تلفیق اطلاعات طیفی و مکانی به‌منظور تفکیک محصولات کشاورزی با استفاده از تصاویر چندزمانة سنتینل‌ـ2 (مطالعة موردی: شهرستان قروه)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، گروه ژئودزی و مهندسی نقشه‌برداری، دانشگاه تفرش

2 استادیار، گروه ژئودزی و مهندسی نقشه‌برداری، دانشگاه تفرش

چکیده

امروزه کاربردهای تصاویر ماهواره‌ای، در پایش و مدیریت زمین‌های کشاورزی، رو به گسترش است. با توجه به قدرت تفکیک مکانی، طیفی و زمانی بالای تصاویر سنتینل‌ـ 2، در این مطالعه، در کشاورزی دقیق در شهرستان قروه از این تصاویر استفاده شده است. ابتدا با توجه به تقویم زراعی محصولات متفاوت آن منطقه، تصاویر سری زمانی جمع‌آوری شد. در روش پیشنهادی، نخست، فضای ویژگی طیفی براساس بازتاب طیفی باندها و همچنین شاخص‌های گیاهی، ایجاد شد. ابعاد فضای ویژگی طیفی، با استفاده از روش آنالیز مؤلفه‌های اصلی، کاهش یافت. سپس چهار طبقه‌بندی‌کنندة قدرتمند ماشین‌های بردار پشتیبان، شبکة عصبی پرسپترون چندلایه، نزدیک‌ترین k همسایه و جنگل‌های تصادفی نقشة طبقه‌بندی از اطلاعات طیفی تولید کردند. در ادامه، مکانی با هدف تعیین مرز مزارع، اطلاعات استخراج شد. برای این منظور، از شناسایی لبه‌ها در سری زمانی تصاویر سنتینل‌ـ 2 استفاده شد. در نهایت، نقشة طبقه‌بندی نهایی، با تلفیق اطلاعات مکانی و ادغام نتایج طبقه‌بندی‌کننده‌ها ایجاد شد. نتایج به‌دست‌آمده نشان داد که دقت طبقه‌بندی‌کننده‌های نزدیک‌ترین k همسایه، ماشین‌های بردار پشتیبان، شبکة عصبی پرسپترون چندلایه و جنگل‌های تصادفی روی فضای ویژگی طیفی اولیه، به‌ترتیب 78/77%، 16/79%، 41/76% و 89/76% است. با استفاده از روش پیشنهادی، دقت طبقه‌بندی به 72/94% افزایش پیدا کرد که حاکی از توانایی آن در منطقة مورد مطالعه است.

کلیدواژه‌ها

عنوان مقاله [English]

Fusion of spectral and spatial information for agricultural crop classification in multi-temporal Sentinel images (Case Study: Qorveh County)

نویسندگان [English]

  • Saeid Ahmadi 1
  • Hadiseh Hasani 2
1 M.Sc. Student, Geodesy and Surveying Engineering, Tafresh University, Tafresh
2 Assistant Prof., Dep. of Geodesy and Surveying Engineering, Tafresh University, Tafresh
چکیده [English]

Nowaday, there are wide applications for satellite images in agriculture monitoring and management. According to high spatial, spectral and temporal resolution of Sentinel-2 images, we used them for precise agriculture in Qorveh country. Proposed methd consist of five step: firstly, multi-temporal images are collected based on agriculture calender of crops. Then feature space is generated based on spectral reflectance and vegetation indices which consists of 70 features. According to high dimensionality of feature space, principle component analysis is applied to reduce its dimension. Four power classifiers include support vector machine, k-nearest neighbour, multi-layer perceptron and random forests classify the reduced spectral feature space. On the other hand, spatial information are extracted from multi-temporal multispectral images. For this pupose, strandard deviation (STD) maps are generated for red, NIR and SWIR bands of each epoch. Then, by averaging the STD maps, final STD map is obtained. Edge detection is performed on STD map and it improves by removing small lines, smoothing, thining, etc. Finally, crop mapping is done by fusion of four classification maps and agriculture farm boundaries. The obtained results show that classification accuracy of k-nearest neighbour, support vector machine, multi-layer perceptron and random forest classifiers are 77.78%, 79,16%, 76.41% and 76.89%, respectively. The overall accuracy of the proposed method improve up to 94.72% which proves high potential of the proposed method.

