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Öğe 3-D Hybrid CNN Combined With 3-D Generative Adversarial Network for Wetland Classification With Limited Training Data(Ieee-Inst Electrical Electronics Engineers Inc, 2022) Jamali, Ali; Mahdianpari, Masoud; Mohammadimanesh, Fariba; Brisco, Brian; Salehi, BahramRecently, deep learning algorithms, specifically convolutional neural networks (CNNs), have played an important role in remote sensing image classification, including wetland mapping. However, one limitation of deep CNN for classification is its requirement for a great number of training samples. This limitation is particularly enhanced when the classes of interest are spectrally similar, such as that of wetland types, and the training samples are limited. This article presents a novel approach named 3-D hybrid generative adversarial network (3-D hybrid GAN) that addresses the limited training sample issue in the classification of remote sensing imagery with a focus on complex wetland classification. We used a conditional map unit that generates synthetic training samples for only classes with a lower number of training samples to improve the per-class accuracy of wetlands. This procedure overcomes the issue of imbalanced data in conventional wetland mapping. Based on the achieved results, better classification accuracy is obtained by integrating a 3-D generative adversarial network (3-D GAN) and the CNN network of a 3-D hybrid CNN using both 3-D and 2-D convolutional filters. Experimental results on the avalon pilot site located in eastern Newfoundland, Canada, and covering five wetland types of bog, fen, marsh, swamp, and shallow water demonstrate that our model significantly outperforms other CNN models, including the HybridSN, SpectralNet, MLP-mixer, as well as a conventional algorithm of random forest for complex wetland classification by approximately 1% to 51% in terms of F-1 score.Öğe 3DUNetGSFormer: A deep learning pipeline for complex wetland mapping using generative adversarial networks and Swin transformer(Elsevier, 2022) Jamali, Ali; Mahdianpari, Masoud; Brisco, Brian; Mao, Dehua; Salehi, Bahram; Mohammadimanesh, FaribaMany ecosystems, particularly wetlands, are significantly degraded or lost as a result of climate change and anthropogenic activities. Simultaneously, developments in machine learning, particularly deep learning methods, have greatly improved wetland mapping, which is a critical step in ecosystem monitoring. Yet, present deep and very deep models necessitate a greater number of training data, which are costly, logistically chal-lenging, and time-consuming to acquire. Thus, we explore and address the potential and possible limitations caused by the availability of limited ground-truth data for large-scale wetland mapping. To overcome this persistent problem for remote sensing data classification using deep learning models, we propose 3D UNet Generative Adversarial Network Swin Transformer (3DUNetGSFormer) to adaptively synthesize wetland training data based on each class's data availability. Both real and synthesized training data are then imported to a novel deep learning architecture consisting of cutting-edge Convolutional Neural Networks and vision transformers for wetland mapping. Results demonstrated that the developed wetland classifier obtained a high level of kappa coefficient, average accuracy, and overall accuracy of 96.99%, 97.13%, and 97.39%, respectively, for the data in three pilot sites in and around Grand Falls-Windsor, Avalon, and Gros Morne National Park located in Canada. The results show that the proposed methodology opens a new window for future high-quality wetland data generation and classification. The developed codes are available at https://github.com/aj1365/3DUNetGSForme r.Öğe A Cloud-Based Framework for Large-Scale Monitoring of Ocean Plastics Using Multi-Spectral Satellite Imagery and Generative Adversarial Network(Mdpi, 2021) Jamali, Ali; Mahdianpari, MasoudMarine debris is considered a threat to the inhabitants, as well as the marine environments. Accumulation of marine debris, besides climate change factors, including warming water, sea-level rise, and changes in oceans' chemistry, are causing the potential collapse of the marine environment's health. Due to the increase of marine debris, including plastics in coastlines, ocean and sea surfaces, and even in deep ocean layers, there is a need for developing new advanced technology for the detection of large-sized marine pollution (with sizes larger than 1 m) using state-of-the-art remote sensing and machine learning tools. Therefore, we developed a cloud-based framework for large-scale marine pollution detection with