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

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.

[1]  Eun-Soo Kim,et al.  Facial expression recognition using active contour-based face detection, facial movement-based feature extraction, and non-linear feature selection , 2014, Multimedia Systems.

[2]  A. Trémeau,et al.  International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives , 2013 .

[3]  Vincent G. Ambrosia,et al.  Unmanned Aircraft Systems in Remote Sensing and Scientific Research: Classification and Considerations of Use , 2012, Remote. Sens..

[4]  J. Berni,et al.  ' s personal copy Imaging chlorophyll fl uorescence with an airborne narrow-band multispectral camera for vegetation stress detection , 2009 .

[5]  C. Delrieux,et al.  Automatic methodology for mapping of coastal zones in video sequences , 2016 .

[6]  Liang Cheng,et al.  Cultivated land information extraction from high-resolution unmanned aerial vehicle imagery data , 2014 .

[7]  D. George,et al.  Hierarchical Temporal Memory Concepts , Theory , and Terminology , 2006 .

[8]  C. Strecha,et al.  PHOTOGRAMMETRIC PERFORMANCE OF AN ULTRA LIGHT WEIGHT SWINGLET UAV , 2012 .

[9]  Qi Tian,et al.  Feature representation for statistical-learning-based object detection: A review , 2015, Pattern Recognit..

[10]  D. A. Devitt,et al.  Evaluating temporal variability in the spectral reflectance response of annual ryegrass to changes in nitrogen applications and leaching fractions , 2006 .

[11]  Alberto Jesús Perea Moreno,et al.  Comparison between New Digital Image Classification Methods and Traditional Methods for Land-Cover Mapping , 2012 .

[12]  William E. Higgins,et al.  Efficient Gabor filter design for texture segmentation , 1996, Pattern Recognit..

[13]  Li Li,et al.  Road boundary estimation to improve vehicle detection and tracking in UAV video , 2014 .

[14]  J. Six,et al.  Object-based crop identification using multiple vegetation indices, textural features and crop phenology , 2011 .

[15]  Alberto Jesús Perea Moreno,et al.  COMPARACIÓN ENTRE EL MÉTODO BASADO EN PÍXELES Y EL BASADO EN OBJETOS PARA EL ANÁLISIS DE FACHADAS DE EDIFICIOS HISTÓRICOS , 2016 .

[16]  H. S. Lim,et al.  Cropcam UAV images for land use/land cover over Penang Island, Malaysia using neural network approach , 2010, Asia-Pacific Remote Sensing.

[17]  Jorge Torres-Sánchez,et al.  Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping , 2015, Sensors.

[18]  Giovanni Maria Farinella,et al.  An integrated system for vehicle tracking and classification , 2015, Expert Syst. Appl..

[19]  Francisco B. Rodríguez,et al.  Extending the bioinspired hierarchical temporal memory paradigm for sign language recognition , 2012, Neurocomputing.

[20]  E. Chaum,et al.  Image processing and hierarchical temporal memories for automated retina analysis , 2010, 2010 Biomedical Sciences and Engineering Conference.

[21]  Pablo J. Zarco-Tejada,et al.  Detecting water stress effects on fruit quality in orchards with time-series PRI airborne imagery , 2010 .

[22]  Bisheng Yang,et al.  Automatic registration of UAV-borne sequent images and LiDAR data , 2015 .

[23]  L. Caturegli,et al.  Unmanned Aerial Vehicle to Estimate Nitrogen Status of Turfgrasses , 2016, PloS one.

[24]  Steven E. Franklin,et al.  A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery , 2012 .

[25]  Jianhua Gong,et al.  Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China , 2015 .

[26]  Jianhua Gong,et al.  UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis , 2015, Remote. Sens..

[27]  A. Gerlak.  Water , 2013, Ecological Restoration.

[28]  D. Devitt,et al.  Golf Course Superintendents' Attitudes and Perceptions Toward Using Reuse Water for Irrigation in the Southwestern United States , 2004 .

[29]  Marco Dubbini,et al.  Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images , 2015, Remote. Sens..

[30]  R A Diaz-Varela,et al.  Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle. , 2014, Journal of environmental management.

[31]  Carrick Detweiler,et al.  Obtaining the Thermal Structure of Lakes from the Air , 2015 .

[32]  John K. Tsotsos,et al.  50 Years of object recognition: Directions forward , 2013, Comput. Vis. Image Underst..

[33]  José E. Meroño,et al.  Application of Numenta® Hierarchical Temporal Memory for land-use classification , 2010 .

[34]  L. López-Bellido,et al.  Nitrogen remote diagnosis in a creeping bentgrass golf green , 2012 .

[35]  J. Gong,et al.  Sloping farmland identification using hierarchical classification in the Xi-He region of China , 2013 .

[36]  Konstantinos Karantzalos,et al.  Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data , 2016, Remote. Sens..

[37]  J. E. Meroño,et al.  Automatic detection of urban areas using the Hierarchical Temporal Memory of Numenta , 2012 .

[38]  Pablo J. Zarco-Tejada,et al.  Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[39]  Philip H. S. Torr,et al.  Efficient online structured output learning for keypoint-based object tracking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[40]  Robert N. Carrow,et al.  Precision turfgrass management: challenges and field applications for mapping turfgrass soil and stress , 2010, Precision Agriculture.

[41]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[42]  Jorge Torres-Sánchez,et al.  High-Throughput 3-D Monitoring of Agricultural-Tree Plantations with Unmanned Aerial Vehicle (UAV) Technology , 2015, PloS one.

[43]  Krzysztof Kusnierek,et al.  Challenges in using an analog uncooled microbolometer thermal camera to measure crop temperature , 2014 .