We have developed and applied a spatiotemporal fusion framework that uses different fusion strategies across time frames (temporal fusion) as well as between sensors (spatial fusion). We have developed, at the feature level, new and different fusion strategies (additive and minmax fusion) in addition to the traditional strategies (multiplicative, min, and max fusion). These different fusion strategies are compared and predicted by their receiver operating characteristics performance in the likelihood-reading domain. Furthermore, by applying the additive fusion strategy, we have developed methods for adaptive sensor weighting using reliability functions to improve fusion performance. Our simulated test and analysis results show that temporal fusion (as well as spatial fusion) can considerably improve target classification, and also show that the adaptive sensor weighting using reliability functions can significantly improve fusion performance when one sensor is much more reliable than the other. Finally, we propose an optimal integrated spatiotemporal multiple sensor fusion system that includes two new processors: the adaptive processor and the fusion selection processor.