Remote sensing image recommendation method based on content interpretation

doi:10.11772/j.issn.1001-9081.2023030313

Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (3): 722-731.DOI: 10.11772/j.issn.1001-9081.2023030313

• Artificial intelligence • Previous Articles Next Articles

Remote sensing image recommendation method based on content interpretation

Yuqiu LI¹, Liping HOU¹, Jian XUE¹, Ke LYU¹^,²(), Yong WANG³

^1.School of Engineering Science，University of Chinese Academy of Sciences，Beijing 100049，China
^2.Peng Cheng Laboratory，Shenzhen Guangdong 518055，China
^3.School of Artificial Intelligence，University of Chinese Academy of Sciences，Beijing 100049，China

Received:2023-03-24 Revised:2023-06-16 Accepted:2023-06-19 Online:2023-09-05 Published:2024-03-10
Contact: Ke LYU
About author:LI Yuqiu， born in 1998， M. S. candidate. His research interests include computer vision， remote sensing image processing.
HOU Liping， born in 1996， Ph. D. Her research interests include computer vision， deep learning， object detection， remote sensing image processing.
XUE Jian， born in 1979， Ph. D.， professor. His research interests include digital image processing， scientific computing visualization.
WANG Yong， born in 1975， Ph. D.， research fellow. His research interests include modeling and optimization of complex systems， pattern recognition， data mining.
Supported by:
National Natural Science Foundation of China(61731022)

基于内容解译的遥感图像推荐方法

李雨秋¹, 侯利萍¹, 薛健¹, 吕科¹^,²(), 王泳³

^1.中国科学院大学工程科学学院, 北京 100049
^2.鹏城实验室, 广东深圳 518055
^3.中国科学院大学人工智能学院, 北京 100049

通讯作者: 吕科
作者简介:李雨秋（1998—），男，山东德州人，硕士研究生，CCF会员，主要研究方向：计算机视觉、遥感图像处理
侯利萍（1996—），女，河南商丘人，博士，主要研究方向：计算机视觉、深度学习、目标检测、遥感图像处理
薛健（1979—），男，江苏宜兴人，教授，博士，CCF会员，主要研究方向：数字图像处理、科学计算可视化
王泳（1975—），男，山东济南人，研究员，博士，主要研究方向：复杂系统建模与优化、模式识别、数据挖掘。
基金资助:
国家自然科学基金资助项目(61731022)

Abstract

Abstract:

With the continuous development of remote sensing technology， there has been a significant increase in the volume of remote sensing data. Providing accurate and timely remote sensing information recommendation services has become an urgent problem to solve. Existing remote sensing image recommendation algorithms mainly focus on user portrait， overlooking the influence of image content semantics on recommendation results. To address these issues， a remote sensing image recommendation method based on content interpretation was proposed. Firstly， an object extraction module based on YOLOv3 was used to extract objects from remote sensing images. Then， the location distribution vectors of key objects were integrated as image content information. Additionally， a multi-element user interest portrait was constructed and dynamically adjusted based on the user’s active search history to enhance the personality of recommendation results. Finally， the image content information was matched with the inherent attribute information of image and the user profile model to achieve accurate and intelligent recommendations of remote sensing data. Comparative experiments were conducted on real order data， to compare the proposed method with the newer recommendation method based solely on image attribute information. Experimental results show that the proposed method achieves a 70% improvement in the discrimination of positive and negative samples on the experimental data compared to the recommendation method considering user portrait. When using 10% training data with similar consumption time， the recommendation error rate decreases by 4.0 - 5.6 percentage points compared to the comparison method. When using 100% training data， the recommendation error rate decreases by 0.6 - 1.0 percentage points. These results validate the feasibility and effectiveness of the proposed method.

