Land-cover classification in remote-sensing images using structured neural networks

This paper proposes the application of structured neural networks to land-cover classification in remote-sensing images (in particular, multisensor images are considered). Purpose of our approach is to give a criterion for network architecture definition and to allow the interpretation of the "network behaviour'. The first result aims to avoid a cumbersome trial-and-error process; the latter one can be used to obtain information about the relevance of sensors and related bands to land-cover classification. First of all, the architecture of structured networks is tailored to a multisensor classification problem. Then, they are trained and transformed into "simplified networks' which allow one to evaluate the relevance of sensors and related bands. Experimental results on a multisensor data set related to an agricultural area are reported. -from Author

Classifier fusion for multisensor image recognition

Classifier fusion approaches are receiving increasing attention for their capability of improving classification performances. At present, the usual operation mechanism for classifier fusion is the "combination" of classifier outputs. Improvements in performances are related to the degree of "error diversity" among combined classifiers. Unfortunately, in remote-sensing image recognition applications, it may be difficult to design an ensemble that exhibit an high degree of error diversity. Recently, some researchers have pointed out the potentialities of "dynamic classifier selection" (DCS) as an alternative operation mechanism. DCS techniques are based on a function that selects the most appropriate classifier for each input pattern. The assumption of uncorrelated errors is not necessary for DCS because an "optimal" classifier selector always selects the most appropriate classifier for each test pattern. The potentialities of DCS have been motivated so far by experimental results on ensemble of classifiers trained using the same feature set. In this paper, we present an approach to multisensor remote-sensing image classification based on DCS. A selection function is presented aimed at choosing among classifiers created using different feature sets. The experimental results obtained in the classification of remote-sensing images and comparisons with different combination methods are reported