Remote Sensing Data and Classification Algorithms

Dear Colleagues,

Decision-making systems represent a growing and innovative field of research, covering a wide range of applications. The development of these systems is based on reliable remote sensing data and efficient classification algorithms. The data exploited in these systems must be accurate regardless of the application field. Thus, the analysis and interpretation of remote sensing images must be objective and relevant, considering their physical nature. To effectively achieve these objectives and to meet expectations, classification algorithms must provide optimal partitions according to one or more criteria. Despite numerous existing methods in the literature, achieving accurate image partitioning remains a challenge. Indeed, to resolve this problem, one must take into account the diversity of objects and their classes in an image, the partial and limited knowledge of ground truth data, and spatial resolution, regardless of the remote sensing data acquisition system (satellite, aircraft, UAV). Classification algorithms must therefore be adapted to the nature of the data for better image partitioning. The classification of remote sensing data remains an open and complex problem for the three categories of supervised, semi-supervised, and unsupervised classification methods. Thus, the development of an efficient classification algorithm remains the main contributor to the quality of decision-making systems.

Prof. Dr. Kacem Chehdi
Guest Editor

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