Similar land-form discovery: Complex absolute-value max pooling in complex-valued convolutional neural networks in interferometric synthetic aperture radar

Abstract

In a complex-valued convolutional neural network, its elementary unit consists of a complex-valued convolution layer and a complex pooling layer. The pooling layer has a variety in its dynamics. In this paper, we propose complex absolute-value max pooling to extract complex-amplitude feature patterns meaningful for discovery and/or adaptive classification of land form in interferometric synthetic aperture radar (InSAR). Experimental examination into amplitude and phase values in convolutional kernels reveals that useful land-shape features emerge through self-organization in high-magnitude kernels, which suggests that the proposed dynamics is successful in extracting important features.