Key words: remote sensing image, recommendation method, content interpretation, deep learning, object detection

摘要：

随着遥感技术的不断发展，遥感数据呈现出海量增多的趋势，如何提供精准及时的遥感信息推荐服务成为亟待解决的问题。现有的遥感图像推荐算法大多针对用户画像进行设计，忽视了图像内容的语义信息对推荐结果的影响。针对上述问题，提出一种基于内容解译的遥感图像推荐方法。首先，通过基于YOLOv3的目标检测模块对遥感图像进行目标提取；然后，整合关键目标的位置分布向量作为图像内容信息；同时，构建多元素的用户兴趣画像，并根据用户主动搜索历史进行动态调整，以提高推荐结果的个性化程度；最后，将图像内容信息与图像自带属性信息、用户画像模型进行匹配，实现遥感数据的精准智能推荐。在真实订单数据上与较新的仅基于图像属性信息的推荐方法进行对比实验，实验结果表明，所提方法在实验数据上取得的正负样本区分度比考虑用户画像的推荐方法提高了70%；在耗时基本相近的情况下，在使用10%训练数据时，推荐错误率与对比方法相比下降了4.0~5.6个百分点，而在使用100%训练数据时推荐错误率则下降了0.6~1.0个百分点，验证了所提方法的可行性与有效性。

关键词: 遥感图像, 推荐方法, 内容解译, 深度学习, 目标检测

CLC Number:

TP751.1

Yuqiu LI, Liping HOU, Jian XUE, Ke LYU, Yong WANG. Remote sensing image recommendation method based on content interpretation[J]. Journal of Computer Applications, 2024, 44(3): 722-731.

李雨秋, 侯利萍, 薛健, 吕科, 王泳. 基于内容解译的遥感图像推荐方法[J]. 《计算机应用》唯一官方网站, 2024, 44(3): 722-731.

Figures/Tables 17

Fig. 1 Content interpretation process of remote sensing image

Fig. 2 Schematic diagram of image region partition

Fig. 3 Basic structure of SE block

Fig. 4 Network architecture of RS-YOLO

Fig. 5 Process of building user demand model

Fig. 6 Process of recommending and distributing remote sensing images

Tab. 1 Theme element （X） values of some orders

用户	拍摄时间	经度（ $° E$ ）	纬度（ $° N$ ）	分辨率 $/ m$	图像来源
生态环境部卫星环境应用中心	2021-05-10	109.5—110.5	34.0—35.0	0.5/2	GF-2
	2021-06-20	103.0—104.5	30.0—31.5	1/4/3	JL-1
	2022-01-15	115.5—116.5	38.0—39.0	3/5	CycloMedia
	2022-05-30	112.0—113.0	36.0—37.0	1/4/3	Landsat-9
	2022-11-05	102.5—103.5	31.5—32.5	0.5/1/10	Google Earth
中国交通通信信息中心	2021-07-02	116.0—117.0	39.5—40.5	1/4/3	JL-1
	2022-04-20	116.5—117.5	40.5—41.5	2	Landsat-9
	2022-02-28	117.0—118.0	40.0—41.0	1/4/3	JL-1
	2022-03-08	116.0—117.0	39.5—40.5	0.5/2	GF-2
	2021-06-08	115.5—116.5	39.0—40.0	3/5	CycloMedia

Tab. 1 Theme element （X） values of some orders

用户	拍摄时间	经度（ $° E$ ）	纬度（ $° N$ ）	分辨率 $/ m$	图像来源
生态环境部卫星环境应用中心	2021-05-10	109.5—110.5	34.0—35.0	0.5/2	GF-2
	2021-06-20	103.0—104.5	30.0—31.5	1/4/3	JL-1
	2022-01-15	115.5—116.5	38.0—39.0	3/5	CycloMedia
	2022-05-30	112.0—113.0	36.0—37.0	1/4/3	Landsat-9
	2022-11-05	102.5—103.5	31.5—32.5	0.5/1/10	Google Earth
中国交通通信信息中心	2021-07-02	116.0—117.0	39.5—40.5	1/4/3	JL-1
	2022-04-20	116.5—117.5	40.5—41.5	2	Landsat-9
	2022-02-28	117.0—118.0	40.0—41.0	1/4/3	JL-1
	2022-03-08	116.0—117.0	39.5—40.5	0.5/2	GF-2
	2021-06-08	115.5—116.5	39.0—40.0	3/5	CycloMedia

