Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review
Abstract
:1. Introduction
2. Attention Mechanism in Deep Learning
- (i)
- The softness of attention: the initial attention mechanism proposed by [20] is a soft version, which is also known as deterministic attention. This network considers all input elements (computes the average for each weight) to compute the final context vector. The context vector is the high-dimensional vector representation of the input elements or sequences of the input elements and in general the attention mechanism aims to add more contextual information to compute the final context vector. However, hard attention, which is also known as stochastic attention, randomly selects from the sample elements to compute the final context vector [40]. This, therefore, reduces the computational time. Furthermore, there is another categorization that is frequently used in computer vision tasks and RS image processing, i.e., global and local attentions [41,42]. Global attention is similar to soft attention since it also considers all input elements. However, global attention simplifies soft attention by using the output of the current time step rather than the prior one, while local attention is a combination of soft and hard attentions. This approach considers a subset of input elements at a time, and thus, overcomes the limitation of hard attention, i.e., being nondifferentiable, and in the meantime is less computationally expensive.
- (ii)
- Forms of input features: attention mechanisms can be grouped based on their input requirements: item-wise and location-wise. Item-wise attention requires inputs that are known to the model explicitly or produced with a preprocess [43,44,45]. However, location-wise attention does not necessarily require known inputs, in this case, the model needs to deal with input items that are difficult to distinguish. Due to the characteristics and features of the RS images and targeted tasks, location-wise attention is commonly used for RS image processing [42,46,47,48].
- (iii)
- Input representations: there are single-input and multi-input attention models [49,50]. In addition, the general processing procedure of the inputs also varies between the developed models. Most of the current attention networks work with single-input, and the model processes them in two independent sequences (i.e., distinctive model). The co-attention model is a multi-input attention network that parallelly implements the attention mechanism on two different sources but finally merges them [50]. This makes it suitable for change detection from RS images [51]. A self-attention network computes attentions only based on the model inputs, and thus, it decreases the dependence on external information [52,53,54]. This allows the model to perform better in images with complex background by focusing more on targeted areas [55]. Hierarchical attention mechanism computes weights from the original input and different levels/scales of the inputs [56]. This attention mechanism is also known as fine-grained attention for image classification [57].
- (iv)
- Output representations: single-output is the commonly used output representation in attention mechanisms. It processes a single feature at a time and computes weight scores. There are also two other multidimensional and multi-head attention mechanisms [21]. Multi-head attention processes the inputs linearly in multiple subsets, and finally merges them to compute the final attention weights [58], and is especially useful when employing the attention mechanism in conjunction with CNN methods [59,60,61]. Multidimensional attention, which is mostly employed for natural language processing, computes weights based on matrix representation of the features instead of vectors [62,63].
3. Deep Neural Network Architectures with Attention for RS Image Processing
4. Methodology
4.1. Research Questions
4.2. Search Strategy
- Search string:
4.3. Study Selection Criteria
4.4. Data Extraction
4.5. Data Synthesis
5. Results and Discussion
5.1. Overview of the Reviewed Papers
5.2. RQ1. What Are the Specific Objectives in Remote Sensing Image Processing That Are Addressed with Attention-Based Deep Learning?
- (i)
- Image classification: refers to labeling a group of pixels (objects or patches) in the RS images using training samples (e.g., land cover and land use classification). 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. The large amount of the freely available benchmark data sets and organized competitions in this regard attracts researchers to develop DL methods in this subject area.
- (ii)
- Object detection: refers to the detection of different objects in an image. It is the second most popular task that is addressed using At-DL including general object/target detection from RS images [46,60,95] or detection of the specific objects and features such as buildings [74,96], ships [97,98], landslides [99], clouds [53,100], airports [101], roads [72] and trees [102].
- (iii)
- (iv)
- Image fusion: is mostly known as a fundamental preprocess in the RS field, and aims to produce higher spectral and spatial resolutions. There are two main image fusion tasks that were addressed using At-DL in 13 papers. One is pan-sharpening that aims to fuse a coarse resolution multispectral image with a correspondingly high-resolution panchromatic image to produce a high-resolution multispectral image [106,107,108]. Another one is image super-resolution which refers to enhancing the resolution of the original image using At-DL methods [106,107,109].
