Tile-Based Semisupervised Classification of Large-Scale VHR Remote Sensing Images

Abstract EN

This paper deals with the problem of the classification of large-scale very high-resolution (VHR) remote sensing (RS) images in a semisupervised scenario, where we have a limited training set (less than ten training samples per class).

Typical pixel-based classification methods are unfeasible for large-scale VHR images.

Thus, as a practical and efficient solution, we propose to subdivide the large image into a grid of tiles and then classify the tiles instead of classifying pixels.

Our proposed method uses the power of a pretrained convolutional neural network (CNN) to first extract descriptive features from each tile.

Next, a neural network classifier (composed of 2 fully connected layers) is trained in a semisupervised fashion and used to classify all remaining tiles in the image.

This basically presents a coarse classification of the image, which is sufficient for many RS application.

The second contribution deals with the employment of the semisupervised learning to improve the classification accuracy.

We present a novel semisupervised approach which exploits both the spectral and spatial relationships embedded in the remaining unlabelled tiles.

In particular, we embed a spectral graph Laplacian in the hidden layer of the neural network.

In addition, we apply regularization of the output labels using a spatial graph Laplacian and the random Walker algorithm.

Experimental results obtained by testing the method on two large-scale images acquired by the IKONOS2 sensor reveal promising capabilities of this method in terms of classification accuracy even with less than ten training samples per class.