Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

Karakizi, Christina; Oikonomou, M.; Καράντζαλος, Κωνσταντίνος

doi:10.3390/rs8030235

Cited by 33 publications

(29 citation statements)

References 54 publications

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“…Once the network has been trained and tested, the algorithm classifies the videos, which are received in real-time from the Wi-Fi computer camera system of the DJI Phantom 2 Vision+ (Figure 13). In contrast, in the works [1,2,51] of classical classification, post-processing work was required.…”

Section: Resultsmentioning

confidence: 99%

“…We compared our results to those of other works. For example, Revollo et al [49] develop an autonomous application for geographic feature extraction and recognition in coastal videos and obtained an overall accuracy of 95%; Duro et al [50] used object-oriented classification and decision trees in Spot images to identify vegetal coverings and obtained an overall accuracy of 95%; Karakizi et al [51] developed and evaluated an object-based classification framework towards the detection of vineyards reaching an overall accuracy rate of 96%.…”

Section: Resultsmentioning

confidence: 99%

“…Unmanned aerial vehicles (UAVs) are currently a valuable source of aerial photographs In contrast, in the works [1,2,51] of classical classification, post-processing work was required.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Assessment of the Potential of UAV Video Image Analysis for Planning Irrigation Needs of Golf Courses

Perea-Moreno

Ureña

Larriva

et al. 2016

Water

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Golf courses can be considered as precision agriculture, as being a playing surface, their appearance is of vital importance. Areas with good weather tend to have low rainfall. Therefore, the water management of golf courses in these climates is a crucial issue due to the high water demand of turfgrass. Golf courses are rapidly transitioning to reuse water, e.g., the municipalities in the USA are providing price incentives or mandate the use of reuse water for irrigation purposes; in Europe this is mandatory. So, knowing the turfgrass surfaces of a large area can help plan the treated sewage effluent needs. Recycled water is usually of poor quality, thus it is crucial to check the real turfgrass surface in order to be able to plan the global irrigation needs using this type of water. In this way, the irrigation of golf courses does not detract from the natural water resources of the area. The aim of this paper is to propose a new methodology for analysing geometric patterns of video data acquired from UAVs (Unmanned Aerial Vehicle) using a new Hierarchical Temporal Memory (HTM) algorithm. A case study concerning maintained turfgrass, especially for golf courses, has been developed. It shows very good results, better than 98% in the confusion matrix. The results obtained in this study represent a first step toward video imagery classification. In summary, technical progress in computing power and software has shown that video imagery is one of the most promising environmental data acquisition techniques available today. This rapid classification of turfgrass can play an important role for planning water management.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of the Potential of UAV Video Image Analysis for Planning Irrigation Needs of Golf Courses

Perea-Moreno

Ureña

Larriva

et al. 2016

Water

View full text Add to dashboard Cite

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“…Automatic crop identification methodologies based on the processing of remotely sensed images reduce the cost and accelerate the updating process, making this technique a viable option for detecting changes in AGDB [3]. Different authors have assessed the utility of remotely sensed image processing for automatic identification and management of crops [4][5][6][7] as well as for updating GDBs [8,9].…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Uprooted Orchards Based on Orthophoto Texture Analysis

et al. 2017

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Permanent crops, such as olive groves, vineyards and fruit trees, are important in European agriculture because of their spatial and economic relevance. Agricultural geographical databases (AGDBs) are commonly used by public bodies to gain knowledge of the extension covered by these crops and to manage related agricultural subsidies and inspections. However, the updating of these databases is mostly based on photointerpretation, and thus keeping this information up-to-date is very costly in terms of time and money. This paper describes a methodology for automatic detection of uprooted orchards (parcels where fruit trees have been eliminated) based on the textural classification of orthophotos with a spatial resolution of 0.25 m. The textural features used for this classification were derived from the grey level co-occurrence matrix (GLCM) and wavelet transform, and were selected through principal components (PCA) and separability analyses. Next, a Discriminant Analysis classification algorithm was used to detect uprooted orchards. Entropy, contrast and correlation were found to be the most informative textural features obtained from the co-occurrence matrix. The minimum and standard deviation in plane 3 were the selected features based on wavelet transform. The classification based on these features achieved a true positive rate (TPR) of over 80% and an accuracy (A) of over 88%. As a result, this methodology enabled reducing the number of fields to photointerpret by 60-85%, depending on the membership threshold value selected. The proposed approach could be easily adopted by different stakeholders and could increase significantly the efficiency of agricultural database updating tasks.

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“…To increase the chance of successful spectral discrimination of varieties, the sources of canopy reflectance variation must be eliminated as much as possible. This can be achieved by classifying the pixels inside a crop type mask in a given area [177] (Figure 5), by classifying only vegetation objects even with a small size of foliage [187], or by selecting fields with homogeneous cropping practices such as the crop age [176]. However, sources of spectral variation due to factors other than crop variety can persist, such as the soil, terrain slope, and row orientation that play relevant roles in the reflectance responses [180], particularly through the fraction of shadowed/sunlit soil for perennial crops.…”

Section: Crop Varietiesmentioning

confidence: 99%

Remote Sensing and Cropping Practices: A Review

et al. 2018

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For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.

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Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

Cited by 33 publications

References 54 publications

Assessment of the Potential of UAV Video Image Analysis for Planning Irrigation Needs of Golf Courses

Assessment of the Potential of UAV Video Image Analysis for Planning Irrigation Needs of Golf Courses

Automatic Detection of Uprooted Orchards Based on Orthophoto Texture Analysis

Remote Sensing and Cropping Practices: A Review

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