the integration of Sentinel-2 satellite imagery and advanced machine learning tools on the Sentinel Hub cloud application programming interface (API). Moreover, we evaluated the performance of two shallow machine learning algorithms of random forest (RF) and support vector machine (SVM), as well as the deep learning method of the generative adversarial network-random forest (GAN-RF) for the detection of ocean plastics in the pilot site of Mytilene Island, Greece. Based on the obtained results, the shallow algorithms of RF and SVM achieved an overall accuracy of 88% and 84%, respectively, with available training data of plastic debris. The GAN-RF classifier improved the detection of ocean plastics of the RF method by 8%, achieving an overall accuracy of 96% by generating several synthetic ocean plastic samples.Öğe Deep Forest classifier for wetland mapping using the combination of Sentinel-1 and Sentinel-2 data(Taylor & Francis Ltd, 2021) Jamali, Ali; Mahdianpari, Masoud; Brisco, Brian; Granger, Jean; Mohammadimanesh, Fariba; Salehi, BahramWetlands are among the most important, yet in danger ecosystems and play a vital role for the well-being of humans as well as flora and fauna. Over the past few years, state-of-the-art deep learning (DL) tools have gained attention for wetland classification within the remote sensing community. However, the DL methods could have complex structure and their efficiency greatly depends on the availability of a large number of training data. Inspired by DL methods, yet with less complexity, the Deep Forest (DF) classifier is an advanced tree-based deep learning tool with a great capability for several remote sensing applications. Despite the effectiveness of DF classifiers, few research studies have investigated the potential of such a powerful technique for classification of remote sensing, with no documented research for wetland classification. Accordingly, the potential of the DF algorithm for the classification of wetland complexes has been investigated in this study. In particular, three well-known classifiers, namely Extreme Gradient Boosting (XGB), Random Forest (RF), and Extra Tree (ET), were used as the tree-based classifier to build DF, for which the hyper parameter tuning is carried out to ensure the optimum classification accuracy. Three well-known tree-based classification algorithms, namely Decision Tree (DT), Conventional Random Forest (CRF), and Conventional Extreme Gradient Boosting (CXGB), as well as a Convolutional Neural Network (CNN) are used as benchmark tools to compare the results obtained from the DF classifiers for wetland mapping. The results demonstrated that the DF-XGB classifier outperforms both DF-RF and DF-ET in terms of classification accuracy albeit with a longer training time. The results also confirmed the superiority of all three DF-based classifiers compared to the CRF and DT classifiers. For example, the DF-XGB improved the F1-score by 14%, 13%, 7%, 3%, and 1% for fen, swamp, marsh, bog, and shallow water, respectively, compared to the optimized CRF. The results indicated that the DF algorithm has great capability to be applied over large areas to support regional and national wetland mapping and monitoring.Öğe A deep learning framework based on generative adversarial networks and vision transformer for complex wetland classification using limited training samples(Elsevier, 2022) Jamali, Ali; Mahdianpari, Masoud; Mohammadimanesh, Fariba; Homayouni, SaeidWetlands have long been recognized among the most critical ecosystems globally, yet their numbers quickly diminish due to human activities and climate change. Thus, large-scale wetland monitoring is essential to provide efficient spatial and temporal insights for resource management and conservation plans. However, the main challenge is the lack of enough reference data for accurate large-scale wetland mapping. As such, the main objective of this study was to investigate the efficient deep-learning models for generating high-resolution and temporally rich training datasets for wetland mapping. The Sentinel-1 and Sentinel-2 satellites from the Euro-pean Copernicus program deliver radar and optical data at a high temporal and spatial resolution. These Earth observations provide a unique source of information for more precise wetland mapping from space. The second objective was to investigate the efficiency of vision transformers for complex landscape mapping. As such, we proposed a 3D Generative Adversarial Network (3D GAN) to best achieve these two objectives of synthesizing training data and a Vision Transformer model for large-scale wetland classification. The proposed approach was tested in three different study areas of Saint John, Sussex, and Fredericton, New Brunswick, Canada. The results showed the ability of the 3D GAN to stimulate and increase the number of training data and, as a result, increase the accuracy of wetland classification. The quantitative results also demonstrated the capability of jointly using data augmentation, 3D GAN, and Vision Transformer models with overall accuracy, average accuracy, and Kappa index of 75.61%, 73.4%, and 71.87%, respectively, using a disjoint data sampling strategy. Therefore, the proposed deep learning method opens a new window for large-scale remote sensing wetland classification.