Remote Sensing Big Data Classification with High Performance Distributed Deep Learning

Sedona, Rocco; Cavallaro, Gabriele; Jitsev, Jenia; Strube, Alexandre; Riedel, Morris; Benediktsson, Jón Atli

doi:10.3390/rs11243056

Cited by 29 publications

(16 citation statements)

References 53 publications

(53 reference statements)

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“…In addition, the development of intelligent techniques and algorithms for classification and information extraction facilitates the use of the big RS data in their full potential [28]. The RS technologies (especially, the Sentinel program) supply huge volumes of raw data (big earth data) with various spectral, spatial, coverage, and multi-scale characteristics; such volumes of data require various algorithms and appropriate models to precisely extract and classify the information [29]. Table 1.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data

Ahmadi

Kalantar

Saeidi³

et al. 2020

Remote Sensing

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The estimation and mapping of forest stand characteristics are vital because this information is necessary for sustainable forest management. The present study considers the use of a Bayesian additive regression trees (BART) algorithm as a non-parametric classifier using Sentinel-2A data and topographic variables to estimate the forest stand characteristics, namely the basal area (m2/ha), stem volume (m3/ha), and stem density (number/ha). These results were compared with those of three other popular machine learning (ML) algorithms, such as generalised linear model (GLM), K-nearest neighbours (KNN), and support vector machine (SVM). A feature selection was done on 28 variables including the multi-spectral bands on Sentinel-2 satellite, related vegetation indices, and ancillary data (elevation, slope, and topographic solar-radiation index derived from digital elevation model (DEM)) and then the most insignificant variables were removed from the datasets by recursive feature elimination (RFE). The study area was a mountainous forest with high biodiversity and an elevation gradient from 26 to 1636 m. An inventory dataset of 1200 sample plots was provided for training and testing the algorithms, and the predictors were fed into the ML models to compute and predict the forest stand characteristics. The accuracies and certainties of the ML models were assessed by their root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2) values. The results demonstrated that BART generated the best basal area and stem volume predictions, followed by GLM, SVM, and KNN. The best RMSE values for both basal area (8.12 m2/ha) and stem volume (29.28 m3/ha) estimation were obtained by BART. Thus, the ability of the BART model for forestry application was established. On the other hand, KNN exhibited the highest RMSE values for all stand variable predictions, thereby exhibiting the least accuracy for this specific application. Moreover, the effectiveness of the narrow Sentinel-2 bands around the red edge and elevation was highlighted for predicting the forest stand characteristics. Therefore, we concluded that the combination of the Sentinel-2 products and topographic variables derived from the PALSAR data used in this study improved the estimation of the forest attributes in temperate forests.

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Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data

Ahmadi

Kalantar

Saeidi³

et al. 2020

Remote Sensing

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“…Sedona et al [20] proposed to use High Performance Computing machines to compute classification algorithms in parallel with CNNs.…”

Section: Related Workmentioning

confidence: 99%

A Big Data Framework for Satellite Images Processing using Apache Hadoop and RasterFrames: A Case Study of Surface Water Extraction in Phu Tho, Viet Nam

Nguyen¹,

Anh²

2020

IJACSA

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“…In contrast, satellite-view images have the advantages of wide spatiotemporal coverages and having geo-tags. Some traditional satellite-view image processing methods (e.g., image classification, object detection, and semantic segmentation) simply use the surface feature information captured by satellite images [17][18][19][20][21][22][23][24][25][26], and the geo-tags of the satellite-view image are usually neglected. To make full use of the geo-tags of the satellite-view images to locate images from other views, scientific communities began to pay attention to cross-view image matching between the satellite view and other views.…”

Section: Introductionmentioning

confidence: 99%

A Practical Cross-View Image Matching Method between UAV and Satellite for UAV-Based Geo-Localization

et al. 2020

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Cross-view image matching has attracted extensive attention due to its huge potential applications, such as localization and navigation. Unmanned aerial vehicle (UAV) technology has been developed rapidly in recent years, and people have more opportunities to obtain and use UAV-view images than ever before. However, the algorithms of cross-view image matching between the UAV view (oblique view) and the satellite view (vertical view) are still in their beginning stage, and the matching accuracy is expected to be further improved when applied in real situations. Within this context, in this study, we proposed a cross-view matching method based on location classification (hereinafter referred to LCM), in which the similarity between UAV and satellite views is considered, and we implemented the method with the newest UAV-based geo-localization dataset (University-1652). LCM is able to solve the imbalance of the input sample number between the satellite images and the UAV images. In the training stage, LCM can simplify the retrieval problem into a classification problem and consider the influence of the feature vector size on the matching accuracy. Compared with one study, LCM shows higher accuracies, and Recall@K (K ∈ {1, 5, 10}) and the average precision (AP) were improved by 5–10%. The expansion of satellite-view images and multiple queries proposed by the LCM are capable of improving the matching accuracy during the experiment. In addition, the influences of different feature sizes on the LCM’s accuracy are determined, and we found that 512 is the optimal feature size. Finally, the LCM model trained based on synthetic UAV-view images was evaluated in real-world situations, and the evaluation result shows that it still has satisfactory matching accuracy. The LCM can realize the bidirectional matching between the UAV-view image and the satellite-view image and can contribute to two applications: (i) UAV-view image localization (i.e., predicting the geographic location of UAV-view images based on satellite-view images with geo-tags) and (ii) UAV navigation (i.e., driving the UAV to the region of interest in the satellite-view image based on the flight record).

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Remote Sensing Big Data Classification with High Performance Distributed Deep Learning

Cited by 29 publications

References 53 publications

Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data

Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data

A Big Data Framework for Satellite Images Processing using Apache Hadoop and RasterFrames: A Case Study of Surface Water Extraction in Phu Tho, Viet Nam

A Practical Cross-View Image Matching Method between UAV and Satellite for UAV-Based Geo-Localization

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