Active Deep Densely Connected Convolutional Network for Hyperspectral Image Classification

TL;DR

This paper introduces an active deep learning framework with a densely connected convolutional network for hyperspectral image classification, effectively reducing labeling costs and achieving high accuracy with few labeled samples.

Contribution

It proposes a novel end-to-end active learning method that predicts sample loss to select informative unlabeled samples for training hyperspectral classifiers.

Findings

Achieves high classification accuracy with minimal labeled samples.

Outperforms traditional active learning methods in hyperspectral image classification.

Demonstrates effectiveness through extensive experiments.

Abstract

Deep learning based methods have seen a massive rise in popularity for hyperspectral image classification over the past few years. However, the success of deep learning is attributed greatly to numerous labeled samples. It is still very challenging to use only a few labeled samples to train deep learning models to reach a high classification accuracy. An active deep-learning framework trained by an end-to-end manner is, therefore, proposed by this paper in order to minimize the hyperspectral image classification costs. First, a deep densely connected convolutional network is considered for hyperspectral image classification. Different from the traditional active learning methods, an additional network is added to the designed deep densely connected convolutional network to predict the loss of input samples. Then, the additional network could be used to suggest unlabeled samples that the deep densely connected convolutional network is more likely to produce a wrong label. Note that the additional network uses the intermediate features of the deep densely connected convolutional network as input. Therefore, the proposed method is an end-to-end framework. Subsequently, a few of the selected samples are labelled manually and added to the training samples. The deep densely connected convolutional network is therefore trained using the new training set. Finally, the steps above are repeated to train the whole framework iteratively. Extensive experiments illustrates that the method proposed could reach a high accuracy in classification after selecting just a few samples.