Deep Learning Methods for Land Cover and Land Use Classification in Remote Sensing: A Review

Alem, Abebaw; Kumar, Shailender

doi:10.1109/icrito48877.2020.9197824

Cited by 34 publications

(17 citation statements)

References 46 publications

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“…This is one of the most frequently used RS image processing tasks in various application domains as the starting point of the process [87][88][89]. Image classification is also called scene classification [88] or land cover and land use classifications [90] in the literature, depending on the aim and the data used in the studies. About half of the papers in At-DL addressed the image classification tasks for images acquired from different sensors such as multispectral satellites [67,91,92], hyperspectral [71,93], and unmanned aerial vehicles (UAV) [34,94] images.…”

Section: Overview Of the Reviewed Papersmentioning

confidence: 99%

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

et al. 2021

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Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the networks and/or developing new techniques, such as attention mechanisms. Since the attention mechanism has been proposed, regardless of its type, it has been increasingly used for diverse RS applications to improve the performances of the existing DL methods. However, these methods are scattered over different studies impeding the selection and application of the feasible approaches. This study provides an overview of the developed attention mechanisms and how to integrate them with different deep learning neural network architectures. In addition, it aims to investigate the effect of the attention mechanism on deep learning-based RS image processing. We identified and analyzed the advances in the corresponding attention mechanism-based deep learning (At-DL) methods. A systematic literature review was performed to identify the trends in publications, publishers, improved DL methods, data types used, attention types used, overall accuracies achieved using At-DL methods, and extracted the current research directions, weaknesses, and open problems to provide insights and recommendations for future studies. For this, five main research questions were formulated to extract the required data and information from the literature. Furthermore, we categorized the papers regarding the addressed RS image processing tasks (e.g., image classification, object detection, and change detection) and discussed the results within each group. In total, 270 papers were retrieved, of which 176 papers were selected according to the defined exclusion criteria for further analysis and detailed review. The results reveal that most of the papers reported an increase in overall accuracy when using the attention mechanism within the DL methods for image classification, image segmentation, change detection, and object detection using remote sensing images.

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Section: Overview Of the Reviewed Papersmentioning

confidence: 99%

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

et al. 2021

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“…DL has been widely used in many applications since 2015 such as mapping land-cover (Li et al, 2016) and crops (Kussul et al, 2017) (Zhong, 2019), estimating crop yields (Kuwata and Shibasaki, 2015), detecting oil palm trees (Li et al, 2017) and plant diseases (Mohanty et al, 2016) with accuracies reached to 90%. A review to Different methods of deep learning for classifying land cover and land use of remote sensing data were presented in (Abebaw Alem and Shailender, 2020). An easy systematic review to the application of transfer learning for scene classification using different Dataset of Land cover and land Use and with different models of deep learning were presented in (De Lima and Marfurt, 2020).…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Model of Bidirectional Long-Short Term Memory and CNN for Multivariate Time Series Classification of Remote Sensing Data

Gharghory¹

2021

Journal of Computer Science

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Classification of multivariate time series has got massive attention in the last decade. The traditional modeling classifiers are complicated patterns and are incompetent to capture the dependencies of multivariate time series data. To include both of the effective features and the embedded relationships in the multivariate time series, a new hybrid model which incorporates both Convolutional Neural Network (CNN) and Bidirectional long-short term memory (BiLSTM) named Conv-BiLSTM is proposed in this study. The proposed Conv-BiLSTM is carried out for classifying the land cover multivariate time series of Landsat 8 satellite images. The efficacy of the proposed network is verified through its comparison with the-state-of-the-art methods using different cases of training dataset. The suggested network outperforms the classification techniques as Random Forest (RF), BiLSTM and the CNN and it has classification accuracy on average 6.5, 8 and 8.7% over that of those classifiers respectively. Moreover, the classification accuracy of the proposed Conv-BiLSTM network in F-Score metric is larger than that value of the state-of-the-art WEASEL+MUSE technique in average by 1.38%.

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“…A semantic segmentation problem involving more than two classes is known as a multi-class segmentation problem. A recurrent example of a multiclass segmentation problem is the land cover and land use classification [23], which includes the joint detection of building and roads [24].…”

Section: Introductionmentioning

confidence: 99%

“…Over time, different strategies to address these problems have been established. While problems with only two classes have been tackled using binary semantic segmentation models [21,22], problems with more than two classes have been approached with multiclass models [23,25]. Since the latter optimizes the overall performance, the accuracy highly depends on the separability of the classes.…”

Section: Introductionmentioning

confidence: 99%

Multi-Class Strategies for Joint Building Footprint and Road Detection in Remote Sensing

2021

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Building footprints and road networks are important inputs for a great deal of services. For instance, building maps are useful for urban planning, whereas road maps are essential for disaster response services. Traditionally, building and road maps are manually generated by remote sensing experts or land surveying, occasionally assisted by semi-automatic tools. In the last decade, deep learning-based approaches have demonstrated their capabilities to extract these elements automatically and accurately from remote sensing imagery. The building footprint and road network detection problem can be considered a multi-class semantic segmentation task, that is, a single model performs a pixel-wise classification on multiple classes, optimizing the overall performance. However, depending on the spatial resolution of the imagery used, both classes may coexist within the same pixel, drastically reducing their separability. In this regard, binary decomposition techniques, which have been widely studied in the machine learning literature, are proved useful for addressing multi-class problems. Accordingly, the multi-class problem can be split into multiple binary semantic segmentation sub-problems, specializing different models for each class. Nevertheless, in these cases, an aggregation step is required to obtain the final output labels. Additionally, other novel approaches, such as multi-task learning, may come in handy to further increase the performance of the binary semantic segmentation models. Since there is no certainty as to which strategy should be carried out to accurately tackle a multi-class remote sensing semantic segmentation problem, this paper performs an in-depth study to shed light on the issue. For this purpose, open-access Sentinel-1 and Sentinel-2 imagery (at 10 m) are considered for extracting buildings and roads, making use of the well-known U-Net convolutional neural network. It is worth stressing that building and road classes may coexist within the same pixel when working at such a low spatial resolution, setting a challenging problem scheme. Accordingly, a robust experimental study is developed to assess the benefits of the decomposition strategies and their combination with a multi-task learning scheme. The obtained results demonstrate that decomposing the considered multi-class remote sensing semantic segmentation problem into multiple binary ones using a One-vs-All binary decomposition technique leads to better results than the standard direct multi-class approach. Additionally, the benefits of using a multi-task learning scheme for pushing the performance of binary segmentation models are also shown.

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Deep Learning Methods for Land Cover and Land Use Classification in Remote Sensing: A Review

Cited by 34 publications

References 46 publications

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

A Hybrid Model of Bidirectional Long-Short Term Memory and CNN for Multivariate Time Series Classification of Remote Sensing Data

Multi-Class Strategies for Joint Building Footprint and Road Detection in Remote Sensing

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