Fig. 7 Some detection results of RS-YOLO

Tab. 2 Detection result comparison with different methods on DOTAv2.0

方法	mAP/%	AP₅₀/%	AP₇₅/%	帧率/（frame·s^-1）
YOLOv4^［27］	43.5	65.7	47.3	62.0
RFBNet^［36］	51.5	68.6	57.1	41.5
EfficientDet^［37］	52.1	70.1	57.5	56.5
YOLOX^［38］	51.2	69.6	55.7	60.0
RetinaNet^［39］	52.1	71.8	56.5	37.0
YOLOv3	42.7	63.8	45.9	55.0
YOLOv3（MobileNet v3）	44.6	66.1	47.7	89.9
YOLOv3 （MobileNet v3+ PANet）	48.1	68.9	52.3	74.0
RS-YOLO	52.2	69.9	56.5	70.0

Fig. 8 Some results of content interpretation

Tab. 3 Frequency and weight of each theme element

用户	元素项	不同组的需求频次				权重
用户	元素项	1	2	3	4	权重
生态环境部卫星环境应用中心	拍摄时间	18	13	13	17	0.22
	空间	20	20	20	20	0.30
	图像来源	15	19	12	17	0.23
	分辨率	17	20	20	10	0.25
	总样本数	20	20	20	20	—
中国交通通信信息中心	拍摄时间	5	8	9	8	0.24
	空间	10	10	10	10	0.31
	图像来源	8	7	4	6	0.20
	分辨率	9	10	8	5	0.25
	总样本数	10	10	10	10	—

Tab. 4 Comparative experiment results on parameters λ and α

$α$	$λ$	正样本推荐度（平均）	负样本推荐度（平均）	区分度（正/负）
0.5	0.05	0.434	0.034	12.76
	0.10	0.472	0.032	14.75
	0.20	0.453	0.035	12.94
	0.50	0.415	0.037	11.22
1.0	0.05	0.584	0.052	11.23
	0.10	0.659	0.049	13.45
	0.20	0.621	0.054	11.50
	0.50	0.546	0.056	9.75
1.5	0.10	0.846	0.066	12.82
1.5	0.20	0.784	0.072	10.89
2.0	0.10	1.033	0.083	12.45
2.0	0.20	0.950	0.090	10.56

Tab. 4 Comparative experiment results on parameters λ and α

$α$	$λ$	正样本推荐度（平均）	负样本推荐度（平均）	区分度（正/负）
0.5	0.05	0.434	0.034	12.76
	0.10	0.472	0.032	14.75
	0.20	0.453	0.035	12.94
	0.50	0.415	0.037	11.22
1.0	0.05	0.584	0.052	11.23
	0.10	0.659	0.049	13.45
	0.20	0.621	0.054	11.50
	0.50	0.546	0.056	9.75
1.5	0.10	0.846	0.066	12.82
1.5	0.20	0.784	0.072	10.89
2.0	0.10	1.033	0.083	12.45
2.0	0.20	0.950	0.090	10.56

Tab.5 Average correlation and recommendation of data to be distributed under different groups of training data

用户	关联度指标	样本正/负	不同组的平均关联度与推荐度
用户	关联度指标	样本正/负	1	2	3	4	5
生态环境部卫星环境应用中心	主题元素	正	0.548	0.598	0.534	0.534	0.619
	主题元素	负	0.072	0.012	0.061	0.063	0.034
	图像内容	正	0.803	0.785	0.794	0.767	0.810
	图像内容	负	0.108	0.037	0.021	0.107	0.073
	推荐度	正	0.676	0.692	0.664	0.651	0.715
	推荐度	负	0.090	0.025	0.041	0.085	0.054
中国交通通信信息中心	主题元素	正	0.591	0.489	0.577	0.574	0.626
	主题元素	负	0.032	0.007	0.003	0.016	0.015
	图像内容	正	0.672	0.721	0.701	0.683	0.745
	图像内容	负	0.080	0.106	0.061	0.042	0.033
	推荐度	正	0.632	0.605	0.639	0.629	0.686
	推荐度	负	0.056	0.057	0.032	0.029	0.024

