2023
DOI: 10.1080/01431161.2023.2255354
|View full text |Cite
|
Sign up to set email alerts
|

Cnns in land cover mapping with remote sensing imagery: a review and meta-analysis

Ioannis Kotaridis,
Maria Lazaridou
Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
3
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
8

Relationship

0
8

Authors

Journals

citations
Cited by 10 publications
(3 citation statements)
references
References 164 publications
0
3
0
Order By: Relevance
“…Since the full convolutional neural network (FCN) [32] realizes the pixel-by-pixel semantic segmentation of images, many classical semantic segmentation models based on convolutional neural networks have been developed, such as UNet [33], Deeplabv3+ [34], FPN [35], PSPNet [36], DANet [37], UPerNet [38], and CCNet [39], extensively embraced in remote sensing semantic segmentation applications [40][41][42][43]. Remote sensing semantic segmentation models dedicated to landcover classification chiefly fall into distinct categories, including the pixel-based CNN, object-based CNN, graph-based CNN, siamese CNN, and ensembled CNN, with the ensembled CNN model emerging as adept at efficiently addressing complex landcover scenarios [44].…”
Section: 2 Research On Fine Landcover Classification With Semantic Se...mentioning
confidence: 99%
“…Since the full convolutional neural network (FCN) [32] realizes the pixel-by-pixel semantic segmentation of images, many classical semantic segmentation models based on convolutional neural networks have been developed, such as UNet [33], Deeplabv3+ [34], FPN [35], PSPNet [36], DANet [37], UPerNet [38], and CCNet [39], extensively embraced in remote sensing semantic segmentation applications [40][41][42][43]. Remote sensing semantic segmentation models dedicated to landcover classification chiefly fall into distinct categories, including the pixel-based CNN, object-based CNN, graph-based CNN, siamese CNN, and ensembled CNN, with the ensembled CNN model emerging as adept at efficiently addressing complex landcover scenarios [44].…”
Section: 2 Research On Fine Landcover Classification With Semantic Se...mentioning
confidence: 99%
“…Deep convolutional neural networks (CNNs) have played a significant role in RSI classification, owing to their exceptional performance in capturing local features (Kaul & Raina, 2022;Kotaridis & Lazaridou, 2023). This enables CNNs to learn from induced biases and be robust against translation variances.…”
Section: Introductionmentioning
confidence: 99%
“…Additionally, a couple of deep learning algorithms including models such as VGG16, ResNet152V2, long short-term memory (LSTM) and 2D-CNN have been widely used in multi-spectral image classification [46][47][48]. Among the algorithms above, CNN has been proven to perform well in computer vision tasks, such as image classification, object detection, and segmentation, due to its ability to capture local spatial relationships through convolutional operations [49]. Further, since vegetation remote sensing has multi-temporal and multi-modal characteristics, combining data from multiple sensors or acquisition dates for vegetation analysis has often been a technical challenge.…”
Section: Introductionmentioning
confidence: 99%