One-dimensional convolution neural networks for object-based feature selection

Abstract

Recently, the new Geographic object-based image analysis (GEOBIA) was proposed as an alternative classification approach to pixel based ones. In GEOBIA, image segments can be depicted with various attributes such as spectral, texture, shape, deep features and context, and hence final classification can produce better land cover/use map. The presence of such a large number of features poses significant challenges to standard machine learning methods and has rendered many existing classification techniques impractical. In this work, we are interested to feature selection techniques, which are employed to reduce the dimensionality of the data while keeping the most of its expressive power. Inspired by recent works in remote sensing using Convolutional Neural Networks (CNNs), especially for hyperspectral band selection, a feature selection approach based on One-Dimensional Convolutional Neural Networks (1-D CNN) is proposed in this study. All object-based features are used to train the 1-D CNN to obtain well trained model. After testing different feature combinations, we use the well trained model to obtain their test classification accuracies, and finally we select the subset of features with the highest precision. In our experiments, we evaluate our feature selection approach on 30-cm resolution colour infrared (CIR) aerial orthoimagery. A multi-resolution segmentation is performed to segment the images into regions, which are characterized later using various spectral, textural and spatial attributes to form the final object-based feature dataset. The obtained experimental results show that the proposed method can achieve satisfactory results when compared with traditional feature selection approaches.