Transfer Learning for Changes Detection in Optical Remote Sensing Imagery

Larabi, Mohammed El Amin; Chaib, Souleyman; Bakhti, Khadidja; Karoui, Moussa Sofiane

doi:10.1109/igarss.2019.8900296

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…A multi-temporal perspective on how those areas change over four time steps was conducted by Liu et al [174]. In general urban settings, settlement changes like new build areas or changes in building structures were investigated by employing change detection approaches which use both multispectral and optical [175][176][177] as well as radar data [178][179][180]. Fewer studies investigated specific industrial applications like the detection of power plants and chimneys [181][182][183], derived cadastral boundaries [184,185] or recently differentiated urban areas due to their local climate zones [186,187].…”

Section: Settlementmentioning

confidence: 99%

Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

2020

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In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on a fine-grained feature level which can help us to better understand the dynamics of land surfaces by taking object dynamics into account. To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN). In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. We extensively examine the spatial distribution of study sites, employed sensors, used datasets and CNN architectures, and give a thorough overview of applications in EO which used CNNs. Our main finding is that CNNs are in an advanced transition phase from computer vision to EO. Upon this, we argue that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research. With a focus on EO applications in this Part II, we complete the methodological review provided in Part I.

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Section: Settlementmentioning

confidence: 99%

Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

2020

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“…The first way consists of extracting implicit attributes through pretrained deep networks with other image datasets. For this study, the architectures chosen were standard ImageNet VGG16 (Hon and Khan, 2017, Tammina, 2019, Theckedath and Sedamkar, 2020 and standard MNIST LeNet (Krishna and Kalluri, 2019, Larabi et al, 2019, Sun et al, 2021, Tan et al, 2018. The second way was to extract explicit attributes from each signal window, both in the time domain and in the frequency domain.…”

Section: Proposed Methodsmentioning

confidence: 99%

ASTERI: image-based representation of EEG signals for motor imagery classification

2022

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Electroencephalography (EEG) signals are valuable in the monitoring and investigation of neurological diseases and in the control of brain-machine interfaces (BCI). However, these signals are noisy and are non-linear and non-stationary in nature. Signal analysis is an expensive task and can lead to misdiagnosis. Deep learning can be used to overcome these challenges. The most used deep architectures are based on convolutional neural networks (CNNs). Representing EEG signals as images can be useful to use deep architectures based on CNNs in solutions based on intelligent EEG-based systems. Methods: In this work, we propose the ASTERI method, for representing EEG signals in two dimensions using the backprojection reconstruction method. To validate the proposal, experiments were performed with motor imagery EEG signals from the BCI Competition IV 2b motor imagery database. To extract attributes from the EEG signal windows, we used the ASTERI representation and investigated the pre-trained networks VGG16 and LeNet. To classify the final classification of imagined movements to the right and to the left, we used Random Forests. Results: For the original database, the validation with 100 trees achieved an average accuracy and kappa index of 88.97% and 0.78, respectively. In the case of augmented databases with synthetic instances, the average accuracies were 89.10% and 89.13% for increases of 50% and 150% for each class, respectively. Conclusion: The experimental results showed that the ASTERI method, together with hybrid deep architectures based on CNN and Random Forests, can generate competitive results in solving motor imagery problems. The representation of signals as images can also con tribute to the exploration of new solutions of applied neuroscience problems based on deep architectures.

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“…Using pre-trained models (features) and fine-tuning on the target dataset is the most widely used way to exploit transfer learning. A recent study found fine-tuning pre-trained CNN models achieves the best performance for optical remote sensing change detection [19].…”

Section: B Transfer Learningmentioning

confidence: 99%

Enhanced Deep Learning Super-Resolution for Bathymetry Data

Williams

et al. 2022

2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)

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Spatial resolution is critical for observing and monitoring environmental phenomena. Acquiring high-resolution bathymetry data directly from satellites is not always feasible due to limitations on equipment, so spatial data scientists and researchers turn to single image super-resolution (SISR) methods that utilize deep learning techniques as an alternative method to increase pixel density. While super resolution residual networks (e.g., SR-ResNet) are promising for this purpose, several challenges still need to be addressed: (1) Earth data such as bathymetry is expensive to obtain and relatively limited in its data record amount; (2) certain domain knowledge needs to be complied with during model training; (3) certain areas of interest require more accurate measurements than other areas. To address these challenges, following the transfer learning principle, we study how to leverage an existing pre-trained superresolution deep learning model, namely SR-ResNet, for highresolution bathymetry data generation. We further enhance the SR-ResNet model to add corresponding loss functions based on domain knowledge. To let the model perform better for certain spatial areas, we add additional loss functions to increase the penalty of the areas of interest. Our experiments show our approaches achieve higher accuracy than most baseline models when evaluating using metrics including MSE, PSNR, and SSIM.

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Transfer Learning for Changes Detection in Optical Remote Sensing Imagery

Cited by 9 publications

References 9 publications

Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

ASTERI: image-based representation of EEG signals for motor imagery classification

Enhanced Deep Learning Super-Resolution for Bathymetry Data

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