Fig. 9 Comparison of recommendation results on positive and negative samples

Tab. 6 Comparison of average recommendation among different methods

方法	不同样本的平均推荐度		区分度
方法	正样本	负样本	区分度
文献［5］方法	0.548	0.072	7.61
文献［7］方法	0.553	0.076	7.28
本文方法	0.656	0.053	12.38

Tab. 7 Comparison of recommendation error rate among different methods

用户	组号	训练样本数占比/%	不同方法错误率/%
用户	组号	训练样本数占比/%	文献［5］方法	文献［7］方法	本文方法
国家减灾中心	1	10	19.8	14.0	10.0
	2	20	17.6	9.0	11.4
	3	50	11.0	5.4	7.4
	4	100	6.8	4.0	3.2
生态环境部卫星环境应用中心	1	10	13.6	17.0	11.6
	2	20	11.6	14.8	9.4
	3	50	6.4	9.8	9.2
	4	100	5.4	5.2	4.2
中国交通通信信息中心	1	10	17.8	19.2	13.6
	2	20	14.0	14.4	13.4
	3	50	6.8	8.4	9.2
	4	100	6.5	4.6	4.0

Fig. 10 Comparison of recommendation time among different methods

References 39

1	王密，杨芳. 智能遥感卫星与遥感影像实时服务［J］. 测绘学报， 2019， 48（12）： 1586-1594.
	WANG M， YANG F. Intelligent remote sensing satellite and remote sensing image real-time service［J］. Acta Geodaetica et Cartographica Sinica， 2019， 48（12）： 1586-1594.
2	李德仁，沈欣. 我国天基信息实时智能服务系统发展战略研究［J］. 中国工程科学， 2020， 22（2）： 138-143. 10.15302/j-sscae-2020.02.017
	LI D R， SHEN X. Research on the development strategy of real-time and intelligent space-based information service system in China ［J］. Strategic Study of CAE， 2020， 22（2）： 138-143. 10.15302/j-sscae-2020.02.017
3	程滔. 大数据时代遥感数据管理与服务模式探索［J］. 国土资源遥感， 2016， 28（4）： 202-206. 10.6046/gtzyyg.2016.04.31
	CHENG T. Exploring management and service mode for remote sensing data in big data era ［J］. Remote Sensing for Land and Resources， 2016， 28（4）： 202-206. 10.6046/gtzyyg.2016.04.31
4	成鹏飞，黄钰譞，刘正，等. 电子商务平台大数据个性化推荐方法研究［J］. 商学研究， 2021， 28（3）： 116-124. 10.3969/j.issn.1008-2107.2021.03.012
	CHENG P F， HUANG Y X， LIU Z， et al. Research on personalized recommendation method of big data on E-commerce platform ［J］. Commercial Science Research， 2021， 28（3）： 116-124. 10.3969/j.issn.1008-2107.2021.03.012
5	韦二龙，刘东，龙恩，等.基于用户画像的遥感信息精准服务系统设计［J］. 无线电工程，2021，51（8）：720-724. 10.3969/j.issn.1003-3106.2021.08.007
	WEI E L， LIU D， LONG E， et al. Design of precision service system based on user portrait for remote sensing information ［J］. Radio Engineering， 2021，51（8）： 720-724. 10.3969/j.issn.1003-3106.2021.08.007
6	杨晓霞，徐婷，李少达，等.用户模型驱动的遥感信息智能服务方法［J］.测绘学报，2015，44（11）：1285-1294. 10.11947/j.AGCS.2015.20140413
	YANG X X， XU T， LI S D， et al. A user profile-driven intelligent service of remote sensing information ［J］. Acta Geodaetica et Cartographica Sinica， 2015， 44（11）：1285-1294. 10.11947/j.AGCS.2015.20140413
7	龙恩，吕守业，岑鹏瑞，等. 顾及用户画像的多源遥感信息智能推荐方法［J］. 测绘学报， 2023， 52（2）： 297-306.
	LONG E， LÜ S Y， CEN P R， et al. An intelligent recommendation method of multi-source remote sensing information considering user portrait ［J］. Acta Geodaetica et Cartographica Sinica， 2023， 52（2）： 297-306.
8	ZHU X X， TUIA D， MOU L， et al. Deep learning in remote sensing： a comprehensive review and list of resources ［J］. IEEE Geoscience and Remote Sensing Magazine， 2017， 5（4）： 8-36. 10.1109/mgrs.2017.2762307