کلیدواژه‌ها [English]

  • Multi-temporal Sentinel-2 images
  • Agriculture farm boundary
  • Classifier fusion
  • Spatial information
  • Spectral information

مراجع

Asqari Sareskanroud, S. & Emami, H., 2019, Monitoring the Earth Surface Temperature and Relationship Land Use with Surface Temperature Using of OLI and TIRS Image, Journal of Applied Researches in Geographical Sciences, 19(53), PP 195-215.
Asokan, A. & Anitha, J., 2019, Change Detection Techniques for Remote Sensing Applications: A Survey, Earth Science Informatics, 12(2): 143-160.
Bargiel, D., 2017, A New Method for Crop Classification Combining Time Series of Radar Images and Crop Phenology Information, Remote Sensing of Environment, 198, PP. 369-383.
Bian, J., Zhang, Z., Chen, J., Chen, H., Cui, C. et al., 2019, Simplified Evaluation of Cotton Water Stress Using High Resolution Unmanned Aerial Vehicle Thermal Imagery, Remote Sensing, 11(3), P. 267.
 
Castillejo-González, L.L., López-Granados, F., García-Ferrer, A., Peña-Barragán, J.M., et al., 2009, Object-and Pixel-Based Analysis for Mapping Crops and their Agro-Environmental Associated Measures Using QuickBird Imagery, Computers and Electronics in Agriculture, 68(2), PP. 207-215.
Forkuor, G., Conrad, C., Thiel, M., Ullmann, T. & Zoungrana, E., 2014, Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa, Remote Sensing, 6(7), PP. 6472-6499.
Gao, F., 2021, Remote Sensing for Agriculture, Agro-geoinformatics: Theory and Practice.
Jafari, M., Zehtabian, GH. & Ehsani, A.H., 2013, Effect of Thermal Bonding and Supervised Classification Algorithms of Satellite Data in Making Land Use Maps (Case Study: Kashan), Iranian Journal of Rangeland and Desert Research, 20(1), PP. 72-87.
Joshi, P.K., Roy, P.S., Singh, S., Agrawal, S. & Yadav, D., 2006, Vegetation Cover Mapping in India Using Multi-Temporal IRS Wide Field Sensor (WiFS) Data, Remote Sensing of Environment, 2(2), PP. 190-202.
Karakuş, P., Karabork, H. & Kaya, S., 2017, A Comparison of the Classification Accuracies in Determining the Land Cover of Kadirli Region of Turkey by Using the Pixel Based and Object Based Classification Algorithms, International Journal of Engineering and Geosciences, 2(2), PP. 52-60.
Kavzoglu, T., Colkesen, I. & Tonbul, H., 2019, Agricultural Crop Type Mapping Using Object-Based Image Analysis With Advanced Ensemble Learning Algorithms, 40th Asian Conference on Remote Sensing, Korea.
Kenduiywo, B.K., Bargiel, D. & Soergel, U., 2017, Higher Order Dynamic Conditional Random Fields Ensemble for Crop Type Classification in Radar Images, IEEE Transactions on Geoscience and Remote Sensing, 55(8), PP. 4638-4654.
Khanal, S., Fulton, J.P. & Shearer, S., 2017, An Overview of Current and Potential Applications of Thermal Remote Sensing in Precision Agriculture, Computers and Electronics in Agriculture, 139, PP. 22-32.
Khanal, S., KC, K., Fulton, J.P., Shearer, S. & Ozkan, E., 2020, Remote Sensing in Agriculture—Accomplishments, Limitations, and Opportunities, Remote Sensing, 12(22), P. 3783.
Kussul, N., Lemoine, G., Gallego, F.J., Skakun, V. et al., 2016, Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(6), PP. 2500-2508.
Maktav, D., Erbek, F. & Jürgens, C., 2005, Remote Sensing of Urban Areas, International Journal of Remote Sensing, 26(4), PP. 655-659.
Masoud, K.M., Persello, C. & Tolpekin, V.A., 2020, Delineation of Agricultural Field Boundaries from Sentinel-2 Images Using a Novel Super-Resolution Contour Detector Based on Fully Convolutional Networks, Remote Sensing, 12(1), P. 59.
Mohammadnejad, V., Asghari, S. & Emami, H., 2019, Investigation Land Use Change with Use of a Pixel-Based Method and Object-Oriented Method and Analysis of the Effect of Land Use Change on Soil Erosion (Case Study of Maragheh County), Quantitative Geomorpological Research, 8(1), PP. 160-178.
Momeni, F., Dashtbani, S. & Banuey, A.S., 2018, The Importance of the Agricultural Sector in Maintaining the Socio-Economic Balance of Iran's Urban and Rural Structure, Space Economics and Rural Development, 6(22), PP. 17-46.
