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Artificial Neural Networks and Evolutionary Computation in Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 69005

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Prof. Dr. Taskin Kavzoglu

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Geomatics Engineering, Gebze Technical University, Kocaeli, Turkey
Interests: remote sensing; machine learning algorithms; data mining; digital image processing; object-based image classification; feature selection
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Dear Colleagues,

Artificial neural networks (ANNs) offer great potential to get insight and to uncover the underlying relationships and structures existing in datasets. ANNs imitate the physical process of learning in the human brain in a simple way. A model is formed by artificial neurons on several layers that emulate biological neurons and the synaptic connections. ANNs are effective in identifying patterns and other underlying data structures in multidimensional data, particularly for remotely sensed data. They are also good at dealing with a large set of variables possessing non-linearity, categorical data, and complex structures. Once a neural net is trained, the network is capable of processing new and unseen datasets. At this point, it should be stated that the robustness of the trained neural nets lies in the optimization of the chosen learning algorithm, the parameters controlling the training phase, and, of course, the quality of the training data, which can be considers as their representativeness for the problem under consideration. Over the past decade, there have been considerable increases in both the quantity of remotely sensed data, and the use of neural networks for remote sensing research problems. Initially called black-box methods, neural nets are now more popular, with new network types and algorithms, and they are more interpretable. Up until now, ANNs have been applied to many tasks, not only for statistical regressions or image classification, but also for image segmentation, feature extraction, data fusion, or dimensionality reduction. Although significant progress has been made in the analysis of remotely sensed imagery using neural nets, a number of issues remain to be resolved. This Special Issue aims to showcase the variety and relevance of the recent developments in the theory and application of neural networks and evolutionary computation in remote sensing. Thus, the latest and most advanced ideas and findings related to the application of neural nets will be shared with the remote sensing community. Authors are encouraged to submit original papers of both a theoretical- and application-based nature.

Prof. Dr. Taskin Kavzoglu
Guest Editor

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Image processing
Artificial neural networks
Machine learning
Image classification
Object-based classification
Deep learning
Extreme learning
Convolutional neural networks
Data mining
Image fusion
Dimensionality reduction
Parameter estimation
Spectral unmixing

Published Papers (11 papers)

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Research

17 pages, 5681 KiB

Open AccessArticle

CloudScout: A Deep Neural Network for On-Board Cloud Detection on Hyperspectral Images

by Gianluca Giuffrida, Lorenzo Diana, Francesco de Gioia, Gionata Benelli, Gabriele Meoni, Massimiliano Donati and Luca Fanucci

Remote Sens. 2020, 12(14), 2205; https://doi.org/10.3390/rs12142205 - 10 Jul 2020

Cited by 90 | Viewed by 8155

Abstract

The increasing demand for high-resolution hyperspectral images from nano and microsatellites conflicts with the strict bandwidth constraints for downlink transmission. A possible approach to mitigate this problem consists in reducing the amount of data to transmit to ground through on-board processing of hyperspectral images. In this paper, we propose a custom Convolutional Neural Network (CNN) deployed for a nanosatellite payload to select images eligible for transmission to ground, called CloudScout. The latter is installed on the Hyperscout-2, in the frame of the Phisat-1 ESA mission, which exploits a hyperspectral camera to classify cloud-covered images and clear ones. The images transmitted to ground are those that present less than 70% of cloudiness in a frame. We train and test the network against an extracted dataset from the Sentinel-2 mission, which was appropriately pre-processed to emulate the Hyperscout-2 hyperspectral sensor. On the test set we achieve 92% of accuracy with 1% of False Positives (FP). The Phisat-1 mission will start in 2020 and will operate for about 6 months. It represents the first in-orbit demonstration of Deep Neural Network (DNN) for data processing on the edge. The innovation aspect of our work concerns not only cloud detection but in general low power, low latency, and embedded applications. Our work should enable a new era of edge applications and enhance remote sensing applications directly on-board satellite. Full article

(This article belongs to the Special Issue Artificial Neural Networks and Evolutionary Computation in Remote Sensing)

