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Nowadays, operational earth observation satellites provide a large variety of multi-sensor, multi-temporal and multi-modal data. Signals generated by miscellaneous sensors needs to be sampled, filtered, fused, stored, and interpreted (Yu & Christakos, 2010). Each of these data-processing steps must be conducted in an efficient way to conserve data fidelity. Amidst these research areas, remote sensing community is notably interested in studying multi-source images fusion issues. Over last past decades, information fusion has emerged to manage large amounts of multi-source data in the military field (Mahler, 2007). Recently, substantial amount of research has been dedicated to data fusion techniques development and adaptation for signal and images processing applications. Therefore, data fusion is now largely adopted in several fields including, but are not limited to, satellite and aerial imaging, medical imaging, sonar and radar, robotics, etc. (Stathaki, 2008). Until recently, images fusion has become a worthy tool in remote sensing image processing and received great attention for satellite image interpretation. Motivations for images fusion are numerous and predominantly justified by application issue (Farah et al., 2008b). Fusion techniques aim to produce an enhanced single view with extended information content by combining intelligently multi-modality data coming from different sources. However, remote sensing images are characterized by their unique spectral, spatial, temporal and directional dimensions depending fundamentally on the nature of the corresponding sensor (Farah et al., 2010). Thus, image fusion can be looked with different points of view; each one is designed to answer specific research requirements and to meet a particular need. Typically, for an efficient fusion, some questions must been answered before deciding about the fusion approach: What is the objective of image fusion? Which types of data are the most useful? What is the most “appropriate” method of fusion to achieve study goals? What technique is used for results assessment? (Pohl & Van Genderen, 1998). Moreover, numerous challenging research issues are related to developing new approaches for remotely sensed signals managing and interpretation. In most actual researches, sensors must operate in an unfriendly environment with many complications. Therefore, an image processing method must be able to deal effectively with limited resources and

A Multi Views Approach for Remote Sensing Fusion Based on Spectral, Spatial and Temporal Information