  Synthetic aperture radar (SAR) is widely used for ground surface
classification since it utilizes information on vegetation and soil unavailable
in optical observation. Image classification often employs convolutional neural
networks. However, they have serious problems such as long learning time and
resolution degradation in their convolution and pooling processes. In this
paper, we propose complex-valued reservoir computing (CVRC) to deal with
complex-valued images in interferometric SAR (InSAR). We classify InSAR image
data by using CVRC successfully with a higher resolution and a lower
computational cost, i.e., one-hundredth learning time and one-fifth
classification time, than convolutional neural networks. We also conduct
experiments on slope angle estimation. CVRC is found applicable to quantitative
tasks dealing with continuous values as well as discrete classification tasks
with a higher accuracy.