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25 pages, 4196 KiB

Open AccessArticle

Machine Learning Classification Ensemble of Multitemporal Sentinel-2 Images: The Case of a Mixed Mediterranean Ecosystem

by Christos Vasilakos, Dimitris Kavroudakis and Aikaterini Georganta

Remote Sens. 2020, 12(12), 2005; https://doi.org/10.3390/rs12122005 - 22 Jun 2020

Cited by 33 | Viewed by 5203

Abstract

Land cover type classification still remains an active research topic while new sensors and methods become available. Applications such as environmental monitoring, natural resource management, and change detection require more accurate, detailed, and constantly updated land-cover type mapping. These needs are fulfilled by newer sensors with high spatial and spectral resolution along with modern data processing algorithms. Sentinel-2 sensor provides data with high spatial, spectral, and temporal resolution for the in classification of highly fragmented landscape. This study applies six traditional data classifiers and nine ensemble methods on multitemporal Sentinel-2 image datasets for identifying land cover types in the heterogeneous Mediterranean landscape of Lesvos Island, Greece. Support vector machine, random forest, artificial neural network, decision tree, linear discriminant analysis, and k-nearest neighbor classifiers are applied and compared with nine ensemble classifiers on the basis of different voting methods. kappa statistic, F1-score, and Matthews correlation coefficient metrics were used in the assembly of the voting methods. Support vector machine outperformed the base classifiers with kappa of 0.91. Support vector machine also outperformed the ensemble classifiers in an unseen dataset. Five voting methods performed better than the rest of the classifiers. A diversity study based on four different metrics revealed that an ensemble can be avoided if a base classifier shows an identifiable superiority. Therefore, ensemble approaches should include a careful selection of base-classifiers based on a diversity analysis. Full article

(This article belongs to the Special Issue Artificial Neural Networks and Evolutionary Computation in Remote Sensing)

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19 pages, 12167 KiB

Open AccessArticle

Computer Vision and Deep Learning Techniques for the Analysis of Drone-Acquired Forest Images, a Transfer Learning Study

by Sarah Kentsch, Maximo Larry Lopez Caceres, Daniel Serrano, Ferran Roure and Yago Diez

Remote Sens. 2020, 12(8), 1287; https://doi.org/10.3390/rs12081287 - 18 Apr 2020

Cited by 44 | Viewed by 7789

Abstract

Unmanned Aerial Vehicles (UAV) are becoming an essential tool for evaluating the status and the changes in forest ecosystems. This is especially important in Japan due to the sheer magnitude and complexity of the forest area, made up mostly of natural mixed broadleaf deciduous forests. Additionally, Deep Learning (DL) is becoming more popular for forestry applications because it allows for the inclusion of expert human knowledge into the automatic image processing pipeline. In this paper we study and quantify issues related to the use of DL with our own UAV-acquired images in forestry applications such as: the effect of Transfer Learning (TL) and the Deep Learning architecture chosen or whether a simple patch-based framework may produce results in different practical problems. We use two different Deep Learning architectures (ResNet50 and UNet), two in-house datasets (winter and coastal forest) and focus on two separate problem formalizations (Multi-Label Patch or MLP classification and semantic segmentation). Our results show that Transfer Learning is necessary to obtain satisfactory outcome in the problem of MLP classification of deciduous vs evergreen trees in the winter orthomosaic dataset (with a 9.78% improvement from no transfer learning to transfer learning from a a general-purpose dataset). We also observe a further 2.7% improvement when Transfer Learning is performed from a dataset that is closer to our type of images. Finally, we demonstrate the applicability of the patch-based framework with the ResNet50 architecture in a different and complex example: Detection of the invasive broadleaf deciduous black locust (Robinia pseudoacacia) in an evergreen coniferous black pine (Pinus thunbergii) coastal forest typical of Japan. In this case we detect images containing the invasive species with a 75% of True Positives (TP) and 9% False Positives (FP) while the detection of native trees was 95% TP and 10% FP. Full article

(This article belongs to the Special Issue Artificial Neural Networks and Evolutionary Computation in Remote Sensing)

