# A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation
## 4 Data Fusion Techniques Applied to Remote Sensing Data
The strength of the data fusion approach can be appraised when considering different types of instruments. For instance, the fusion of multispectral satellite imagery and LiDAR data allows for an accurate object recognition and classification, when the spectral signature is supported by the altimetric variations provided by the digital elevation model [287; 301]. Another useful multi-instrument data combination relies on fusing optical images with SAR data, which, being based on radar backscatter measurements, extend the information on the observed scene acquired in the VIS to NIR spectral bands, especially when the cloud coverage causes gaps in the time series of any continuous monitoring [284; 286; 287]. On the other hand, another convenient data fusion technique is related to the revisit capability characterizing spaceborne instruments, which can acquire the same ground area with a frequency of up to a daily basis (e.g., small satellites in constellation configuration, cf. Table 2) [286; 302]. The spatio-temporal fusion allows to _fuse_ low spatial/high temporal resolution data with high spatial/low temporal resolution data, providing multitemporal high spatial-resolution data. This data fusion, which benefits from the data acquired in the same acquisition mode reducing any co-registration issues, allows for applying change detection techniques to monitor highly dynamic processes, including hazard handling [284; 286; 287; 292]. Similarly, the spectral and/or back-scattering data can also be integrated with the temporal data [286]. The fusion of data acquired at different times from the same sensor or different sensors, such as VIS-NIR optical, SAR, and LiDAR instruments, enables the creation of a comprehensive 4D representation of the observed ground scene [284; 286; 302]. The initial set of data used for fusion and/or the degree of information to be extracted from the final output determine the data fusion level: (i) raw data level (e.g., pan-sharpening), where the input data from the various sources are integrated into one single, higher spatial resolution data; (ii) feature level (e.g., classification and change detection), where surface features can be extracted and re-arranged; and (iii) decision level (e.g., fusion of data acquired by multiple sensors--optical, SAR, and LiDAR), where the results from multiple algorithms are combined and fused into one single, fused decision [284, 287, 288]. Such an increase in the degree of data fusion level brings the current trend out, where this approach has become a fundamental tool for data analysis, partly due to the increase in the number of the launched satellites (eventually in constellation configuration), to the improvement of the spatial and spectral resolutions of space sensors, and to the opportunity of validating and integrating the remote sensing data with ground data [284]. In this framework, the DORA project can greatly benefit from the data fusion techniques outlined above. On one hand, the current telescope prototype under development is equipped with an optical sensor operating in the VIS to TIR spectral bands, for which the various study cases requested a \(\sim\)300-400 nm panchromatic filter and several \(\sim\)50 nm multispectral filters. On the other hand, a similar instrument equipped instead with an SAR antenna is under study, as well as the possibility of having a constellation of small platforms flying deployable optics. At the present state of activities, with only the optical sensor under development, we will consider here only the pan-sharpening technique for the data fusion application. So far, large satellites, such as the WorldView, have optical sensors with very high resolutions (of the order of a meter or less) and multiple spectral bands, which allow for the pan-sharpening technique.