Survey on Remote Sensing Data Augmentation: Advances, Challenges, and Future Perspectives

Oubara, Amel; Wu, Falin; Amamra, Abdenour; Yang, Gongliu

doi:10.1007/978-3-031-12097-8_9

Cited by 5 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The existing data augmentation methods were mainly based on simple transformations of images. For example, geometric transformations, including flipping, scaling, translating, rotating and random cropping, and intensity transformations, including grayscale and color transformation [46]. These methods were relatively straightforward for data processing and could only increase the quantity of the As shown in Figure 6, the input feature map was operated by bilinear interpolation of sizes 2 × 2 and 4 × 4 for upsampling.…”

Section: Proposed Data Augmentationmentioning

confidence: 99%

Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique

Jia

Wei

2023

Remote Sensing

View full text Add to dashboard Cite

Forests are critical to mitigating global climate change and regulating climate through their role in the global carbon and water cycles. Accurate monitoring of forest cover is, therefore, essential. Image segmentation networks based on convolutional neural networks have shown significant advantages in remote sensing image analysis with the development of deep learning. However, deep learning networks typically require a large amount of manual ground truth labels for training, and existing widely used image segmentation networks struggle to extract details from large-scale high resolution satellite imagery. Improving the accuracy of forest image segmentation remains a challenge. To reduce the cost of manual labelling, this paper proposed a data augmentation method that expands the training data by modifying the spatial distribution of forest remote sensing images. In addition, to improve the ability of the network to extract multi-scale detailed features and the feature information from the NIR band of satellite images, we proposed a high-resolution forest remote sensing image segmentation network by fusing multi-scale features based on double input. The experimental results using the Sanjiangyuan plateau forest dataset show that our method achieves an IoU of 90.19%, which outperforms prevalent image segmentation networks. These results demonstrate that the proposed approaches can extract forests from remote sensing images more effectively and accurately.

show abstract

Section: Proposed Data Augmentationmentioning

confidence: 99%

Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique

Jia

Wei

2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…However, the Remote Sensing Change Detection (RSCD) task has been profoundly reshaped by the strides taken in deep learning. These advancements have launched a new era of methodologies characterized by heightened efficiency and precision [3].…”

Section: Introductionmentioning

confidence: 99%

“…These innovative methodologies effectively address concerns such as data diversity, blurriness, and distortions. However, it is important to note that the generated images often exhibit compromised quality [3]. Despite these efforts, the challenge of generating high-quality synthetic images for bolstering CD datasets remains.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Adversarial Learning-Based Change Detection in Imbalanced Datasets Using Artificial Image Generation and Attention Mechanism

Oubara,

Wu,

Maleki

et al. 2024

IJGI

View full text Add to dashboard Cite

Deep Learning (DL) has become a popular method for Remote Sensing (RS) Change Detection (CD) due to its superior performance compared to traditional methods. However, generating extensive labeled datasets for DL models is time-consuming and labor-intensive. Additionally, the imbalance between changed and unchanged areas in object CD datasets, such as buildings, poses a critical issue affecting DL model efficacy. To address this issue, this paper proposes a change detection enhancement method using artificial image generation and attention mechanism. Firstly, the content of the imbalanced CD dataset is enhanced using a data augmentation strategy that synthesizes effective building CD samples using artificial RS image generation and building label creation. The created building labels, which serve as new change maps, are fed into a generator model based on a conditional Generative Adversarial Network (c-GAN) to generate high-resolution RS images featuring building changes. The generated images with their corresponding change maps are then added to the CD dataset to create the balance between changed and unchanged samples. Secondly, a channel attention mechanism is added to the proposed Adversarial Change Detection Network (Adv-CDNet) to boost its performance when training on the imbalanced dataset. The study evaluates the Adv-CDNet using WHU-CD and LEVIR-CD datasets, with WHU-CD exhibiting a higher degree of sample imbalance compared to LEVIR-CD. Training the Adv-CDNet on the augmented dataset results in a significant 16.5% F1-Score improvement for the highly imbalanced WHU-CD. Moreover, comparative analysis showcases the superior performance of the Adv-CDNet when complemented with the attention module, achieving a 6.85% F1-Score enhancement.

show abstract

“…Advanced approaches, including Generative Adversarial Network (GAN), have emerged as a popular class of modern deep learning models for synthetically generating image data [20]. Various works on GAN extensions, such as Deep Convolution GANs (DCGANs), Auxiliary Classifier GAN (AC-GAN), CycleGANs, and Progressively-Growing GANs [21,22], have been adopted in the radar area and have shown a great ability to mimic complex real-world data. However, the fidelity of the generated data is not guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Advanced Human Activity Recognition through Data Augmentation and Feature Concatenation of Micro-Doppler Signatures

Souhila Korti,

Slimane

2023

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

Developing accurate classification models for radar-based Human Activity Recognition (HAR), capable of solving real-world problems, depends heavily on the amount of available data. In this paper, we propose a simple, effective, and generalizable data augmentation strategy along with preprocessing for micro-Doppler signatures to enhance recognition performance. By leveraging the decomposition properties of the Discrete Wavelet Transform (DWT), new samples are generated with distinct characteristics that do not overlap with those of the original samples. The micro-Doppler signatures are projected onto the DWT space for the decomposition process using the Haar wavelet. The returned decomposition components are used in different configurations to generate new data. Three new samples are obtained from a single spectrogram, which increases the amount of training data without creating duplicates. Next, the augmented samples are processed using the Sobel filter. This step allows each sample to be expanded into three representations, including the gradient in the x-direction (Dx), y-direction (Dy), and both x- and y-directions (Dxy). These representations are used as input for training a three-input convolutional neural network-long short-term memory support vector machine (CNN-LSTM-SVM) model. We have assessed the feasibility of our solution by evaluating it on three datasets containing micro-Doppler signatures of human activities, including Frequency Modulated Continuous Wave (FMCW) 77 GHz, FMCW 24 GHz, and Impulse Radio Ultra-Wide Band (IR-UWB) 10 GHz datasets. Several experiments have been carried out to evaluate the model's performance with the inclusion of additional samples. The model was trained from scratch only on the augmented samples and tested on the original samples. Our augmentation approach has been thoroughly evaluated using various metrics, including accuracy, precision, recall, and F1-score. The results demonstrate a substantial improvement in the recognition rate and effectively alleviate the overfitting effect. Accuracies of 96.47%, 94.27%, and 98.18% are obtained for the FMCW 77 GHz, FMCW 24 GHz, and IR- UWB 10 GHz datasets, respectively. The findings of the study demonstrate the utility of DWT to enrich micro-Doppler training samples to improve HAR performance. Furthermore, the processing step was found to be efficient in enhancing the classification accuracy, achieving 96.78%, 96.32%, and 100% for the FMCW 77 GHz, FMCW 24 GHz, and IR-UWB 10 GHz datasets, respectively.

show abstract

Survey on Remote Sensing Data Augmentation: Advances, Challenges, and Future Perspectives

Cited by 5 publications

References 18 publications

Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique

Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique

Enhancing Adversarial Learning-Based Change Detection in Imbalanced Datasets Using Artificial Image Generation and Attention Mechanism

Advanced Human Activity Recognition through Data Augmentation and Feature Concatenation of Micro-Doppler Signatures

Contact Info

Product

Resources

About