- (v)
- Change detection: refers to detecting and quantifying the changes in multi-temporal RS images. This is one of the challenging tasks and with the increasing amount of multi-temporal RS images has become more popular. At-DL was used in 7 papers to detect changes in general [110,111], in buildings [51], or any other objects [81,112].
- (vi)
5.3. RQ2. What Are the Deep Learning Algorithms That Are Improved with Attention Mechanism for Remote Sensing Image Processing?
5.4. RQ3. Which Types of Attention Mechanisms Were Used in Deep Learning Methods for Remote Sensing Image Processing?
5.5. RQ4. What Are the Used Data Sets/Types in Attention-Based Deep Learning Methods for Remote Sensing Image Processing?
5.6. RQ5. What Are the Effects of the Attention Mechanism in the Performance of the Deep Learning Methods in Remote Sensing Image Processing?
5.7. Threats to Validity of This Review
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Data Extraction Form
# | Extraction Element | Contents |
---|---|---|
General information | ||
1 | ID | Unique ID for the study |
2 | Title | Full title of the article |
3 | Authors | The authors of the article |
4 | Year | The publication year |
5 | Journal name | The journal name (e.g., Journal of Dairy Science) |
Study description | ||
6 | Study target | ☐Image classification ☐Image segmentation ☐Object detection ☐Image fusion ☐Change detection ☐Other |
7 | Details about the study | E.g., any interesting findings or problems |
8 | Directly address RS image processing | ☐Yes ☐No |
9 | Deep learning algorithm | ☐CNN ☐RNN ☐GAN ☐GNN ☐Other |
10 | Attention type | ☐Spatial ☐Channel ☐Combined |
11 | Remote sensing image type | ☐MS Satellite ☐Aerial ☐Hyperspectral ☐SAR ☐UAV ☐Other |
12 | Remote sensing image spatial resolution | ☐High (<10 m) ☐Medium (10–30 m) ☐Low (>30 m) |
13 | Overall accuracy (%) | The overall accuracy of the produced results using At-DL method |
14 | Effect of attention mechanism (%) | The increased rate of the overall accuracy when used attention mechanism. |
15 | Additional notes | E.g., the opinions of the reviewer about the study |
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ID | Criterion |
---|---|
EC1. | Papers in which the full text is unavailable |
EC2. | Papers are not written in English |
EC3. | Papers are not aiming to directly contribute to remote sensing image processing |
EC4. | Papers do not directly use attention mechanism within DL methods |
EC5. | Papers do not validate the proposed study |
EC6. | Papers that provide a general summary without a clear contribution |
EC7. | Review, conference, and editorial papers |
Journal Name | Number of Papers |
---|---|
Remote Sensing | 44 |
IEEE Transactions on Geoscience and Remote Sensing | 33 |
IEEE Access | 27 |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | 17 |
IEEE Geoscience and Remote Sensing Letters | 14 |
Sensors | 6 |
ISPRS Journal of Photogrammetry and Remote Sensing | 5 |
International Journal of Remote Sensing | 3 |
IET Image Processing | 2 |
ISPRS International Journal of Geo-Information | 2 |
Journal of Applied Remote Sensing | 2 |
Remote Sensing of Environment | 2 |
Symmetry | 2 |
Other | 17 |
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Ghaffarian, S.; Valente, J.; van der Voort, M.; Tekinerdogan, B. Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review. Remote Sens. 2021, 13, 2965. https://doi.org/10.3390/rs13152965
Ghaffarian S, Valente J, van der Voort M, Tekinerdogan B. Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review. Remote Sensing. 2021; 13(15):2965. https://doi.org/10.3390/rs13152965
Chicago/Turabian StyleGhaffarian, Saman, João Valente, Mariska van der Voort, and Bedir Tekinerdogan. 2021. "Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review" Remote Sensing 13, no. 15: 2965. https://doi.org/10.3390/rs13152965