Öğe HYPERSPECTRAL IMAGE CLASSIFICATION USING MULTI-LAYER PERCEPTRON MIXER (MLP-MIXER)(Copernicus Gesellschaft Mbh, 2023) Jamali, Ali; Mahdianpari, Masoud; Rahman, Alias AbdulThe classifying of hyperspectral images (HSI) is a difficult task given the high dimensionality of the space, the huge number of spectral bands, and the small number of labeled data. As such, we offer a unique hyperspectral image classification methodology to address these issues based on sophisticated Multi-Layer Perceptron (MLP) algorithms. In this paper, we propose using MLP-Mixer to classify HSI data in three data benchmarks of Pavia, Salinas, and Indian Pines. Based on the results, the proposed MLP-Mixer achieved a high level of classification accuracy and produced noise-free and homogenous classification maps in all study areas. For the classification of HSI data in Salinas, Indian Pines, and Pavia, the proposed MLP-Mixer achieved an average accuracy of 99.82%, 99.81%, and 99.23%, respectively.Öğe PolSAR Image Classification Based on Deep Convolutional Neural Networks Using Wavelet Transformation(Ieee-Inst Electrical Electronics Engineers Inc, 2022) Jamali, Ali; Mahdianpari, Masoud; Mohammadimanesh, Fariba; Bhattacharya, Avik; Homayouni, SaeidShallow convolutional neural networks (CNNs) have successfully been used to classify polarimetric synthetic aperture radar (PolSAR) imagery. However, one drawback of the existing deep CNN-based techniques is that the input PolSAR training data are often insufficient due to their need for a significant number of training data compared to shallow CNN models utilized in PolSAR image classification. In this letter, we propose using the Haar wavelet transform in deep CNNs for effective feature extraction to improve the classification accuracy of PolSAR imagery. Based on the results, the proposed deep CNN model obtained better average accuracy in the San Francisco region with an accuracy of 93.3% and produced more homogeneous classification maps with less noise compared to the two much shallower CNN models of AlexNet (87.8%) and a 2-D CNN network (91%). The proposed algorithm is efficient and may be applied over large areas to support regional wetland mapping and monitoring activities using PolSAR imagery. The codes are available at (https://github.com/aj1365/DeepCNN_Polsar).Öğe Swin Transformer and Deep Convolutional Neural Networks for Coastal Wetland Classification Using Sentinel-1, Sentinel-2, and LiDAR Data(Mdpi, 2022) Jamali, Ali; Mahdianpari, MasoudThe use of machine learning algorithms to classify complex landscapes has been revolutionized by the introduction of deep learning techniques, particularly in remote sensing. Convolutional neural networks (CNNs) have shown great success in the classification of complex high-dimensional remote sensing imagery, specifically in wetland classification. On the other hand, the state-of-the-art natural language processing (NLP) algorithms are transformers. Although the transformers have been studied for a few remote sensing applications, the integration of deep CNNs and transformers has not been studied, particularly in wetland mapping. As such, in this study, we explore the potential and possible limitations to be overcome regarding the use of a multi-model deep learning network with the integration of a modified version of the well-known deep CNN network of VGG-16, a 3D CNN network, and Swin transformer for complex coastal wetland classification. Moreover, we discuss the potential and limitation of the proposed multi-model technique over several solo models, including a random forest (RF), support vector machine (SVM), VGG-16, 3D CNN, and Swin transformer in the pilot site of Saint John city located in New Brunswick, Canada. In terms of F-1 score, the multi-model network obtained values of 0.87, 0.88, 0.89, 0.91, 0.93, 0.93, and 0.93 for the recognition of shrub wetland, fen, bog, aquatic bed, coastal marsh, forested wetland, and freshwater marsh, respectively. The results suggest that the multi-model network is superior to other solo classifiers from 3.36% to 33.35% in terms of average accuracy. Results achieved in this study suggest the high potential for integrating and using CNN networks with the cutting-edge transformers for the classification of complex landscapes in remote sensing.