Computational Intelligence and its Application in Remote Sensing

摘要: Remote sensing observations provide a new global perspective of the Earth environment. Measurements from airborne and space borne sensor systems help scientists gain better understanding complex interactions between Earth’s atmosphere, oceans, ice regions land surfaces, as well human-induced change due to population growth human activities. These remote measurements are widely used in geographical, meteorological, environmental studies. Technological advancements have resulted an increase number observation platforms capabilities (e.g., spectral spatial resolution). This trend will continue soon produce unprecedented volume data. Information extracted these datasets support national research agendas applications that exert ever-increasing requirement for shorter processing times greater data algorithm accuracies. Hence, advanced mathematical techniques needed effectively analyze generated rapidly growing technology. For most geophysical retrieval algorithms, adding additional information improve measurement situ properties is not simple task because nonlinear nature problem computational difficulties. Moreover, current generally require high level scientific knowledge physical system accurately remotely sensed In contrast, intelligence (CI) such artificial neural networks, genetic fuzzy logic systems, capability examine without requiring detailed about underlying system. example, CI been estimate bio-optical parameters coastal aquatic environments by employing special features ability learn data, adaptive behavior, handling non-linear flexibility towards choice inputs, resilience against noise. satellite ocean color, algorithms based on regression (or empirical) models use power and/or cubic polynomials relate ratios reflectance chlorophyll