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21 pages, 6565 KiB

Open AccessArticle

Improved SRGAN for Remote Sensing Image Super-Resolution Across Locations and Sensors

by Yingfei Xiong, Shanxin Guo, Jinsong Chen, Xinping Deng, Luyi Sun, Xiaorou Zheng and Wenna Xu

Remote Sens. 2020, 12(8), 1263; https://doi.org/10.3390/rs12081263 - 16 Apr 2020

Cited by 46 | Viewed by 7083

Abstract

Detailed and accurate information on the spatial variation of land cover and land use is a critical component of local ecology and environmental research. For these tasks, high spatial resolution images are required. Considering the trade-off between high spatial and high temporal resolution in remote sensing images, many learning-based models (e.g., Convolutional neural network, sparse coding, Bayesian network) have been established to improve the spatial resolution of coarse images in both the computer vision and remote sensing fields. However, data for training and testing in these learning-based methods are usually limited to a certain location and specific sensor, resulting in the limited ability to generalize the model across locations and sensors. Recently, generative adversarial nets (GANs), a new learning model from the deep learning field, show many advantages for capturing high-dimensional nonlinear features over large samples. In this study, we test whether the GAN method can improve the generalization ability across locations and sensors with some modification to accomplish the idea “training once, apply to everywhere and different sensors” for remote sensing images. This work is based on super-resolution generative adversarial nets (SRGANs), where we modify the loss function and the structure of the network of SRGANs and propose the improved SRGAN (ISRGAN), which makes model training more stable and enhances the generalization ability across locations and sensors. In the experiment, the training and testing data were collected from two sensors (Landsat 8 OLI and Chinese GF 1) from different locations (Guangdong and Xinjiang in China). For the cross-location test, the model was trained in Guangdong with the Chinese GF 1 (8 m) data to be tested with the GF 1 data in Xinjiang. For the cross-sensor test, the same model training in Guangdong with GF 1 was tested in Landsat 8 OLI images in Xinjiang. The proposed method was compared with the neighbor-embedding (NE) method, the sparse representation method (SCSR), and the SRGAN. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were chosen for the quantitive assessment. The results showed that the ISRGAN is superior to the NE (PSNR: 30.999, SSIM: 0.944) and SCSR (PSNR: 29.423, SSIM: 0.876) methods, and the SRGAN (PSNR: 31.378, SSIM: 0.952), with the PSNR = 35.816 and SSIM = 0.988 in the cross-location test. A similar result was seen in the cross-sensor test. The ISRGAN had the best result (PSNR: 38.092, SSIM: 0.988) compared to the NE (PSNR: 35.000, SSIM: 0.982) and SCSR (PSNR: 33.639, SSIM: 0.965) methods, and the SRGAN (PSNR: 32.820, SSIM: 0.949). Meanwhile, we also tested the accuracy improvement for land cover classification before and after super-resolution by the ISRGAN. The results show that the accuracy of land cover classification after super-resolution was significantly improved, in particular, the impervious surface class (the road and buildings with high-resolution texture) improved by 15%. Full article

(This article belongs to the Special Issue Artificial Neural Networks and Evolutionary Computation in Remote Sensing)

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19 pages, 3980 KiB

Open AccessArticle

Design of Feedforward Neural Networks in the Classification of Hyperspectral Imagery Using Superstructural Optimization