9	LU X， WANG B， ZHENG X， et al. Exploring models and data for remote sensing image caption generation ［J］. IEEE Transactions on Geoscience and Remote Sensing， 2018， 56（4）： 2183-2195. 10.1109/tgrs.2017.2776321
10	CHENG Q， HUANG H， XU Y， et al. NWPU-Captions dataset and MLCA-Net for remote sensing image captioning ［J］. IEEE Transactions on Geoscience and Remote Sensing， 2022， 60： 5629419. 10.1109/tgrs.2022.3201474
11	ZHAN Y， XIONG Z T， YUAN Y. RSVG： exploring data and models for visual grounding on remote sensing data ［EB/OL］. ［2023-02-11］. . 10.1109/tgrs.2023.3250471
12	冯号，黄朝兵，文元桥. 基于改进YOLOv3的遥感图像小目标检测［J］. 计算机应用， 2022， 42（12）：3723-3732.
	FENG H， HUANG C B， WEN Y Q. Remote sensing image small target detection based on improved YOLOv3 ［J］. Journal of Computer Applications， 2022， 42（12）： 3723-3732.
13	AHMED M， SERAJ R， ISLAM S M S. The k-means algorithm： a comprehensive survey and performance evaluation ［J］. Electronics， 2020， 9（8）： 1295. 10.3390/electronics9081295
14	XU S， WANG X， LV W， et al. PP-YOLOE： an evolved version of YOLO ［EB/OL］. ［2023-02-13］. .
15	CHAROU E， KABASSI K， MARTINIS A， et al. Integrating multimedia GIS technologies in a recommendation system for geotourism ［C］// Proceedings of the 2010 Conference on Multimedia Services in Intelligent Environments： Smart Innovation， Systems and Technologies. Berlin： Springer， 2010： 63-74. 10.1007/978-3-642-13396-1_3
16	SONG X， GUO Y， CHANG Y， et al. A hybrid recommendation system for marine science observation data based on content and literature filtering ［J］. Sensors， 2020， 20（22）： 6414. 10.3390/s20226414
17	CHEN X， LIU Y， LI F， et al. Remote sensing image recommendation based on spatial-temporal embedding topic model［J］. Computers & Geosciences， 2021， 157： 104935. 10.1016/j.cageo.2021.104935
18	中国电子科技集团公司第五十四研究所. 一种基于多属性融合的遥感影像推荐方法：CN202011037613.4［P］. 2021-11-09.
	The 54th Research Institute of China Electronics Technology Group Corporation. A remote sensing image recommendation method based on multi-attribute fusion： CN202011037613.4［P］. 2021-11-09.
19	北京数慧时空信息技术有限公司. 基于强化学习的遥感影像推荐系统及方法： CN202210953208.X ［P］. 2022-11-01.
	Beijing Shuhui Shikong Information Technology Company Limited. Remote sensing image recommendation system and method based on reinforcement learning： CN202210953208.X ［P］. 2022-11-01.
20	DALAL N， TRIGGS B. Histograms of oriented gradients for human detection ［C］// Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2005， 1： 886-893. 10.1109/cvpr.2005.4
21	LOWE D G. Distinctive image features from scale-invariant keypoints ［J］. International Journal of Computer Vision， 2004， 60： 91-110. 10.1023/b:visi.0000029664.99615.94
22	郭正胜，李参海. 基于深度学习的遥感图像匹配方法［J］. 测绘与空间地理信息， 2019，42（1）：138-141，146. 10.3969/j.issn.1672-5867.2019.01.038
	GUO Z S， LI C H. Deep learning based remote sensing image matching method［J］. Mapping and Spatial Geographic Information， 2019， 42（1）： 138-141，146. 10.3969/j.issn.1672-5867.2019.01.038
23	REDMON J， FARHADI A. YOLOv3： an incremental improvement ［EB/OL］. ［2022-12-14］. . 10.1109/cvpr.2017.690