Öğe Swin Transformer for Complex Coastal Wetland Classification Using the Integration of Sentinel-1 and Sentinel-2 Imagery(Mdpi, 2022) Jamali, Ali; Mahdianpari, MasoudThe emergence of deep learning techniques has revolutionized the use of machine learning algorithms to classify complicated environments, notably in remote sensing. Convolutional Neural Networks (CNNs) have shown considerable promise in classifying challenging high-dimensional remote sensing data, particularly in the classification of wetlands. State-of-the-art Natural Language Processing (NLP) algorithms, on the other hand, are transformers. Despite the fact that transformers have been utilized for a few remote sensing applications, they have not been compared to other well-known CNN networks in complex wetland classification. As such, for the classification of complex coastal wetlands in the study area of Saint John city, located in New Brunswick, Canada, we modified and employed the Swin Transformer algorithm. Moreover, the developed transformer classifier results were compared with two well-known deep CNNs of AlexNet and VGG-16. In terms of average accuracy, the proposed Swin Transformer algorithm outperformed the AlexNet and VGG-16 techniques by 14.3% and 44.28%, respectively. The proposed Swin Transformer classifier obtained F-1 scores of 0.65, 0.71, 0.73, 0.78, 0.82, 0.84, and 0.84 for the recognition of coastal marsh, shrub, bog, fen, aquatic bed, forested wetland, and freshwater marsh, respectively. The results achieved in this study suggest the high capability of transformers over very deep CNN networks for the classification of complex landscapes in remote sensing.Öğe A Synergic Use of Sentinel-1 and Sentinel-2 Imagery for Complex Wetland Classification Using Generative Adversarial Network (GAN) Scheme(Mdpi, 2021) Jamali, Ali; Mahdianpari, Masoud; Mohammadimanesh, Fariba; Brisco, Brian; Salehi, BahramDue to anthropogenic activities and climate change, many natural ecosystems, especially wetlands, are lost or changing at a rapid pace. For the last decade, there has been increasing attention towards developing new tools and methods for the mapping and classification of wetlands using remote sensing. At the same time, advances in artificial intelligence and machine learning, particularly deep learning models, have provided opportunities to advance wetland classification methods. However, the developed deep and very deep algorithms require a higher number of training samples, which is costly, logistically demanding, and time-consuming. As such, in this study, we propose a Deep Convolutional Neural Network (DCNN) that uses a modified architecture of the well-known DCNN of the AlexNet and a Generative Adversarial Network (GAN) for the generation and classification of Sentinel-1 and Sentinel-2 data. Applying to an area of approximately 370 sq. km in the Avalon Peninsula, Newfoundland, the proposed model with an average accuracy of 92.30% resulted in F-1 scores of 0.82, 0.85, 0.87, 0.89, and 0.95 for the recognition of swamp, fen, marsh, bog, and shallow water, respectively. Moreover, the proposed DCNN model improved the F-1 score of bog, marsh, fen, and swamp wetland classes by 4%, 8%, 11%, and 26%, respectively, compared to the original CNN network of AlexNet. These results reveal that the proposed model is highly capable of the generation and classification of Sentinel-1 and Sentinel-2 wetland samples and can be used for large-extent classification problems.Öğe WETLAND CLASSIFICATION WITH SWIN TRANSFORMER USING SENTINEL-1 AND SENTINEL-2 DATA(Ieee, 2022) Jamali, Ali; Mohammadimanesh, Fariba; Mahdianpari, MasoudConvolutional Neural Networks (CNNs) have shown promising results in classifying complex remote sensing scenery, particularly in the classification of wetlands. State-of-the-art Natural Language Processing ( NLP) algorithms, on the other hand, are transformers. In this paper, we illustrate the effectiveness of the cutting-edge Swin Transformer for the classification of complex wetlands in New Brunswick, Canada. The precision of the proposed transformer is 0.66, 0.71, 0.75, 0.78, 0.82, 0.83, 0.84, 0.90, 0.90, 0.95, and 0.98 for the recognition of shrub, fen, forested wetland, crop, bog, freshwater marsh, coastal marsh, aquatic bed, grass, urban, and water, respectively. Based on the results, with a relatively high level of overall accuracy of slightly less than 80%, the proposed Swin Transformer is highly capable of complex wetland classification.
