Edge Guided Context Aggregation Network for Semantic Segmentation of Remote Sensing Imagery
<p>Network architecture of the proposed edge guided context aggregation network (EGCAN). EGCAN contains three parts: the mainstream, the edge guided context aggregation branch, and the minority categories extraction branch.The input of the ECGAN is RGB images and the output is the corresponding segmentation result.</p> "> Figure 2
<p>An overview of the dual expectation maximization attention module (DEMA). DEMA contains two parts: SEMA and DEMA. The main steps of DEMA are step E and step M. Step E is purposed to calculate the spatial attention map z. Step M is purposed to maximize the maximum likelihood value obtained in step E to calculate the value of bases <math display="inline"><semantics> <mi>μ</mi> </semantics></math>.</p> "> Figure 3
<p>An overview of the hybrid spatial pyramid pooling module (HSPP). The input of HSPP is from the output feature of the dilated Resnet101 backone network. The output of HSPP is the concatenation of different scales and rates of features.</p> "> Figure 4
<p>Visual quality comparison of the proposed model over other different methods on the Tianzhi testing dataset.</p> "> Figure 5
<p>Visual quality comparison of the proposed model over other different methods on the Vaihingen testing dataset.</p> "> Figure 6
<p>Visual quality comparison of the proposed model over other different methods on the Potsdam testing dataset.</p> "> Figure 7
<p>Visual quality comparison of the proposed model over EEM on the Tianzhi testing dataset. (<b>a</b>) Unet, (<b>b</b>) Unet + sEEM, (<b>c</b>) Unet + EEM (without canny and dilation), (<b>d</b>) Unet + EEM (the whole).</p> "> Figure 8
<p>Visual quality comparison of the proposed model over DEMA on the Tianzhi testing dataset. (<b>a</b>) Unet, (<b>b</b>) Unet + SEMA, (<b>c</b>) Unet + CEMA, (<b>d</b>) Unet + SEMA||CEMA, (<b>e</b>) Unet + CEMA→SEMA, (<b>f</b>) Unet + SEMA→CEMA.</p> "> Figure 9
<p>Visual quality comparison of the proposed model over DEMA on the Tianzhi testing dataset. (<b>a</b>) Unet + EEM + DEM, (<b>b</b>) Unet + EEM + DEM + EGM1, (<b>c</b>) Unet + EEM + DEM + EGM1 + EGM2, (<b>d</b>) Unet + EEM + DEM + EGM1 + EGM2 + EGM3.</p> "> Figure 10
<p>Visual quality comparison of the proposed model over spatial pyramid pooling module on the Tianzhi testing dataset. (<b>a</b>) Unet + ASPP, (<b>b</b>) Unet + Non Local, (<b>c</b>) Unet + RCCA, (<b>d</b>) Unet + DNL, (<b>e</b>) Unet + HSPP.</p> "> Figure 10 Cont.
<p>Visual quality comparison of the proposed model over spatial pyramid pooling module on the Tianzhi testing dataset. (<b>a</b>) Unet + ASPP, (<b>b</b>) Unet + Non Local, (<b>c</b>) Unet + RCCA, (<b>d</b>) Unet + DNL, (<b>e</b>) Unet + HSPP.</p> ">
Abstract
:1. Introduction
- A novel architecture named edge guided context aggregation network (EGCAN) is proposed for RSI semantic segmentation. The advantage of the proposed network is that the edge information is employed as a priori knowledge to guide remote sensing image segmentation. The edge information is beneficial for effectively distinguishing background and different categories, especially the categories occupying a small percentage.
- A novel edge guided context aggregation branch is invented containing three modules, edge extraction module (EEM), dual expectation maximization attention module (DEMA) and edge guided module (EGM) to promote the accuracy of edge predictions, which enhances edge feature interdependencies and representation ability of the network along the spatial and channel directions.
