Object-based classification approach for greenhouse mapping using Landsat-8 imagery

Wu Chaofan, Deng Jinsong, Wang Ke, Ma Ligang, Amir Reza Shah Tahmassebi

Abstract


Suburban greenhouses with intensive agricultural productivity have increasingly influenced the daily diet and vegetable supply in Chinese cities. With their enormous input of fertilizers and pesticides, greenhouses have considerably changed the local soil quality and environmental risk factors. The ability to obtain timely and accurate information regarding the spatial distribution of greenhouses could make an important contribution to local agricultural management and soil protection. This paper attempts to present a practical framework for extracting suburban greenhouses, integrating remote sensing data from Landsat-8 and object-oriented classification. Inheritance classification was implemented, and various properties, including texture and neighborhood features in addition to spectral information, were investigated through the popular random forest technique for feature selection prior to SVM classification to improve the mapping accuracy. The results demonstrated that object-based classification incorporating non-spectral features yielded a significant improvement compared with the classification results obtained using only the spectral information in traditional per-pixel classification. Both the producer’s and user’s accuracy were higher than 85% for greenhouse identification. Although it remained a challenge to completely distinguish greenhouses from sparse plants, the final greenhouse map indicated that the proposed object-based classification scheme, providing multiple feature selections and multi-scale analysis, yielded worthwhile information when applied to a continuous series of the freely available Landsat-8 imagery data.
Keywords: greenhouse, mapping, Landsat-8, object-based classification, feature selection, multi-scale
DOI: 10.3965/j.ijabe.20160901.1414

Citation: Wu C F, Deng J S, Wang K, Ma L G, Tahmassebi A R S. Object-based classification approach for greenhouse mapping using Landsat-8 imagery. Int J Agric & Biol Eng, 2016; 9(1): 79－88.

Keywords


greenhouse, mapping, Landsat-8, object-based classification, feature selection, multi-scale

Full Text:

PDF

References


Du X M, Wu Z H, Zhang Y Q, Pei X X. Study on changes of soil salt and nutrient in greenhouse of different planting years. J. Soils Water Conserv., 2007; 2: 019.

Agüera F, Aguilar F J, Aguilar M A. Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses. ISPRS J. Photogrammetry Remote Sens., 2008; 63(6): 635–646.

Haack B, Bryant N, Adams S. An assessment of LANDSAT MSS and TM data for urban and near-urban land-cover digital classification. Remote Sens. Environ., 1987; 21(2): 201–213.

De Fries R S, Hansen M, Townshend J R G, Sohlberg R. Global land cover classifications at 8 km spatial resolution: the use of training data derived from LANDSAT imagery in decision tree classifiers. Int. J. Remote Sens., 1998; 19(16): 3141–3168.

Yuan F, Sawaya K E, Loeffelholz B C, Bauer M E. Land

cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan area by multitemporal LANDSAT remote sensing. Remote Sens. Environ., 2005; 98(2–3): 317–328.

Xian G, Homer C, Fry J. Updating the 2001 National Land Cover Database land cover classification to 2006 by using LANDSAT imagery change detection methods. Remote Sens. Environ., 2009; 113(6): 1133–1147.

Rodriguez-Galiano V F, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sanchez J P. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogrammetry Remote Sens., 2012; 67: 93–104.

Li J, Zhao G X, Li T, Yue Y D. Study on technique of extracting greenhouse vegetable information from LANDSAT TM image. J. Soils Water Conserv., 2004; 18(1): 126–129.

Ma Q. Land use information remote sensing extraction and effects of different land use on soil quality in greenhouse vegetable region. Master Dissertation. Shandong Agricultural University, 2011.

Carvajal F, Agüera F, Aguilar F J, Aguilar M A. Relationship between atmospheric corrections and training-site strategy with respect to accuracy of greenhouse detection process from very high resolution imagery. Int. J. Remote Sens., 2010; 31(11): 2977–2994.

Koc-San D. Evaluation of different classification techniques for the detection of glass and plastic greenhouses from WorldView-2 satellite imagery. J. Appl. Remote Sens., 2013; 7(1): 073553–073553.

