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

Nondestructive phenolic compounds measurement and origin discrimination of peated barley malt using near-infrared hyperspectral imagery and machine learning.

Quantifying phenolic compound in peated barley malt and discriminating its origin are essential to maintain the aroma of high-quality Scottish whisky during the manufacturing process. The content of the total phenol varies in peated barley malts, which is critical in measuring the associated peatiness level. Existing methods for measuring such phenols are destructive and/or time consuming. To tackle these issues, we propose in this paper a novel nondestructive system for fast and effective estimating the phenolic concentrations and discriminating their origins with the near-infrared hyperspectral imagery and machine learning. First, novel ways of data acquisition and normalization are developed for robustness. Then, the principal component analysis (PCA) and folded-PCA are fused for extracting the global and local spectral features, followed by the support vector machine (SVM) based origin discrimination and deep neural network based phenolic measurement. In total 27 categories of peated barley malts from eight suppliers are utilized to form thousands of spectral samples for modelling. A classification accuracy up to 99.5% and a squared-correlation-coefficient up to 98.57% are achieved in our experiments, outperforming a few state-of-the-art. These have fully demonstrated the efficacy of our system in automated phenolic measurement and origin discrimination to benefit the quality monitoring in the whisky industry

Deep learning for fusion of APEX hyperspectral and full-waveform LiDAR remote sensing data for tree species mapping

Deep learning has been widely used to fuse multi-sensor data for classification. However, current deep learning architecture for multi-sensor data fusion might not always perform better than single data source, especially for the fusion of hyperspectral and light detection and ranging (LiDAR) remote sensing data for tree species mapping in complex, closed forest canopies. In this paper, we propose a new deep fusion framework to integrate the complementary information from hyperspectral and LiDAR data for tree species mapping. We also investigate the fusion of either “single-band” or multi-band (i.e., full-waveform) LiDAR with hyperspectral data for tree species mapping. Additionally, we provide a solution to estimate the crown size of tree species by the fusion of multi-sensor data. Experimental results on fusing real APEX hyperspectral and LiDAR data demonstrate the effectiveness of the proposed deep fusion framework. Compared to using only single data source or current deep fusion architecture, our proposed method yields improvements in overall and average classification accuracies ranging from 82.21% to 87.10% and 76.71% to 83.45%, respectively

Deep learning-based change detection in remote sensing images:a review

Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods