… used in this paper without any other processing. In the second case, the InpectionV-4 … In
the current study, a network with the multi-channel convolutions and dual ASPP modules is …

… using neural networks whose behavior can be interpreted. It is well-known that neural networks
… To this end, we suggest that each neuron should process a different information aspect …

… For remote sensing data, labeling process is expensive, and only a few training samples
are available usually. To overcome this problem, a random seed is generated for rotating with …

… Third, the sheer volume of remotely sensed data to be processed must be considered.
Therefore a suitable retrieval strategy must have the ability to carry out spatially and temporally …

… Good introductions to neural networks are provided in texts … The use of artiŪcial neural
networks for remote sensing data … e cient at processing vast quantities of data from a variety of di …

… Over the past decade, research on land cover mapping and inversion mainly focuses on the
algorithm development of RS data (acquired by the single sensor) processing and analysis […

… processing steps, such as classification or object extraction in general. As a wide range of
remote sensing applications use … nor desirable we approach the multichannel dataset as a set …

… Through the deep-learning approach, the deep network could learn to simulate the
meteorological knowledge from the enormous data and training process. Nevertheless, learning …

… from characteristics of backscattered electromagnetic field. The multichannel methods using
… This report gives brief radiophysical background of multichannel remote sensing. The basic …

… remote sensing data. This indicates the significance of developing a pipeline compatible
with multi-channel … , is a process that produces more training samples from existing training …