Active Deep Learning for Classification of Hyperspectral Images

TL;DR

This paper introduces an active deep learning method using weighted incremental dictionary learning to efficiently select training samples, improving hyperspectral image classification accuracy with fewer labeled samples.

Contribution

It proposes a novel active learning algorithm based on weighted incremental dictionary learning for hyperspectral image classification, enhancing training efficiency and accuracy.

Findings

The proposed method outperforms existing algorithms in classification accuracy.

It reduces the number of labeled samples needed for effective training.

The algorithm is computationally efficient and effective in remote sensing applications.

Abstract

Active deep learning classification of hyperspectral images is considered in this paper. Deep learning has achieved success in many applications, but good-quality labeled samples are needed to construct a deep learning network. It is expensive getting good labeled samples in hyperspectral images for remote sensing applications. An active learning algorithm based on a weighted incremental dictionary learning is proposed for such applications. The proposed algorithm selects training samples that maximize two selection criteria, namely representative and uncertainty. This algorithm trains a deep network efficiently by actively selecting training samples at each iteration. The proposed algorithm is applied for the classification of hyperspectral images, and compared with other classification algorithms employing active learning. It is shown that the proposed algorithm is efficient and effective in classifying hyperspectral images.