24	REN S， HE K， GIRSHICK R， et al. Faster R-CNN： towards real-time object detection with region proposal networks ［C］// Proceedings of the 28th International Conference on Neural Information Processing Systems. Cambridge： MIT Press， 2015： 91-99.
25	LIU W， ANGUELOV D， ERHAN D， et al. SSD： single shot multiBox detector ［C］// Proceedings of the 14th European Conference on Computer Vision. Cham： Springer， 2016： 21-37. 10.1007/978-3-319-46448-0_2
26	REDMON J， FARHADI A. YOLO9000： better， faster， stronger［C］// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2017： 6517-6525. 10.1109/cvpr.2017.690
27	BOCHKOVSKIY A， WANG C-Y， LIAO H-Y M. YOLOv4： optimal speed and accuracy of object detection ［EB/OL］. ［2022-09-15］. .
28	Ultralytics. YOLOv5： state-of-the-art object detection model — YOLOv5 in PyTorch ［EB/OL］. ［2022-12-07］. .
29	HOWARD A， SANDLER M， CHEN B， et al. Searching for MobileNetV3 ［C］// Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision. Piscataway： IEEE， 2019： 1314-1324. 10.1109/iccv.2019.00140
30	LIU S， QI L， QIN H， et al. Path aggregation network for instance segmentation ［C］// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2018： 8759-8768. 10.1109/cvpr.2018.00913
31	LIN T-Y， DOLLÁR P， GIRSHICK R， et al. Feature pyramid networks for object detection ［C］// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2017： 936-944. 10.1109/cvpr.2017.106
32	HU J， SHEN L， SUN G. Squeeze-and-excitation networks ［C］// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2018： 7132-7141. 10.1109/cvpr.2018.00745
33	PADDLEPADDLE. PaddleDetection： object detection and instance segmentation toolkit based on PaddlePaddle ［EB/OL］. ［2022-11-22］. .
34	DENG J， DONG W， SOCHER R， et al. ImageNet： a large-scale hierarchical image database ［C］// Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2009： 248-255. 10.1109/cvpr.2009.5206848
35	DING J， XUE N， XIA G-S， et al. Object detection in aerial images： a large-scale benchmark and challenges ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2022， 44（11）： 7778-7796. 10.1109/tpami.2021.3117983
36	LIU S， HUANG D， WANG Y. Receptive field block net for accurate and fast object detection ［C］// Proceedings of the 15th European Conference on Computer Vision. Cham： Springer， 2018： 404-419. 10.1007/978-3-030-01252-6_24
37	TAN M， PANG R， LE Q V. EfficientDet： scalable and efficient object detection ［C］// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2020： 10778-10787. 10.1109/cvpr42600.2020.01079
38	GE Z， LIU S， WANG F， et al. YOLOX： exceeding yolo series in 2021 ［EB/OL］. ［2023-01-13］. .
39	LIN T-Y， GOYAL P， GIRSHICK R， et al. Focal loss for dense object detection ［C］// Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway： IEEE， 2017： 2999-3007. 10.1109/iccv.2017.324

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Remote sensing image recommendation method based on content interpretation

基于内容解译的遥感图像推荐方法

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