientific productivity of the journals, authors, institutions and countries that contribute to this research. The evidence reveals growing interest, especially in the last six years. The main category is Social Sciences. The most productive journals are Computers and Education, American Journal of Pharmaceutical Education, and Theory into Practice. The author with most articles is Reddy, from Rutgers University. The most productive institution is the University of Virginia. The United States is the country with most academic publications, citations and with most international collaborations in its works. Worldwide research has followed an increasing trend, with optimum publication levels in latest years.

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Background: The aim of the present study was to evaluate teacher perceptions on the training received in intercultural education. Methods: The article presents a quantitative, non- experimental and ex-post-facto type of research; directed to inquire about the perceptions of the teachers of primary education in Andalusia (Spain) in relation to the intercultural training received. Based on the descriptive survey method, two questionnaires were administered to a sample composed of 320 students and 80 teachers. Results: The results show certain strengths of the training teacher programs in the field of interculturality (encouragement of reflection, participation and collaboration …), as well as weaknesses (decontextualization, inflexibility, primacy of theoretical learning, non-transversal character, etc.). Conclusions: Despite strengths, intercultural teacher training continues to be a challenge in Andalusia.