An object-oriented approach for agrivultural land classiﬁcation using rapideye imagery

With the improvement of remote sensing technology, the spatial, structural and texture information of land covers are present clearly in high resolution imagery, which enhances the ability of crop mapping. Since the satellite RapidEye was launched in 2009, high resolution multispectral imagery together with wide red edge band has been utilized in vegetation monitoring. Broad red edge band related vegetation indices improved land use classification and vegetation studies. RapidEye high resolution imagery acquired on May 29 and August 9th of 2012 was used in this study to evaluate the potential of red edge band in agricultural land cover/use mapping using an objected-oriented classification approach. A new object-oriented decision tree classifier was introduced in this study to map agricultural lands in the study area. Besides the five bands of RapidEye image, the vegetation indexes derived from spectral bands and the structural and texture features are utilized as inputs for agricultural land cover/use mapping in the study. The optimization of input features for classification by reducing redundant information improves the mapping precision over 9% for AdaTree. WL, and 5% for SVM, the accuracy is over 90% for both approaches. Time phase characteristic is much important in different agricultural lands, and it improves the classification accuracy 7% for AdaTree.WL and 6% for SVM.

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Object-oriented land cover information extraction in emigration area of Zigui county using high resolution imagery

2009 Joint Urban Remote Sensing Event ◽

10.1109/urs.2009.5137702 ◽

2009 ◽

Author(s):

Liang Zhu ◽

Bingfang Wu ◽

Yuemin Zhou ◽

Lei Zhang ◽

Ning Zhang

Keyword(s):

High Resolution ◽

Land Cover ◽

Information Extraction ◽

Object Oriented ◽

High Resolution Imagery

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Assessing Riyadh’s Urban Change Utilizing High-Resolution Imagery

Land ◽

10.3390/land8120193 ◽

2019 ◽

Vol 8 (12) ◽

pp. 193

Author(s):

Ali Alghamdi ◽

Anthony R. Cummings

Keyword(s):

Maximum Likelihood ◽

High Resolution ◽

Accuracy Assessment ◽

Middle Eastern ◽

Object Oriented ◽

Training Data ◽

High Resolution Imagery ◽

Significant Research ◽

Desert Areas ◽

Spot 5

The implications of change on local processes have attracted significant research interest in recent times. In urban settings, green spaces and forests have attracted much attention. Here, we present an assessment of change within the predominantly desert Middle Eastern city of Riyadh, an understudied setting. We utilized high-resolution SPOT 5 data and two classification techniques—maximum likelihood classification and object-oriented classification—to study the changes in Riyadh between 2004 and 2014. Imagery classification was completed with training data obtained from the SPOT 5 dataset, and an accuracy assessment was completed through a combination of field surveys and an application developed in ESRI Survey 123 tool. The Survey 123 tool allowed residents of Riyadh to present their views on land cover for the 2004 and 2014 imagery. Our analysis showed that soil or ‘desert’ areas were converted to roads and buildings to accommodate for Riyadh’s rapidly growing population. The object-oriented classifier provided higher overall accuracy than the maximum likelihood classifier (74.71% and 73.79% vs. 92.36% and 90.77% for 2004 and 2014). Our work provides insights into the changes within a desert environment and establishes a foundation for understanding change in this understudied setting.

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Training Deep Convolutional Neural Networks for Land–Cover Classification of High-Resolution Imagery

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2017.2657778 ◽

2017 ◽

Vol 14 (4) ◽

pp. 549-553 ◽

Cited By ~ 117

Author(s):

Grant J. Scott ◽

Matthew R. England ◽

William A. Starms ◽

Richard A. Marcum ◽

Curt H. Davis

Keyword(s):

Neural Networks ◽

High Resolution ◽

Land Cover ◽

Convolutional Neural Networks ◽

Land Cover Classification ◽

Deep Convolutional Neural Networks ◽

High Resolution Imagery

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Comparison of Land Cover Characterization Using EOS MISR and MODIS Data and a Decision Tree Classifier

Geocarto International ◽

10.1080/10106040608542389 ◽

2006 ◽

Vol 21 (3) ◽

pp. 19-26 ◽

Cited By ~ 2

Author(s):

Limin Yang

Keyword(s):

Land Cover ◽

Decision Tree ◽

Decision Tree Classifier ◽

Modis Data ◽

Tree Classifier

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Erratum to ‘Hierarchical object oriented classification using very high resolution imagery and LIDAR data over urban areas’

Advances in Space Research ◽

10.1016/j.asr.2012.01.003 ◽

2012 ◽

Vol 49 (6) ◽

pp. 1112 ◽

Cited By ~ 2

Author(s):

Yunhao Chen ◽

Wei Su ◽

Jing Li ◽

Zhongping Sun

Keyword(s):

High Resolution ◽

Urban Areas ◽

Object Oriented ◽

Lidar Data ◽

High Resolution Imagery ◽

Very High Resolution Imagery ◽

Very High

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Improved Mapping of Mountain Shrublands Using the Sentinel-2 Red-Edge Band

Remote Sensing ◽

10.3390/rs11232807 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2807 ◽

Cited By ~ 2

Author(s):

Arthur Bayle ◽

Bradley Carlson ◽

Vincent Thierion ◽

Marc Isenmann ◽

Philippe Choler

Keyword(s):

Land Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Model Performance ◽

Mountain Range ◽

Rhododendron Ferrugineum ◽

Red Edge ◽

Rocky Habitats ◽

Edge Band ◽

Sentinel 2

Shrub encroachment into grassland and rocky habitats is a noticeable land cover change currently underway in temperate mountains and is a matter of concern for the sustainable management of mountain biodiversity. Current land cover products tend to underestimate the extent of mountain shrublands dominated by Ericaceae (Vaccinium spp. (species) and Rhododendron ferrugineum). In addition, mountain shrubs are often confounded with grasslands. Here, we examined the potential of anthocyanin-responsive vegetation indices to provide more accurate maps of mountain shrublands in a mountain range located in the French Alps. We relied on the multi-spectral instrument onboard the Sentinel-2A and 2B satellites and the availability of red-edge bands to calculate a Normalized Anthocyanin Reflectance Index (NARI). We used this index to quantify the autumn accumulation of anthocyanin in canopies dominated by Vaccinium spp. and Rhododendron ferrugineum and compared the effectiveness of NARI to Normalized Difference Vegetation Index (NDVI) as a basis for shrubland mapping. Photointerpretation of high-resolution aerial imagery, intensive field campaigns, and floristic surveys provided complementary data to calibrate and evaluate model performance. The proposed NARI-based model performed better than the NDVI-based model with an area under the curve (AUC) of 0.92 against 0.58. Validation of shrub cover maps based on NARI resulted in a Kappa coefficient of 0.67, which outperformed existing land cover products and resulted in a ten-fold increase in estimated area occupied by Ericaceae-dominated shrublands. We conclude that the Sentinel-2 red-edge band provides novel opportunities to detect seasonal anthocyanin accumulation in plant canopies and discuss the potential of our method to quantify long-term dynamics of shrublands in alpine and arctic contexts.

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