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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (3) : 80-89     DOI: 10.6046/gtzyyg.2020.03.11
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Remote sensing image scene classification based on Inception-V3
CAI Zhiling1(), WENG Qian1(), YE Shaozhen1, JIAN Cairen2
1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China
2. School of Information Science and Technology, Xiamen University Tan Kahkee College, Zhangzhou 363105, China
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Abstract  

With the deepening and cross-fusion of modern remote sensing image research, the classification of high spatial resolution remote sensing image (referred to as “high-resolution image”) has become a research hotspot in the field of remote sensing. As the phenomenon of “homology spectrum” and “homology spectrum” of high-resolution image is more serious, the deep learning method that has emerged in recent years has proposed a new solution for high-resolution image classification. However, the lack of training samples of remote sensing images can easily lead to over-fitting of deep learning networks. In this paper, an improved Inception-V3 remote sensing image scene classification model is proposed by using deep learning method and transfer learning strategy. The model first adds Dropout layer before the full connection layer of the original Inception-V3 model in order to avoid over-fitting. In the training process, the transfer learning strategy is adopted to make full use of the existing model and knowledge and improve the training efficiency. The experimental results based on AID and NWPU-RESISC45 datasets show that the improved Inception-V3 has faster convergence speed and smoother training effect than the original Inception-V3 training. Compared with accuracy of other traditional methods and deep learning networks, the classification accuracy of the proposed model has been greatly improved and verified. The effectiveness of the model is verified.
Keywords deep learning      transfer learning      convolutional neural network      Inception-V3      remote sensing image classification      scene classification     
:  TP79  
Corresponding Authors: WENG Qian     E-mail: cai_zhi_ling@163.com;fzuwq@fzu.edu.cn
Issue Date: 09 October 2020
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Zhiling CAI
Qian WENG
Shaozhen YE
Cairen JIAN
Cite this article:   
Zhiling CAI,Qian WENG,Shaozhen YE, et al. Remote sensing image scene classification based on Inception-V3[J]. Remote Sensing for Land & Resources, 2020, 32(3): 80-89.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.03.11     OR     https://www.gtzyyg.com/EN/Y2020/V32/I3/80
Fig.1  Inception-V3 network architecture
Fig.2  Dropout and BN schematics
Fig.3  Freezing and fine-tuning in transfer learning
Fig.4  Sketch of the fine-tuning steps
Fig.5  Flow chart of scene classification for high-resolution remote sensing images
Fig.6  Inception and Inception-L1 training on AID datasets
Fig.7  Inception and Inception-L1 training on NWPU-RESISC45 datasets
Fig.8  Different Dropout rate training on AID datasets
Fig.9  Different Dropout rate training on NWPU-RESISC45 datasets
Inception-L1 test_accuracy
Dropout率0.1 Dropout率0.2 Dropout率0.4
AID 94.30±0.25 94.44±0.23 94.40±0.31
NWPU-RESISC45 93.70±0.28 93.91±0.15 3.95±0.26
Tab.1  Comparison of classification accuracy of different Dropout rates(%)
方法 test_accuracy 方法 test_accuracy
AID NWPU-RESISC45 AID NWPU-RESISC45
LBP 26.26±0.52 21.74±0.18 SPM+CH 41.27±0.49 41.82±0.21
CH 34.29±0.40 27.52±0.14 VLAD+CH 44.78±0.28 50.57±0.48
SIFT 13.24±0.74 11.48±0.21 AlexNet 86.34±0.43 79.24±0.10
GIST 30.61±0.63 17.88±0.22 VGG-16 86.87±0.41 82.21±0.32
BoVW+CH 47.77±0.52 49.87±0.23 GooLeNet 83.84±0.36 78.47±0.28
IFK+CH 64.83±0.42 66.47±0.27 ResNet50 89.70±1.05 88.35±0.49
LLC+CH 49.36±0.57 46.81±0.30 Inception-V3 94.18±0.40 93.40±0.28
pLSA+CH 42.87±0.54 41.97±0.43 Inception-L1 94.44±0.23 93.95±0.15
Tab.2  Comparison of classification accuracy(%)
类别 准确率 类别 准确率
森林 100 教堂 96
裸地 100 储存罐 96
棒球场 100 飞机场 95
沙滩 100 桥 95
山脉 100 港口 95
稀疏住宅区 100 火车站 95
草地 100 商业区 93
体育场 99 密集住宅区 92
高架桥 99 中型住宅区 92
停车场 99 工业区 91
池塘 98 学校 85
河流 98 景区 82
操场 97 广场 82
沙漠 97 中心区 78
农田 97 公园 76
Tab.3  Classification results of Inception-L1 in AID dataset(%)
Fig.10  Legend of center, church, park and resort
类别 准确率 类别 准确率
灌木丛 100 湖泊 95
圆形农田 99 高速公路 95
小岛 99 船 95
梯田 99 网球场 95
海冰 99 沙漠 94
棒球内场 98 稀疏住宅区 94
云 98 火电站 94
高尔夫球场 98 飞机 94
田径场 98 飞机场 94
港口 98 工业区 93
活动房区 98 山脉 93
停车场 98 跑道 92
雪堡 98 矩形农田 91
体育场 97 中型住宅区 91
储存罐 97 铁道 90
环岛 97 河流 90
草地 96 湿地 89
篮球场 96 密集住宅区 88
森林 96 火车站 88
桥 96 商业区 87
立交桥 96 教堂 79
十字路口 95 宫殿 71
沙滩 95
Tab.4  Classification results of Inception-L1 in NWPU-RESISC45 dataset(%)
Fig.11  Legend of church and palace
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