Tarantino E, Aiello A. Plastic covered vineyard extraction from airborne sensor data with an object-oriented approach. In: Neale C M U, Maltese A, Richter K, ed. Proceedings of SPIE, International Society for Optics and Photonics, 2011; pp. 817415–817415

Agüera F, Liu J G. Automatic greenhouse delineation from QuickBird and Ikonos satellite images. Comput. Electron. Agric., 2009; 66(2): 191–200.

Roy D P, Wulder M A, Loveland T R, Woodcock C E, Allen R G, Anderson M C, et al. LANDSAT-8: science and product vision for terrestrial global change research. Remote Sens. Environ. 2014; 145: 154–172.

Blaschke T. Object based image analysis for remote sensing. ISPRS J. Photogrammetry Remote Sens., 2010; 65(1): 2–16.

Han N, Du H, Zhou G, Sun X, Ge H, Xu X. Object-based classification using SPOT-5 imagery for Moso bamboo forest mapping. Int. J. Remote Sens, 2014; 35(3): 1126–1142.

Benz U C, Hofmann P, Willhauck G, Lingenfelder I, Heynen M. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogrammetry Remote Sens., 2004; 58(3-4): 239–258.

Mitri G H, Gitas I Z. The development of an object-oriented classification model for operational burned area mapping on the Mediterranean island of Thasos using LANDSAT TM images. Forest Fire Research & Wildland Fire Safety, 2002: 1–12.

Geneletti D, Gorte B G H. A method for object-oriented land cover classification combining LANDSAT TM data and aerial photographs. Int. J. Remote Sens., 2003; 24(6): 1273–1286.

Mitri G H, Gitas I Z. A semi-automated object-oriented model for burned area mapping in the Mediterranean region using LANDSAT-TM imagery. Int. J. Wildland Fire, 2004; 13(3): 367–376.

Frohn R C, Reif M, Lane C, Autrey B. Satellite remote sensing of isolated wetlands using object-oriented classification of LANDSAT-7 data. Wetlands, 2009; 29(3): 931–941.

Zhu Z, Woodcock C E. Object-based cloud and cloud shadow detection in LANDSAT imagery. Remote Sens. Environ., 2012; 118: 83–94.

Tarantino E, Figorito B. Mapping rural areas with widespread plastic covered vineyards using true color aerial data. Remote Sens., 2012; 4(12): 1913–1928.

Brower B V, Laben C A. Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. Google Patents, 2000.

Imaging D. eCognition Developer Software: 8.7.1. Reference Book. Raunheim:Trimble Germany GmbH, 2012.

Laliberte A S, Browning D M, Rango A. A comparison of three feature selection methods for object-based classification of sub-decimeter resolution UltraCam-L imagery. Int. J. Appl. Earth Observation Geoinformation, 2012; 15: 70–78.

Novack T, Esch T, Kux H, Stilla U. Machine learning comparison between WorldView-2 and QuickBird-2- simulated imagery regarding object-based urban land cover classification. Remote Sens., 2011; 3(12): 2263–2282.

Su W, Li J, Chen Y, Liu Z, Zhang J, Low T M et al. Textural and local spatial statistics for the object‐oriented classification of urban areas using high resolution imagery. Int. J. Remote Sens., 2008; 29(11): 3105–3117.

Gislason P O, Benediktsson J A, Sveinsson J R. Random forests for land cover classification. Pattern Recognit. Lett. 2006; 27(4): 294–300.

Breiman L. Random forests. Mach. Learn. 2001; 45(1): 5–32.

Livingston F. Implementing Breiman’s Random Forest Algorithm into Weka, 2005.

Löw F, Michel U, Dech S, Conrad C. Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using support vector machines. ISPRS J. Photogrammetry Remote Sens., 2013; 85: 102–119.

Pal M, Mather P M. Support vector machines for classification in remote sensing. Int. J. Remote Sens., 2005; 26(5): 1007–1011.

Agüera F, Aguilar M A, Aguilar F J. Detecting greenhouse changes from QuickBird imagery on the Mediterranean coast. Int. J. Remote Sens., 2006; 27(21): 4751–4767.

Cai S, Liu D. A comparison of object-based and contextual pixel-based classifications using high and medium spatial resolution images. Remote Sens. Lett., 2013; 4(10): 998–1007.




Copyright (c)



2023-2026 Copyright IJABE Editing and Publishing Office