Kick: Shift-N-Overlap Cascades of Transposed Convolutional Layer for Better Autoencoding Reconstruction on Remote Sensing Imagery

Abstract

A convolutional autoencoder is an essential deep neural model architecture for understanding and predicting large-scale and widespread multi-dimensional information such as remote sensing imagery. To training a convolutional autoencoder, an automatic image reconstruction from input data and evaluation is repeatedly performed to achieve optimal reconstruction performance. Checkerboard artifacts, which are frequently produced on output images and lead to degraded image quality, are a significant issue during image reconstruction using a convolutional autoencoder. To remedy this coarse visual saliency issue during model training, we propose the `Kick' deconvolutional layer - a cascaded transposed convolutional layer with pixel shifting and overlapping for checkerboard pattern smoothing. By using pixel-shifted identity convolutional layers, we improved image reconstruction performance using fewer trainable decoder parameters than previously suggested models without losing reconstruction capability. Moreover, our proposed layer can be used with any type of convolutional autoencoder, including typical convolutional autoencoders and adversarial autoencoders. To evaluate an image reconstruction performance of our suggested deconvolutional layer, we used a dataset containing 12 years of geostationary satellite observation data of East Asia.

Keywords

• Deconvolutional layer
• convolutional autoencoder
• adversarial autoencoder
• image reconstruction
• checkerboard artifact
• satellite observation