Using UAS Hyperspatial RGB Imagery for Identifying Beach Zones along the South Texas Coast

Su, Lihong; Gibeaut, James C.

doi:10.3390/rs9020159

Cited by 26 publications

(17 citation statements)

References 30 publications

(35 reference statements)

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“…There is a growing trend in studies of coastal and fluvial systems for using automated methods to extract information from time-series of imagery from fixed camera installations [10][11][12][13][14][15][16], UAVs [17][18][19], and other aerial platforms [20]. Fixed camera installations are designed for generating time-series of images for the assessment of geomorphic changes in dynamic environments.…”

Section: The Growing Use Of Image Classification In the Geosciencesmentioning

confidence: 99%

Landscape Classification with Deep Neural Networks

Buscombe

Ritchie

2018

Geosciences

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Abstract:The application of deep learning, specifically deep convolutional neural networks (DCNNs), to the classification of remotely-sensed imagery of natural landscapes has the potential to greatly assist in the analysis and interpretation of geomorphic processes. However, the general usefulness of deep learning applied to conventional photographic imagery at a landscape scale is, at yet, largely unproven. If DCNN-based image classification is to gain wider application and acceptance within the geoscience community, demonstrable successes need to be coupled with accessible tools to retrain deep neural networks to discriminate landforms and land uses in landscape imagery. Here, we present an efficient approach to train/apply DCNNs with/on sets of photographic images, using a powerful graphical method called a conditional random field (CRF), to generate DCNN training and testing data using minimal manual supervision. We apply the method to several sets of images of natural landscapes, acquired from satellites, aircraft, unmanned aerial vehicles, and fixed camera installations. We synthesize our findings to examine the general effectiveness of transfer learning to landscape-scale image classification. Finally, we show how DCNN predictions on small regions of images might be used in conjunction with a CRF for highly accurate pixel-level classification of images.

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Section: The Growing Use Of Image Classification In the Geosciencesmentioning

confidence: 99%

Landscape Classification with Deep Neural Networks

Buscombe

Ritchie

2018

Geosciences

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“…Together with spectral features, some textural features, based on co-occurrence filters, were computed to highlight spatial patterns of the multi-spectral ortho-mosaic. Textural features are among the most used features in remote sensing classification (Haralick, Shanmugam, and Dinstein 1973;Su and Gibeaut 2017), allowing for the representation of spatial patterns of an image. Some recent works reported a significant improvement of classification accuracy of very high-resolution (VHR) data when these features are combined with spectral features (Qin 2015).…”

Section: Spectral Indices and Textural Features Calculationmentioning

confidence: 99%

Early season weed mapping in rice crops using multi-spectral UAV data

Stroppiana

Villa

Sona

et al. 2018

International Journal of Remote Sensing

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In this article, we propose an automatic procedure for classification of UAV imagery to map weed presence in rice paddies at early stages of the growing cycle. The objective was to produce a weed map (common weeds and cover crop remnants) to support variable rate technologies for site-specific weed management. A multi-spectral orthomosaic, derived from images acquired by a Parrot Sequoia sensor mounted on a quadcopter, was classified through an unsupervised clustering algorithm; cluster labelling into 'weed'/'no weed' classes was achieved using geo-referenced observations. We tested the best set of input features among spectral bands, spectral indices and textural metrics. Weed mapping performance was assessed by calculating overall accuracy (OA) and, for the weed class, omission (OE) and commission errors (CE). Classification results were assessed under an 'alarmist' approach in order to minimise the chance of overestimating weed coverage. Under this condition, we found that best results are provided by a set of spectral indices (OA = 96.5%, weed CE = 2.0%). The output weed map was aggregated to a grid layer of 5 × 5 m to simulate variable rate management units; a weed threshold was applied to identify the portion of the field to be subject to treatment with herbicides. Ancillary information on weed and crop conditions were derived over the grid cells to support precision agronomic management of rice crops at the early stage of growth.

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“…The RGB data was also transformed to the HSV colorspace: past research has suggested HSV may be better than RGB for image classification because there is less covariance in the HSV colorspace [41][42][43][44][45]. This gives 10 available data channels and the performance of 9 sets of these were considered.…”

Section: Classification Methodologymentioning

confidence: 99%

The Use of Unmanned Aerial Systems to Map Intertidal Sediment

et al. 2018

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This paper describes a new methodology to map intertidal sediment using a commercially available unmanned aerial system (UAS). A fixed-wing UAS was flown with both thermal and multispectral cameras over three study sites comprising of sandy and muddy areas. Thermal signatures of sediment type were not observable in the recorded data and therefore only the multispectral results were used in the sediment classification. The multispectral camera consisted of a Red-Green-Blue (RGB) camera and four multispectral sensors covering the green, red, red edge and near-infrared bands. Statistically significant correlations (>99%) were noted between the multispectral reflectance and both moisture content and median grain size. The best correlation against median grain size was found with the near-infrared band. Three classification methodologies were tested to split the intertidal area into sand and mud: k-means clustering, artificial neural networks, and the random forest approach. Classification methodologies were tested with nine input subsets of the available data channels, including transforming the RGB colorspace to the Hue-Saturation-Value (HSV) colorspace. The classification approach that gave the best performance, based on the j-index, was when an artificial neural network was utilized with near-infrared reflectance and HSV color as input data. Classification performance ranged from good to excellent, with values of Youden's j-index ranging from 0.6 to 0.97 depending on flight date and site.

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Using UAS Hyperspatial RGB Imagery for Identifying Beach Zones along the South Texas Coast

Cited by 26 publications

References 30 publications

Landscape Classification with Deep Neural Networks

Landscape Classification with Deep Neural Networks

Early season weed mapping in rice crops using multi-spectral UAV data

The Use of Unmanned Aerial Systems to Map Intertidal Sediment

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