Recurrent Multiresolution Convolutional Networks for VHR Image Classification

TL;DR

This paper introduces FuseNet and ReuseNet, innovative end-to-end recurrent multiresolution convolutional networks that improve VHR satellite image classification by jointly addressing fusion, feature extraction, and regularization.

Contribution

The study presents a novel unified framework with FuseNet and ReuseNet that outperform traditional multi-stage methods in VHR image classification tasks.

Findings

FuseNet and ReuseNet outperform baseline methods in accuracy.

Recurrent architecture effectively incorporates contextual information.

End-to-end training improves classification consistency.

Abstract

Classification of very high resolution (VHR) satellite images has three major challenges: 1) inherent low intra-class and high inter-class spectral similarities, 2) mismatching resolution of available bands, and 3) the need to regularize noisy classification maps. Conventional methods have addressed these challenges by adopting separate stages of image fusion, feature extraction, and post-classification map regularization. These processing stages, however, are not jointly optimizing the classification task at hand. In this study, we propose a single-stage framework embedding the processing stages in a recurrent multiresolution convolutional network trained in an end-to-end manner. The feedforward version of the network, called FuseNet, aims to match the resolution of the panchromatic and multispectral bands in a VHR image using convolutional layers with corresponding downsampling and upsampling operations. Contextual label information is incorporated into FuseNet by means of a recurrent version called ReuseNet. We compared FuseNet and ReuseNet against the use of separate processing steps for both image fusion, e.g. pansharpening and resampling through interpolation, and map regularization such as conditional random fields. We carried out our experiments on a land cover classification task using a Worldview-03 image of Quezon City, Philippines and the ISPRS 2D semantic labeling benchmark dataset of Vaihingen, Germany. FuseNet and ReuseNet surpass the baseline approaches in both quantitative and qualitative results.