Identifying potential areas of <i>Cannabis sativa</i> plantations using object-based image analysis of SPOT-5 satellite data

Lisita, Alessandra; Sano, Edson Eyji; Durieux, Laurent

doi:10.1080/01431161.2013.790574

Cited by 19 publications

(10 citation statements)

References 17 publications

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“…The minimum mapping unit of object-based approach is not a pixel but the object composed by groups of adjacent pixels containing specific semantic information. Additionally, except for spectral features, the geometric and texture features of a single object, as well as the topological relationships between different image objects can be frequently utilized in image classification [30]. In this study, the classification process was implemented using the software Defines Developer 8.0 (formerly known as eCognition).…”

Section: Object-based Classification Methodsmentioning

confidence: 99%

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

et al. 2013

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Google Earth (GE) releases free images in high spatial resolution that may provide some potential for regional land use/cover mapping, especially for those regions with high heterogeneous landscapes. In order to test such practicability, the GE imagery was selected for a case study in Wuhan City to perform an object-based land use/cover classification. The classification accuracy was assessed by using 570 validation points generated by a random sampling scheme and compared with a parallel classification of QuickBird (QB) imagery based on an object-based classification method. The results showed that GE has an overall classification accuracy of 78.07%, which is slightly lower than that of QB. No significant difference was found between these two classification results by the adoption of Z-test, which strongly proved the potentials of GE in land use/cover mapping. Moreover, GE has different discriminating capacity for specific land use/cover types. It possesses some advantages for mapping those types with good spatial characteristics in terms of geometric, shape and context. The object-based method is recommended for imagery classification when using GE imagery for mapping land use/cover. However, GE has some limitations for those types classified by using only spectral characteristics largely due to its poor spectral characteristics. OPEN ACCESSRemote Sens. 2013, 5 6027

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Section: Object-based Classification Methodsmentioning

confidence: 99%

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

et al. 2013

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“…On the other hand, the image time series at finer spatial resolution, such as Landsat Thematic Mapper (TM) and Satellite Pour l'Observationde la Terre (SPOT), have shown the potential to discriminate crops [5,15,34]. Note that there have been some attempts at identifying crop types without ground reference data of the classification year using the spectrum at specific phenological periods [22,23].…”

Section: Introductionmentioning

confidence: 99%

Using Moderate-Resolution Temporal NDVI Profiles for High-Resolution Crop Mapping in Years of Absent Ground Reference Data: A Case Study of Bole and Manas Counties in Xinjiang, China

Hao

Wang

Zhan

et al. 2016

IJGI

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Most methods used for crop classification rely on the ground-reference data of the same year, which leads to considerable financial and labor cost. In this study, we presented a method that can avoid the requirements of a large number of ground-reference data in the classification year. Firstly, we extracted the Normalized Difference Vegetation Index (NDVI) time series profiles of the dominant crops from MODIS data using the historical ground-reference data in multiple years (2006, 2007, 2009 and 2010). Artificial Antibody Network (ABNet) was then employed to build reference NDVI time series for each crop based on the historical NDVI profiles. Afterwards, images of Landsat and HJ were combined to obtain 30 m image time series with 15-day acquisition frequency in 2011. Next, the reference NDVI time series were transformed to Landsat/HJ NDVI time series using their linear model. Finally, the transformed reference NDVI profiles were used to identify the crop types in 2011 at 30 m spatial resolution. The result showed that the dominant crops could be identified with overall accuracy of 87.13% and 83.48% in Bole and Manas, respectively. In addition, the reference NDVI profiles generated from multiple years could achieve better classification accuracy than that from single year (such as only 2007). This is mainly because the reference knowledge from multiple years contains more growing conditions of the same crop. Generally, this approach showed potential to identify crops without using large number of ground-reference data at 30 m resolution.

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“…And in a second step, a classification process is applied to these objects. Since OBIA offers the potential to exploit geographical information system (GIS) functionality, such as the incorporation of the spatial context or object shape in the classification, it provides a framework for overcoming the limitations of conventional pixel-based image classification methods and has been successfully applied to landslide mapping (Feizizadeh et al, 2014;Li et al, 2015b;Martha et al, 2010;Martha et al, 2011;Martha et al, 2012;Stumpf and Kerle, 2011), land cover and land use mapping (Baker et al, 2013;Benz et al, 2004;Blaschke, 2003;Blaschke et al, 2011;Blaschke et al, 2008;Contreras et al, 2015;D'Oleire-Oltmanns et al, 2014;De Pinho et al, 2012;Doleire-Oltmanns et al, 2013;Drăguţ and Blaschke, 2006;Drăguţ and Eisank, 2012;Duro et al, 2012;Eisank et al, 2011;Goodin et al, 2015;Hay et al, 2003;Hofmann et al, 2011;Kim et al, 2011;Leon and Woodroffe, 2011;Li et al, 2014;Li and Shao, 2013;Lisita et al, 2013;Macfaden et al, 2012;Mallinis et al, 2008;Mishra and Crews, 2014;Moskal et al, 2011;Myint et al, 2011;Phinn et al, 2012;Tzotsos et al, 2011;Walker and Blaschke, 2008;Walker and Briggs, 2007;We...…”

Section: Obia-based Object Detectionmentioning

confidence: 99%

A survey on object detection in optical remote sensing images

Cheng

Han

2016

ISPRS Journal of Photogrammetry and Remote Sensing

1,117

501

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Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey 1) template matching-based object detection methods, 2) knowledge-based object detection methods, 3) object-based image analysis (OBIA)-based object detection methods, 4) machine learning-based object detection methods, and 5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.

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Identifying potential areas of Cannabis sativa plantations using object-based image analysis of SPOT-5 satellite data

Cited by 19 publications

References 17 publications

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

Using Moderate-Resolution Temporal NDVI Profiles for High-Resolution Crop Mapping in Years of Absent Ground Reference Data: A Case Study of Bole and Manas Counties in Xinjiang, China

A survey on object detection in optical remote sensing images

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