by Hasan Sildir, Erdal Aydin and Taskin Kavzoglu

Remote Sens. 2020, 12(6), 956; https://doi.org/10.3390/rs12060956 - 16 Mar 2020

Cited by 23 | Viewed by 3523

Abstract

Artificial Neural Networks (ANNs) have been used in a wide range of applications for complex datasets with their flexible mathematical architecture. The flexibility is favored by the introduction of a higher number of connections and variables, in general. However, over-parameterization of the ANN equations and the existence of redundant input variables usually result in poor test performance. This paper proposes a superstructure-based mixed-integer nonlinear programming method for optimal structural design including neuron number selection, pruning, and input selection for multilayer perceptron (MLP) ANNs. In addition, this method uses statistical measures such as the parameter covariance matrix in order to increase the test performance while permitting reduced training performance. The suggested approach was implemented on two public hyperspectral datasets (with 10% and 50% sampling ratios), namely Indian Pines and Pavia University, for the classification problem. The test results revealed promising performances compared to the standard fully connected neural networks in terms of the estimated overall and individual class accuracies. With the application of the proposed superstructural optimization, fully connected networks were pruned by over 60% in terms of the total number of connections, resulting in an increase of 4% for the 10% sampling ratio and a 1% decrease for the 50% sampling ratio. Moreover, over 20% of the spectral bands in the Indian Pines data and 30% in the Pavia University data were found statistically insignificant, and they were thus removed from the MLP networks. As a result, the proposed method was found effective in optimizing the architectural design with high generalization capabilities, particularly for fewer numbers of samples. The analysis of the eliminated spectral bands revealed that the proposed algorithm mostly removed the bands adjacent to the pre-eliminated noisy bands and highly correlated bands carrying similar information. Full article

(This article belongs to the Special Issue Artificial Neural Networks and Evolutionary Computation in Remote Sensing)

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20 pages, 4054 KiB

Open AccessArticle

Deep Quadruplet Network for Hyperspectral Image Classification with a Small Number of Samples

by Chengye Zhang, Jun Yue and Qiming Qin

Remote Sens. 2020, 12(4), 647; https://doi.org/10.3390/rs12040647 - 15 Feb 2020

Cited by 20 | Viewed by 3532

Abstract

This study proposes a deep quadruplet network (DQN) for hyperspectral image classification given the limitation of having a small number of samples. A quadruplet network is designed, which makes use of a new quadruplet loss function in order to learn a feature space where the distances between samples from the same class are shortened, while those from a different class are enlarged. A deep 3-D convolutional neural network (CNN) with characteristics of both dense convolution and dilated convolution is then employed and embedded in the quadruplet network to extract spatial-spectral features. Finally, the nearest neighbor (NN) classifier is used to accomplish the classification in the learned feature space. The results show that the proposed network can learn a feature space and is able to undertake hyperspectral image classification using only a limited number of samples. The main highlights of the study include: (1) The proposed approach was found to have high overall accuracy and can be classified as state-of-the-art; (2) Results of the ablation study suggest that all the modules of the proposed approach are effective in improving accuracy and that the proposed quadruplet loss contributes the most; (3) Time-analysis shows the proposed methodology has a similar level of time consumption as compared with existing methods. Full article

(This article belongs to the Special Issue Artificial Neural Networks and Evolutionary Computation in Remote Sensing)

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23 pages, 6174 KiB

Open AccessArticle

Mapping the Topographic Features of Mining-Related Valley Fills Using Mask R-CNN Deep Learning and Digital Elevation Data

by Aaron E. Maxwell, Pariya Pourmohammadi and Joey D. Poyner

Remote Sens. 2020, 12(3), 547; https://doi.org/10.3390/rs12030547 - 07 Feb 2020

Cited by 52 | Viewed by 7507

Abstract

Modern elevation-determining remote sensing technologies such as light-detection and ranging (LiDAR) produce a wealth of topographic information that is increasingly being used in a wide range of disciplines, including archaeology and geomorphology. However, automated methods for mapping topographic features have remained a significant challenge. Deep learning (DL) mask regional-convolutional neural networks (Mask R-CNN), which provides context-based instance mapping, offers the potential to overcome many of the difficulties of previous approaches to topographic mapping. We therefore explore the application of Mask R-CNN to extract valley fill faces (VFFs), which are a product of mountaintop removal (MTR) coal mining in the Appalachian region of the eastern United States. LiDAR-derived slopeshades are provided as the only predictor variable in the model. Model generalization is evaluated by mapping multiple study sites outside the training data region. A range of assessment methods, including precision, recall, and F1 score, all based on VFF counts, as well as area- and a fuzzy area-based user’s and producer’s accuracy, indicate that the model was successful in mapping VFFs in new geographic regions, using elevation data derived from different LiDAR sensors. Precision, recall, and F1-score values were above 0.85 using VFF counts while user’s and producer’s accuracy were above 0.75 and 0.85 when using the area- and fuzzy area-based methods, respectively, when averaged across all study areas characterized with LiDAR data. Due to the limited availability of LiDAR data until relatively recently, we also assessed how well the model generalizes to terrain data created using photogrammetric methods that characterize past terrain conditions. Unfortunately, the model was not sufficiently general to allow successful mapping of VFFs using photogrammetrically-derived slopeshades, as all assessment metrics were lower than 0.60; however, this may partially be attributed to the quality of the photogrammetric data. The overall results suggest that the combination of Mask R-CNN and LiDAR has great potential for mapping anthropogenic and natural landscape features. To realize this vision, however, research on the mapping of other topographic features is needed, as well as the development of large topographic training datasets including a variety of features for calibrating and testing new methods. Full article

