Semantic segmentation on small datasets of satellite images using convolutional neural networks

Younis, Mohammed Chachan; Keedwell, Edward

doi:10.1117/1.jrs.13.046510

Cited by 15 publications

(7 citation statements)

References 26 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The success of AlexNet, VGG, GoogleNet, and ResNet's convolutional neural network for image classification and target detection has inspired researchers to explore the field of semantic segmentation with deep learning methods [ 8 ]. The key advantages of deep learning make it superior to conventional semantic segmentation methods.…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Semantic Segmentation Algorithm Based on Deep Convolutional Neural Networks

Yang¹,

Guo²

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

With the development of deep learning theory and the decrease of the cost of acquiring massive data, the image semantic segmentation algorithm based on Convolutional Neural Networks (CNNs) is gradually replacing the conventional segmentation algorithm by its high accuracy segmentation performance. By increasing the amount of training data and stacking more convolutional layers to form Deep Convolutional Neural Networks (DCNNs), a neural network model with higher segmentation accuracy can be obtained, but it faces the problems of serious memory consumption and long latency. For some special application scenarios, such as augmented reality and mobile interaction, real-time processing cannot be performed. To improve the speed of semantic segmentation while obtaining the most accurate segmentation results as possible, this paper proposes a semantic segmentation algorithm based on lightweight convolutional neural networks. Taking the computational complexity and segmentation accuracy into account, the algorithm starts from the perspective of extracting high-level semantic features and introduces a position-attention mechanism with richer contextual information to model the relationship between different pixels, avoiding the convolutional local perceptual field to be too small. To recover clearer target boundaries, a channel attention mechanism is introduced in the decoding part of the model to mine more useful feature channel information and effectively improve the fusion of low-level features with high-level features. By verifying the effectiveness of the above model on a publicly available dataset and comparing it with the more popular semantic segmentation methods, the model proposed in this paper has higher semantic segmentation accuracy and reflects certain advantages in objective evaluation.

show abstract

Section: Related Workmentioning

confidence: 99%

A Lightweight Semantic Segmentation Algorithm Based on Deep Convolutional Neural Networks

Yang¹,

Guo²

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…(Vooban 2017;Jiang 2017) used only 25 labelled satellite images for training. While (Younis and Keedwell 2019) modified the structure of SegNet architecture (Figure 3) and train it using 6 RGB images. The results of this study were also promising.…”

Section: Related Workmentioning

confidence: 99%

“…However, in remote sensing the ones available are typically very limited (Ball et al 2018). In such low to medium learning datasets contexts, some architectures like UNet and SegNet networks are frequently privileged (Younis and Keedwell 2019).…”

Section: Introductionmentioning

confidence: 99%

Intelligent mapping of irrigated areas from Landsat 8 images using transfer learning

Benbahria

Sebari²,

Hajji³

et al. 2021

International Journal of Engineering and Geosciences

View full text Add to dashboard Cite

The lack of reliable and up-to-date data in developing countries is a major obstacle to sustainable development. In Morocco, where groundwater withdrawals by farmers are very intensive and informal, maps describing and monitoring the extension of irrigated areas are scarce and labor-intensive to obtain. In this paper a novel transfer learning algorithm is proposed to map irrigated areas at different stages of an agricultural cycle from Landsat 8 images. The results obtained displays satisfactory performance over traditional machine learning algorithms. On a small dataset, we initially tested three well known deep learning architectures (SegNet, DenseNet and Unet). The results obtained were not satisfactory. So, to get high performance, we rely on a transfer learning architecture combining UNet with ResNet50 backbone (trained on 2012 ILSVRC ImageNet dataset) as a baseline after a phase where different configurations were tested. In the first part of this study, we compared the use of three optimization methods: Adam and two variants of Stochastic Gradient Descent (SGD) associated with two techniques (Cyclical Learning Rate and Warm Restart) to find the optimal learning rate and then test the impact of data augmentation on the overall accuracies. Data augmentation had improved the overall accuracy for the three methods. Adam based method from 94% to 97% with mean IoU of 0,79 (for all land cover classes) and 0,86 for irrigated areas class. For SGD based methods, the overall accuracy had increased from 91% to 94% with mean IoU of 0,75 (for all land cover classes) and 0,82 for irrigated areas class. As we are interested in having irrigated areas maps at different key periods of the agricultural cycle, we also explored, in the second part of this study, the temporal generalization of the best model.

show abstract

“…Similarly, Chen et al [47] used CNN and VGI models to identify buildings in Malawi and roads in Guinea. Younis et al [48] employed a neural network called SegNet, which has an encoder-decoder architecture, for the segmentation of buildings and roads in images.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Feature Extraction by Using Convolutional Neural Network: Extraction of Objects from Multiresolution Images of Urban Areas

Fan

2023

IJGI

View full text Add to dashboard Cite

The emergence of deep learning-based classification methods has led to considerable advancements and remarkable performance in image recognition. This study introduces the Multiscale Feature Convolutional Neural Network (MSFCNN) for the extraction of complex urban land cover data, with a specific emphasis on buildings and roads. MSFCNN is employed to extract multiscale features from three distinct image types—Unmanned Aerial Vehicle (UAV) images, high-resolution satellite images (HR), and low-resolution satellite images (LR)—all collected within the Fengshan District of Kaohsiung, Taiwan. The model in this study demonstrated remarkable accuracy in classifying two key land cover categories. Its success in extracting multiscale features from different image resolutions. In the case of UAV images, MSFCNN achieved an accuracy rate of 91.67%, with a Producer’s Accuracy (PA) of 93.33% and a User’s Accuracy (UA) of 90.0%. Similarly, the model exhibited strong performance with HR images, yielding accuracy, PA, and UA values of 92.5%, 93.33%, and 91.67%, respectively. These results closely align with those obtained for LR imagery, which achieved respective accuracy rates of 93.33%, 95.0%, and 91.67%. Overall, the MSFCNN excels in the classification of both UAV and satellite images, showcasing its versatility and robustness across various data sources. The model is well suited for the task of updating cartographic data related to urban buildings and roads.

show abstract

Semantic segmentation on small datasets of satellite images using convolutional neural networks

Cited by 15 publications

References 26 publications

A Lightweight Semantic Segmentation Algorithm Based on Deep Convolutional Neural Networks

A Lightweight Semantic Segmentation Algorithm Based on Deep Convolutional Neural Networks

Intelligent mapping of irrigated areas from Landsat 8 images using transfer learning

Multiscale Feature Extraction by Using Convolutional Neural Network: Extraction of Objects from Multiresolution Images of Urban Areas

Contact Info

Product

Resources

About