Nabavi, S.N. & Sarkaregar Ardakani, A., 2011, Identification and Estimation of Saffron Cultivation Area and Classification of the Region Using Satellite Images (Case Study: Kashmar), 2nd Conference on Environmental Planning and Management, Tehran.
Nezhad, N.M., Heydari, A., Fusilli, L. & Laneve, G., 2019, Land Cover Classification by Using Sentinel-2 Images: A Case Study in the City of Rome, Proceedings of the 4th World Congress on Civil, Structural, and Environmental Engineering (CSEE’19).
North, H.C., Pairman, D. & Belliss, S.E., 2019, Boundary Delineation of Agricultural Fields in Multitemporal Satellite Imagery, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(1), PP. 237-251.
Ok, A.O., Akar, O. & Gungor, O., 2012, Evaluation of Random Forest Method for Agricultural Crop Classification, European Journal of Remote Sensing, 45(1), PP. 421-432.
Ouzemou, J.E., El Harti, A., Lhissou, R., El Moujahid, A., Bouch, N., El Ouazzani, R. et al., 2018, Crop Type Mapping from Pansharpened Landsat 8 NDVI Data: A Case of a Highly Fragmented and Intensive Agricultural System, Remote Sensing Applications: Society and Environment, 11, PP. 94-103.
Peña, J.M., Gutiérrez, P.A., Hervás-Martínez, C., Six, J., Plant, R.E. & López-Granados, F., 2014, Object-Based Image Classification of Summer Crops with Machine Learning Methods, Remote Sensing, 6(6), PP. 5019-5041.
Pourakrami, S., Tavakoli Sabour, S.M. & Torahi, A.A., 2017, Agriculture Crop Type Classification by Normalized Vegetaion Index (in Sentinel-2 Images), The 2nd National Conference on Geospatial Information Technology (NCGIT), K.N.Toosi University of Technology, Iran.
Rahman, M.R., Islam, A.H.M.H. & Rahman, M.A., 2004, NDVI Derived Sugarcane Area Identification and Crop Condition Assessment, Plan Plus, 1(2), PP. 1-12.
Rao, N.R., Garg, P. & Ghosh, S.K., 2007, Development of an Agricultural Crops Spectral Library and Classification of Crops at Cultivar Level Using Hyper-spectral Data, Precision Agriculture, 8(4), PP. 173-185.
Roostaei, S., Mokhtari, D., Valizadeh Kamra, K., Khodaei Geshlag, L., 2019, Comparison of Pixel-Based Algorithm (Maximum Liklihood) and Object-Based Method (Support Vector Machine) in Classification of Land Use (Ahar-Varzeghan Area), Quantitative Geomorphological Research, 8(1), PP. 160-178.
Shafi, U., Mumtaz, R., Iqbal, N., Zaidi, S.M.H., Hussein, I. et al., 2020, A Multi-Modal Approach for Crop Health Mapping Using Low Altitude Remote Sensing, Internet of Things (IoT) and Machine Learning, IEEE Access, 8, PP. 112708-112724.
Sicre, C.M., Fieuzal, R. & Baup, F., 2020, Contribution of Multispectral (Optical and Radar) Satellite Images to the Classification of Agricultural Surfaces, International Journal of Applied Earth Observation and Geoinformation, 84, P. 101972.
Sishodia, R.P., Ray, R.L. & Singh, S.K., 2020, Applications of Remote Sensing in Precision Agriculture: A Review, Remote Sensing, 12(19), P. 3136.
Thenkabail,. PS., Smith, R.B. & De Pauw, E., 2000, Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics, Remote Sensing of Environment, 71(2), PP. 158-182.
Turker, M. & Kok, E.H., 2013, Field-Based Sub-Boundary Extraction from Remote Sensing Imagery Using Perceptual Grouping, ISPRS Journal of Photogrammetry and Remote Sensing, 79, PP. 106-121.
Vadivambal, R. & Jayas, D.S., 2011, Applications of Thermal Imaging in Agriculture and Food Industry—A Review, Food and Bioprocess Technology, 4(2), PP. 186-199.
Van Tricht, K., Gobin, A., Gilliams, S. & Piccard, I., 2018, Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium, Remote Sensing, 10(10), P. 1642.
 
Wójtowicz, M., Wójtowicz, A. & Piekarczyk, J., 2016, Application of Remote Sensing Methods in Agriculture, Communications in Biometry and Crop Science, 11(1), PP. 31-50.
Zafar, S. & Waqar, M.M., 2014, Crop Type Mapping by Integrating Satellite Data and Crop Calendar over Okara District, Punjab (Pakistan), Journal of Space Technology, 4(1), PP. 3-7.
Zhong, L., Hawkins, T., Biging, G. & Gong, P., 2011, A Phenology-Based Approach to Map Crop Types in the San Joaquin Valley, California, International Journal of Remote Sensing, 32(22), PP. 7777-7804.
 

فایل ها

هم رسانی

ارجاع به این مقاله

آمار