Enhancing DeepLabv3+ Convolutional Neural Network Model for Precise Apple Orchard Identification Using GF-6 Remote Sensing Images and PIE-Engine Cloud Platform
Abstract
:1. Introduction
2. Study Area and Data Source
2.1. Case Study Area
2.2. Data Source and Preprocessing
2.2.1. Field Survey
2.2.2. GF-6 Image
2.2.3. Data Preprocessing
2.3. Research Processing Tools
3. Methods
3.1. Overview
3.2. Construction of Apple Orchard Data Sample Set
3.2.1. Image Preliminary Classification
- Multiscale Image Segmentation
- 2.
- Feature Parameter Selection
- 3.
- Machine Learning Classifier
3.2.2. Manual Annotation Data Sample Set
3.3. Enhancing of DeepLabv3+ Model
3.3.1. ResNet
3.3.2. Construction of DeepLabv3+ Model
3.3.3. Model Enhancement
- (1)
- Cross-Entropy: The minimum cross-entropy is equivalent to minimizing the relative entropy between the actual output and the expected output, which can be measured by calculating the KL divergence of probability distributions and . Formula 1 presents the definition of cross-entropy when considering two discrete probability distributions.
- (2)
- Dice Loss: It is utilized for quantifying the similarity between the predicted outcome and the actual label, exhibiting a high sensitivity towards object boundary accuracy.
3.3.4. Hyperparameter Configuration
3.4. Model Performance Evaluation
- (1)
- Precision:
- (2)
- Recall:
- (3)
- Intersection over Union (IoU):
- (4)
- Mean Intersection over Union (mIoU):
4. Results
4.1. Enhanced Model Training Results
4.2. Analysis of the Ablation Experiment Results
4.3. Comparison of Identification Results
5. Discussions
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
CNN | Convolutional neural network |
PSPNet | Pyramid Scene Parsing Network |
GNSS | Global Navigation Satellite System |
BIE | Band enhancement |
KNN | K-nearest neighbor |
SVM | Support vector machine |
RF | Random forest |
ASPP | Atrous Spatial Pyramid Pooling |
PMS | panchromatic/8 m multispectral high-resolution camera |
WFV | Wide-format camera |
NDVI | Normalized vegetation index |
NDWI | Normalized water body index |
DVI | Difference vegetation index |
RVI | Ratio vegetation index |
GLCM | Gray-level co-occurrence matrix |
PA | Producer accuracy |
OA | Overall classification accuracy |
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Sensor | Band | Wave Length | Spatial Resolution | Width |
---|---|---|---|---|
PMS | Panchromatic | 2 m | 90 km | |
B1 (Blue) | ||||
B2 (Green) | 8 m | |||
B3 (Red) | ||||
B4 (Near-infrared) |
Characteristic Exponent | Calculation Formula |
---|---|
NDVI | |
NDWI | |
DVI | |
RVI | |
Mean | |
Variance | |
Homogeneity | |
Contrast | |
Dissimilarity | |
Entropy | |
Angular Second Moment | |
Correlation |
Preliminary Classification Algorithm | UA | PA | Kappa | OA |
---|---|---|---|---|
KNN | 86.08% | 86.00% | 0.88 | 87.88% |
SVM | 89.06% | 87.86% | 0.90 | 91.05% |
RF | 91.14% | 90.00% | 0.92 | 93.33% |
Component | ResNet34 | ResNet50 | ResNet101 |
---|---|---|---|
Total Layers | 34 | 50 | 101 |
Parameters | ~21.8 M | ~25.5 M | ~44.5 M |
Key Building Block | Basic Block (2 conv layers) | Bottleneck Block (3 conv layers) | Bottleneck Block (3 conv layers) |
Structure Details | - 3 × 3 conv ×2 per block - No 1 × 1 conv in shortcuts | - 1 × 1 + 3 × 3 + 1 × 1 conv per block - 1 × 1 conv in shortcuts | Same as ResNet50, but with more stacked blocks |
FLOPs | ~3.6 GFLOPs | ~4.1 GFLOPs | ~7.8 GFLOPs |
Model | Precision/% | Recall/% | mIoU/% |
---|---|---|---|
ResU-Net34 | 86.55% | 86.98% | 80.87% |
ResU-Net50 | 89.48% | 89.82% | 82.20% |
ResU-Net101 | 90.82% | 90.69% | 83.23% |
LinkNet34 | 88.46% | 88.13% | 82.96% |
LinkNet50 | 92.48% | 91.87% | 86.71% |
LinkNet101 | 92.52% | 92.19% | 85.92% |
DeepLabv3+_34 | 91.17% | 91.40% | 84.67% |
DeepLabv3+_50 | 92.55% | 92.70% | 86.79% |
DeepLabv3+_101 | 94.37% | 94.27% | 89.33% |
Township | Area of Apple Orchard (km2) | Township Area (km2) | Apple Orchard Area Proportion (%) |
---|---|---|---|
YangChu | 47.58 | 86.76 | 54.84 |
GuanDao | 55.95 | 113.47 | 49.31 |
GuanLi | 46.92 | 97.44 | 48.15 |
SheWoBo | 92.03 | 201.69 | 45.63 |
SiKou | 40.82 | 91.60 | 44.56 |
XiCheng | 44.75 | 91.27 | 49.03 |
SuJiaDian | 48.36 | 136.43 | 35.45 |
SongShan | 45.34 | 158.50 | 28.61 |
CuiPing | 23.97 | 89.74 | 26.71 |
TangJiaBo | 28.83 | 138.69 | 20.79 |
ZangJiaZhuang | 55.96 | 223.14 | 25.08 |
TingKou | 32.39 | 151.30 | 21.41 |
ZhuangYuan | 20.63 | 73.80 | 27.95 |
TaoCun | 35.12 | 277.56 | 12.65 |
MiaoHou | 10.67 | 85.27 | 12.51 |
Total | 629.32 | 2016.66 | 31.21 |
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Gao, G.; Chen, Z.; Wei, Y.; Zhu, X.; Yu, X. Enhancing DeepLabv3+ Convolutional Neural Network Model for Precise Apple Orchard Identification Using GF-6 Remote Sensing Images and PIE-Engine Cloud Platform. Remote Sens. 2025, 17, 1923. https://doi.org/10.3390/rs17111923
Gao G, Chen Z, Wei Y, Zhu X, Yu X. Enhancing DeepLabv3+ Convolutional Neural Network Model for Precise Apple Orchard Identification Using GF-6 Remote Sensing Images and PIE-Engine Cloud Platform. Remote Sensing. 2025; 17(11):1923. https://doi.org/10.3390/rs17111923
Chicago/Turabian StyleGao, Guining, Zhihan Chen, Yicheng Wei, Xicun Zhu, and Xinyang Yu. 2025. "Enhancing DeepLabv3+ Convolutional Neural Network Model for Precise Apple Orchard Identification Using GF-6 Remote Sensing Images and PIE-Engine Cloud Platform" Remote Sensing 17, no. 11: 1923. https://doi.org/10.3390/rs17111923
APA StyleGao, G., Chen, Z., Wei, Y., Zhu, X., & Yu, X. (2025). Enhancing DeepLabv3+ Convolutional Neural Network Model for Precise Apple Orchard Identification Using GF-6 Remote Sensing Images and PIE-Engine Cloud Platform. Remote Sensing, 17(11), 1923. https://doi.org/10.3390/rs17111923