Maximum entropy neural networks for feature enhanced imaging with collaborative microwave multi-sensor data fusion

We present a collaborative neural network (NN) computing-oriented approach for feature enhanced reconstruction of microwave remote sensing (RS) imagery via sensor data fusion. Two reconstruction/fusion frameworks are proposed and featured. Both unify the maximum entropy and descriptive experiment design regularization (DEDR) paradigms but employ different NN-based fusion (NNF) strategies. The first one addressed as RS-NNF
⁽¹⁾
aggregates the adaptively weighted DEDR-structured individual sensor image recovery objective functions, while the second one addressed as RS-NNF
⁽²⁾
performs cooperative multi-sensor statistical recovery performances enhancement-oriented fusion. The simulations corroborate superiority of both proposed technics over the conventional non-collaborative RS image fusion with RS-NNF
⁽²⁾
on top.