- A hybrid spatial pyramid pooling (HSPP) module is investigated in minority categories segmentation branch, which is comprised of different-scale dilated convolutions and pooling operations to capture rich multi-scale contextual information for improving the proposed model’s discriminative capability of minority categories.
- Extensive experimental results on the dataset released in the TianZhi Cup Artificial Intelligence Challenge, ISPRS Vaihingen, and Potsdam benchmarks demonstrate the superiority of the proposed EGCAN over other state-of-the-art approaches.
2. Related Work
2.1. Semantic Segmentation
2.2. Attention Mechanism
2.3. RSI Semantic Segmentation
3. The Proposed Method
3.1. Overview
3.2. Edge Guided Context Aggregation Branch
3.2.1. Edge Extraction Module (EEM)
3.2.2. Dual Expectation Maximization Attention Module (DEMA)
3.2.3. Edge Guided Module (EGM)
3.3. Minority Categories Extraction Branch
4. Experiments and Results
4.1. Experimental Settings
4.2. Dataset Description
4.3. Evaluation Metrics
4.4. Experimental Results
5. Disussion
5.1. Ablation of Edge Extraction Module
5.2. Ablation of Dual Expectation Maximization Attention Module
5.3. Influence of Edge Guided Module
5.4. Effections of Hybrid Spatial Pyramid Pooling Module
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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CPU | Intel(R) Core(TM) i9-9900K CPU @ 3.60 GHz |
---|---|
RAM | 32 G |
Disk | 2 T |
GPU | GeForce GTX2080 Ti |
System | Ubuntu 16.04 |
Description | Farmland | Roads | Water | Vegetation | OA | mIoU |
---|---|---|---|---|---|---|
Deeplabv3+ | 65.2 | 34.9 | 93.8 | 73.4 | 83.7 | 66.8 |
PSPNet | 64.5 | 34.9 | 93.9 | 72.7 | 83.4 | 66.5 |
SegNet | 59.1 | 28.6 | 92.6 | 73.0 | 82.4 | 63.3 |
UNet | 60.0 | 20.5 | 92.5 | 64.4 | 79.2 | 59.4 |
ErfNet | 53.8 | 14.6 | 89.2 | 73.5 | 81.2 | 57.8 |
FCN | 58.9 | 24.6 | 92.4 | 74.8 | 83.0 | 62.7 |
Ours | 65.3 | 39.3 | 93.4 | 74.4 | 84.1 | 68.1 |
Method | Imp_surf | Building | Low_veg | Tree | Car | Clutter | OA | Mean F1 |
---|---|---|---|---|---|---|---|---|
SVL_3 | 86.6 | 91.0 | 77.0 | 85.0 | 55.6 | 58.9 | 84.8 | 79.0 |
DST_2 | 90.5 | 93.7 | 83.4 | 89.2 | 72.6 | 61.2 | 89.1 | 85.9 |
UZ_1 | 89.2 | 92.5 | 81.6 | 86.9 | 57.3 | 58.6 | 87.3 | 81.5 |
RIT_L7 | 90.1 | 93.2 | 81.4 | 87.2 | 72.0 | 63.4 | 87.8 | 84.8 |
ONE_7 | 91.0 | 94.5 | 84.4 | 89.9 | 77.8 | 71.9 | 89.8 | 87.5 |
ADL_3 | 89.5 | 93.2 | 82.3 | 88.2 | 63.3 | 69.6 | 88.0 | 83.3 |
DLR_10 | 92.3 | 95.2 | 84.1 | 90.0 | 79.3 | 79.3 | 90.3 | 88.2 |
