Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

DALF: An AI Enabled Adversarial Framework for Classification of Hyperspectral Images

Hyperspectral image classification is very complex and challenging process. However, with deep neural networks like Convolutional Neural Networks (CNN) with explicit dimensionality reduction, the capability of classifier is greatly increased. However, there is still problem with sufficient training samples. In this paper, we overcome this problem by proposing an Artificial Intelligence (AI) based framework named Deep Adversarial Learning Framework (DALF) that exploits deep autoencoder for dimensionality reduction, Generative Adversarial Network (GAN) for generating new Hyperspectral Imaging (HSI) samples that are to be verified by a discriminator in a non-cooperative game setting besides using aclassifier. Convolutional Neural Network (CNN) is used for both generator and discriminator while classifier role is played by Support Vector Machine (SVM) and Neural Network (NN). An algorithm named Generative Model based Hybrid Approach for HSI Classification (GMHA-HSIC) which drives the functionality of the proposed framework is proposed. The success of DALF in accurate classification is largely dependent on the synthesis and labelling of spectra on regular basis. The synthetic samples made with an iterative process and being verified by discriminator result in useful spectra. By training GAN with associated deep learning models, the framework leverages classification performance. Our experimental results revealed that the proposed framework has potential to improve the state of the art besides having an effective data augmentation strategy