(This article belongs to the Special Issue Artificial Neural Networks and Evolutionary Computation in Remote Sensing)

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18 pages, 5849 KiB

Open AccessArticle

Improved Winter Wheat Spatial Distribution Extraction from High-Resolution Remote Sensing Imagery Using Semantic Features and Statistical Analysis

by Feng Li, Chengming Zhang, Wenwen Zhang, Zhigang Xu, Shouyi Wang, Genyun Sun and Zhenjie Wang

Remote Sens. 2020, 12(3), 538; https://doi.org/10.3390/rs12030538 - 06 Feb 2020

Cited by 11 | Viewed by 2763

Abstract

Improving the accuracy of edge pixel classification is an important aspect of using convolutional neural networks (CNNs) to extract winter wheat spatial distribution information from remote sensing imagery. In this study, we established a method using prior knowledge obtained from statistical analysis to refine CNN classification results, named post-processing CNN (PP-CNN). First, we used an improved RefineNet model to roughly segment remote sensing imagery in order to obtain the initial winter wheat area and the category probability vector for each pixel. Second, we used manual labels as references and performed statistical analysis on the class probability vectors to determine the filtering conditions and select the pixels that required optimization. Third, based on the prior knowledge that winter wheat pixels were internally similar in color, texture, and other aspects, but different from other neighboring land-use types, the filtered pixels were post-processed to improve the classification accuracy. We used 63 Gaofen-2 images obtained from 2017 to 2019 of a representative Chinese winter wheat region (Feicheng, Shandong Province) to create the dataset and employed RefineNet and SegNet as standard CNN and conditional random field (CRF) as post-process methods, respectively, to conduct comparison experiments. PP-CNN’s accuracy (94.4%), precision (93.9%), and recall (94.4%) were clearly superior, demonstrating its advantages for the improved refinement of edge areas during image classification. Full article

(This article belongs to the Special Issue Artificial Neural Networks and Evolutionary Computation in Remote Sensing)

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28 pages, 6453 KiB

Open AccessArticle

Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite Images

by Ugur Alganci, Mehmet Soydas and Elif Sertel

Remote Sens. 2020, 12(3), 458; https://doi.org/10.3390/rs12030458 - 01 Feb 2020

Cited by 100 | Viewed by 12979

Abstract

Object detection from satellite images has been a challenging problem for many years. With the development of effective deep learning algorithms and advancement in hardware systems, higher accuracies have been achieved in the detection of various objects from very high-resolution (VHR) satellite images. This article provides a comparative evaluation of the state-of-the-art convolutional neural network (CNN)-based object detection models, which are Faster R-CNN, Single Shot Multi-box Detector (SSD), and You Look Only Once-v3 (YOLO-v3), to cope with the limited number of labeled data and to automatically detect airplanes in VHR satellite images. Data augmentation with rotation, rescaling, and cropping was applied on the test images to artificially increase the number of training data from satellite images. Moreover, a non-maximum suppression algorithm (NMS) was introduced at the end of the SSD and YOLO-v3 flows to get rid of the multiple detection occurrences near each detected object in the overlapping areas. The trained networks were applied to five independent VHR test images that cover airports and their surroundings to evaluate their performance objectively. Accuracy assessment results of the test regions proved that Faster R-CNN architecture provided the highest accuracy according to the F1 scores, average precision (AP) metrics, and visual inspection of the results. The YOLO-v3 ranked as second, with a slightly lower performance but providing a balanced trade-off between accuracy and speed. The SSD provided the lowest detection performance, but it was better in object localization. The results were also evaluated in terms of the object size and detection accuracy manner, which proved that large- and medium-sized airplanes were detected with higher accuracy. Full article

