Unsupervised extraction of greenhouses using WorldView-2 images

Taşdemir, Kadim; Koç-San, Dilek

doi:10.1109/igarss.2014.6947597

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…Carvajal et al [1,2] used Artificial Neural Networks Classifier for greenhouse detection. In addition, there are studies that use machine learning algorithms [3][4][5] and unsupervised image classification [6,7] for greenhouse extraction. On the other hand, there is a considerable amount of research about greenhouse detection using Object-Based Image Classification [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Greenhouse Detection from Color Infrared Aerial Image and Digital Surface Model

Çelik

Koç-San

2019

The 6th International Electronic Conference on Sensors and Applications

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Greenhouse detection is important with respect to urban and rural planning, yield estimation and crop planning, sustainable development, natural resource management, risk analysis and damage assessment. The aim of this study is to detect greenhouse areas by using color and infrared orthophoto (RGB-NIR), topographic map and Digital Surface Model (DSM). The study was implemented in Kumluca district of Antalya, Turkey which includes intensive greenhouse areas. In this study, color and infrared orthophotos, normalized Digital Surface Model (nDSM), Normalized Difference Vegetation Index (NDVI) and Visible Red-based Built-up Index (VrNIR_BI) were used and the greenhouse areas were detected using Object Based Image Analysis (OBIA). In this process, the optimum scale parameter was determined automatically by the Estimation of Scale Parameter2 (ESP2) tool and Multi Resolution Segmentation (MRS) was used as the segmentation algorithm. In the classification stage, K-Nearest Neighbor (K-NN), Random Forest (RF) and Support Vector Machine (SVM) classification techniques were used and the accuracies of the classification results were compared. Obtained results showed that greenhouse areas can be determined from color and infrared orthophoto and DSM data successfully by using the OBIA. The highest overall accuracy was obtained when the SVM classifier was used with 94.80%.

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Section: Introductionmentioning

confidence: 99%

Greenhouse Detection from Color Infrared Aerial Image and Digital Surface Model

Çelik

Koç-San

2019

The 6th International Electronic Conference on Sensors and Applications

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“…Plastic-covered vineyards in rural areas were mapped by Tarantino and Figorito (2012) using object-based classifications on true colours aerial photographs. An unsupervised classification was carried out by Tasdemir and Koc-San (2014) in greenhouses detection. An unsupervised classification was carried out by Tasdemir and Koc-San (2014) in greenhouses detection.…”

Section: Introductionmentioning

confidence: 99%

Combining ad hoc spectral indices based on LANDSAT-8 OLI/TIRS sensor data for the detection of plastic cover vineyard

Novelli

Tarantino

2015

Remote Sensing Letters

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In this article, we are proposing a method using Landsat-8 Operational Land Imager and Thermal Infrared Sensor data for agricultural plastic cover detection. Four normalized difference indices were combined in the procedure described to achieve consistent results: the green Normalized Difference Vegetation Index and three ad hoc spectral indices purposely created for this study (rescaled brightness temperature, Plastic Surface Index and Normalized Difference Sandy Index). The sampling time related to the preliminary collection of spectral information on plastic surfaces was reduced using information gathered through the Quality Assessment and Cloud Quality bands. The overall accuracies observed were on average higher than 80%,and the low cost of the open data set used, lacking ancillary data, demonstrated the reliability of the proposed method, proving its suitability for environmental and agricultural monitoring over large areas.

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“…Determination of existing greenhouse areas is very important for urban and rural planning, sustainable development, yield estimation and planning and avoiding environmental problems related to greenhouse expansion (Agüera and Liu, 2009;KocSan, 2013a;Tasdemir and Koc-San, 2014). However, obtaining geographic data is very time consuming and expensive.…”

Section: Introductionmentioning

confidence: 99%

“…With the recent technological developments, the high resolution satellite images have become important data sources that can be used for obtaining and updating man-made objects and geographical data rapidly and effectively, for mapping applications and urban planning (Muyanga et al, 2007;, Liu et al, 2015. The high resolution satellite images can also be used to monitor the current situation and expansion of the greenhouse areas efficiently (Agüera and Liu, 2009;Koc-San, 2013a;Tasdemir and Koc-San, 2014). …”

Section: Introductionmentioning

confidence: 99%

Plastic and Glass Greenhouses Detection and Delineation From Worldview-2 Satellite Imagery

Koç-San

Sönmez

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VII, WG VII/4KEY WORDS: Greenhouses, SVM, RF, Boundary Tracing, Line Simplification, WorldView-2 ABSTRACT:Greenhouse detection using remote sensing technologies is an important research area for yield estimation, sustainable development, urban and rural planning and management. An approach was developed in this study for the detection and delineation of greenhouse areas from high resolution satellite imagery. Initially, the candidate greenhouse patches were detected using supervised classification techniques. For this purpose, Maximum Likelihood (ML), Random Forest (RF), and Support Vector Machines (SVM) classification techniques were applied and compared. Then, sieve filter and morphological operations were performed for improving the classification results. Finally, the obtained candidate plastic and glass greenhouse areas were delineated using boundary tracing and Douglas Peucker line simplification algorithms. The proposed approach was implemented in the Kumluca district of Antalya, Turkey utilizing pan-sharpened WorldView-2 satellite imageries. Kumluca is the prominent district of Antalya with greenhouse cultivation and includes both plastic and glass greenhouses intensively. When the greenhouse classification results were analysed, it can be stated that the SVM classification provides most accurate results and RF classification follows this. The SVM classification overall accuracy was obtained as 90.28%. When the greenhouse boundary delineation results were considered, the plastic greenhouses were delineated with 92.11% accuracy, while glass greenhouses were delineated with 80.67% accuracy. The obtained results indicate that, generally plastic and glass greenhouses can be detected and delineated successfully from WorldView-2 satellite imagery.

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Unsupervised extraction of greenhouses using WorldView-2 images

Cited by 8 publications

References 17 publications

Greenhouse Detection from Color Infrared Aerial Image and Digital Surface Model

Greenhouse Detection from Color Infrared Aerial Image and Digital Surface Model

Combining ad hoc spectral indices based on LANDSAT-8 OLI/TIRS sensor data for the detection of plastic cover vineyard

Plastic and Glass Greenhouses Detection and Delineation From Worldview-2 Satellite Imagery

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