CASIA2 | 93.2 | 96.0 | 84.7 | 89.9 | 86.7 | 84.7 | 91.1 | 90.1 |
BKHN10 | 92.9 | 96.0 | 84.6 | 89.8 | 88.8 | 81.1 | 91.0 | 90.4 |
TreeUNet | 92.5 | 94.9 | 83.6 | 89.6 | 85.9 | 82.6 | 90.4 | 89.3 |
Ours | 93.2 | 95.8 | 84.2 | 89.9 | 85.3 | 85.6 | 91.0 | 89.7 |
Method | Imp_surf | Building | Low_veg | Tree | Car | Clutter | OA | Mean F1 |
---|---|---|---|---|---|---|---|---|
SVL_1 | 83.5 | 91.7 | 72.2 | 63.2 | 62.2 | 69.3 | 77.8 | 74.6 |
DST_5 | 92.5 | 96.4 | 86.7 | 88.0 | 94.7 | 78.4 | 90.3 | 91.7 |
UZ_1 | 89.3 | 95.4 | 81.8 | 80.5 | 86.5 | 79.7 | 85.8 | 86.7 |
RIT_L7 | 91.2 | 94.6 | 85.1 | 85.1 | 92.8 | 83.2 | 88.4 | 89.8 |
SWJ_2 | 94.4 | 97.4 | 87.8 | 87.6 | 94.7 | 82.1 | 91.7 | 92.4 |
CASIA2 | 93.3 | 97.0 | 87.7 | 88.4 | 96.2 | 83.0 | 91.1 | 92.5 |
TreeUNet | 93.1 | 97.3 | 86.8 | 87.1 | 95.8 | 82.9 | 90.7 | 92.0 |
Cascade | 76.3 | 82.2 | 78.3 | 81.7 | 83.5 | 86.9 | 81.4 | 82.3 |
Correct | 83.5 | 88.2 | 84.9 | 86.1 | 85.6 | 87.5 | 85.9 | 86.4 |
EDENet | 95.6 | 96.3 | 88.4 | 89.6 | 83.5 | 87.6 | 90.1 | 91.8 |
Ours | 93.4 | 97.1 | 88.2 | 89.2 | 96.9 | 86.3 | 91.4 | 93.0 |
Methods | a | b | c | d |
---|---|---|---|---|
sEEM | ✓ | |||
EEM (without Canny and Dilation) | ✓ | |||
EEM (the whole) | ✓ | |||
mIoU (↑) | 59.4 | 60.7 | 61.9 | 63.2 |
Module Description | a | b | c | d | e | f |
---|---|---|---|---|---|---|
SEMA | ✓ | ✓ | ✓ | ✓ | ||
CEMA | ✓ | ✓ | ✓ | ✓ | ||
SEMA || CEMA | ✓ | |||||
CEMA → SEMA | ✓ | |||||
SEMA → CEMA | ✓ | |||||
mIoU (↑) | 59.4 | 60.6 | 61.1 | 62.8 | 64.7 | 65.3 |
Module Description | a | b | c | d |
---|---|---|---|---|
EGM1 | ✓ | ✓ | ✓ | |
EGM2 | ✓ | ✓ | ||
EGM3 | ✓ | |||
mIoU (↑) | 66.4 | 66.7 | 67.3 | 68.1 |
Module Description | (a) | (b) | (c) | (d) | (e) |
---|---|---|---|---|---|
mIoU | 64.3 | 63.5 | 62.8 | 66.7 | 68.1 |
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Liu, Z.; Li, J.; Song, R.; Wu, C.; Liu, W.; Li, Z.; Li, Y. Edge Guided Context Aggregation Network for Semantic Segmentation of Remote Sensing Imagery. Remote Sens. 2022, 14, 1353. https://doi.org/10.3390/rs14061353
Liu Z, Li J, Song R, Wu C, Liu W, Li Z, Li Y. Edge Guided Context Aggregation Network for Semantic Segmentation of Remote Sensing Imagery. Remote Sensing. 2022; 14(6):1353. https://doi.org/10.3390/rs14061353
Chicago/Turabian StyleLiu, Zhiqiang, Jiaojiao Li, Rui Song, Chaoxiong Wu, Wei Liu, Zan Li, and Yunsong Li. 2022. "Edge Guided Context Aggregation Network for Semantic Segmentation of Remote Sensing Imagery" Remote Sensing 14, no. 6: 1353. https://doi.org/10.3390/rs14061353