(This article belongs to the Special Issue Artificial Neural Networks and Evolutionary Computation in Remote Sensing)

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24 pages, 12566 KiB

Open AccessArticle

A Coarse-to-Fine Network for Ship Detection in Optical Remote Sensing Images

by Yue Wu, Wenping Ma, Maoguo Gong, Zhuangfei Bai, Wei Zhao, Qiongqiong Guo, Xiaobo Chen and Qiguang Miao

Remote Sens. 2020, 12(2), 246; https://doi.org/10.3390/rs12020246 - 10 Jan 2020

Cited by 24 | Viewed by 3116

Abstract

With the increasing resolution of optical remote sensing images, ship detection in optical remote sensing images has attracted a lot of research interests. The current ship detection methods usually adopt the coarse-to-fine detection strategy, which firstly extracts low-level and manual features, and then performs multi-step training. Inadequacies of this strategy are that it would produce complex calculation, false detection on land and difficulty in detecting the small size ship. Aiming at these problems, a sea-land separation algorithm that combines gradient information and gray information is applied to avoid false alarms on land, the feature pyramid network (FPN) is used to achieve small ship detection, and a multi-scale detection strategy is proposed to achieve ship detection with different degrees of refinement. Then the feature extraction structure is adopted to fuse different hierarchical features to improve the representation ability of features. Finally, we propose a new coarse-to-fine ship detection network (CF-SDN) that directly achieves an end-to-end mapping from image pixels to bounding boxes with confidences. A coarse-to-fine detection strategy is applied to improve the classification ability of the network. Experimental results on optical remote sensing image set indicate that the proposed method outperforms the other excellent detection algorithms and achieves good detection performance on images including some small-sized ships and dense ships near the port. Full article

(This article belongs to the Special Issue Artificial Neural Networks and Evolutionary Computation in Remote Sensing)

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19 pages, 6163 KiB

Open AccessArticle

Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion

by Chengming Zhang, Yan Chen, Xiaoxia Yang, Shuai Gao, Feng Li, Ailing Kong, Dawei Zu and Li Sun

Remote Sens. 2020, 12(2), 213; https://doi.org/10.3390/rs12020213 - 08 Jan 2020

Cited by 33 | Viewed by 4565

Abstract

When extracting land-use information from remote sensing imagery using image segmentation, obtaining fine edges for extracted objects is a key problem that is yet to be solved. In this study, we developed a new weight feature value convolutional neural network (WFCNN) to perform fine remote sensing image segmentation and extract improved land-use information from remote sensing imagery. The WFCNN includes one encoder and one classifier. The encoder obtains a set of spectral features and five levels of semantic features. It uses the linear fusion method to hierarchically fuse the semantic features, employs an adjustment layer to optimize every level of fused features to ensure the stability of the pixel features, and combines the fused semantic and spectral features to form a feature graph. The classifier then uses a Softmax model to perform pixel-by-pixel classification. The WFCNN was trained using a stochastic gradient descent algorithm; the former and two variants were subject to experimental testing based on Gaofen 6 images and aerial images that compared them with the commonly used SegNet, U-NET, and RefineNet models. The accuracy, precision, recall, and F1-Score of the WFCNN were higher than those of the other models, indicating certain advantages in pixel-by-pixel segmentation. The results clearly show that the WFCNN can improve the accuracy and automation level of large-scale land-use mapping and the extraction of other information using remote sensing imagery. Full article

(This article belongs to the Special Issue Artificial Neural Networks and Evolutionary Computation in Remote Sensing)

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