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The Texture Feature Extraction of Agricultural Field Images by HOG Algorithms and Soil Moisture Estimation based on the Texture Features

Year 2016, Volume: 1 Issue: 1, 1 - 7, 01.12.2016

Abstract

Knowing
the value of soil surface moisture in the agricultural areas are very important
in many ways such as minimizing the harmful effects of drought cases, preventing
salinity caused by over watering, protecting agricultural lands and using the
irrigation system efficiently. The main purpose of this study is that determining
a relationship between measurements of local soil moisture and images in
agricultural Mardin region and prediction of soil moisture with the determined
relationship. The images are derived from TARBIL (http://www.tarbil.org) database.
The texture feature vectors are extracted from the images by using Histogram of
Oriented Gradients (HOG) algorithm. The obtained feature vectors are then classified
into three (much, middle and little) groups by using k-Nearest Neighbor (k-NN)
and Multilayer Perceptron (MLP) classifiers. 

References

  • [1] H. S. Srivastava, P. Patel, Y. Sharma, et R. R. Navalgund, “Large-area soil moisture estimation using multi-incidenceangle RADARSAT-1 SAR data “, Geosci. Remote Sens. IEEE Trans. On, vol. 47, no 8, p. 2528 2535, 2009.
  • [2] M. Zribi, A. Chahbi, M. Shabou, Z. Lili-Chabaane, B. Duchemin, N. Baghdadi, R. Amri, et A. Chehbouni, ”Soil surface moisture estimation over a semi-arid region using ENVISAT ASAR radar data for soil evaporation evaluation.”, Hydrol. Earth Syst. Sci., vol. 15, no 1, 2011.
  • [3] N. Baghdadi, S. Gaultier, et C. King, “Retrieving surface roughness and soil moisture from SAR data using neural networks.”, in Retrieval of Bio-and Geo-Physical Parameters from SAR Data for Land Applications, 2002, vol. 475, p. 315 319.
  • [4] El-Hajj, M.; Baghdadi, N.; Belaud, G.; Zribi, M.; Cheviron, B.; Courault, D.; Charron, F. "Soil moisture retrieval over grassland using X-band SAR data", Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International, On page(s): 3638 – 3641.
  • [5] Buttrey, S. and Karo, C.2001. Using k-nearest-neighbor classification in the leaves of a tree. Computational Statistics & Data Analysis, 40 (2002) 27-37.
  • [6] Li, X., Nie, P., Jun,Z. and He, Y. 2011.Using wavelet transform and multi-class least square support vector machine in multi-spectral imaging classification of Chinese famous tea. Expert Systems with Applications 38(9):11149-11159.
  • [7] N. Dalal and B. Triggs "Histograms of oriented gradients for human detection", Proc. IEEE Computer Soc. Conf. Comput. Vis. Pattern Recognit., pp.886 -893 2005.
  • [8] R. Kadota and H. Sugano "Hardware architecture for HOG feature extraction", Proc. 5th Int. Conf. Intell. Inf. Hiding Multimedia Signal, pp.1330 -1333 2009
  • [9] O. Ludwig, D. Delgado, V. Goncalves, and U. Nunes, 'Trainable Classifier-Fusion Schemes: An Application To Pedestrian Detection,' In: 12th International IEEE Conference On Intelligent Transportation Systems, 2009, St. Louis, 2009. V. 1. P. 432-437.
  • [10] Acar, E. and Özerdem, M.S., “Image Classification of Kiziltepe cropland by using Gabor Wavelet Transform”, 20th Signal Processing and Communications Applications Conference (SIU), On page(s): 1 – 4, Muğla, 2012.

Tarımsal İmge Dokularından HOG Algoritması ile Öznitelik Çıkarımı ve Öznitelik Tabanlı Toprak Neminin Tahmini

Year 2016, Volume: 1 Issue: 1, 1 - 7, 01.12.2016

Abstract

Tarımsal alanlardaki toprak
nem düzeyinin bilinmesi; kuraklık durumu zararlarının en aza indirgenmesi,
fazla sulama nedeni ile oluşan tuzluluğun önlenmesi, tarımsal alanlarının
korunması ve sulama sisteminin verimli olarak kullanılması gibi birçok yönden önem
taşımaktadır. Bu çalışmada, Mardin tarımsal alan imgelerine ait doku öznitelik
vektörleri  ile yerel toprak nem
ölçümleri arasındaki ilişkinin kurulması ve bu ilişkiye dayalı toprak nem
düzeyinin tahmini amaçlanmıştır. İmgeler TARBİL (http://www.tarbil.org)
veritabanından elde edilmiştir. İmge dokusuna duyarlı yeni yöntemlerden biri
olan Yönlü Gradyan Histogramı (HOG) algoritması kullanılarak, öznitelik
vektörleri elde edilmiştir. Elde edilen öznitelik vektörleri daha sonra k En
Yakın Komşu (k-NN) ve Çok Katmanlı Algılayıcı (MLP) sınıflandırıcılarının girişlerine
verilerek toprak nemi üç (Çok, Orta ve Az nemli) grubta sınıflandırılmıştır. 

References

  • [1] H. S. Srivastava, P. Patel, Y. Sharma, et R. R. Navalgund, “Large-area soil moisture estimation using multi-incidenceangle RADARSAT-1 SAR data “, Geosci. Remote Sens. IEEE Trans. On, vol. 47, no 8, p. 2528 2535, 2009.
  • [2] M. Zribi, A. Chahbi, M. Shabou, Z. Lili-Chabaane, B. Duchemin, N. Baghdadi, R. Amri, et A. Chehbouni, ”Soil surface moisture estimation over a semi-arid region using ENVISAT ASAR radar data for soil evaporation evaluation.”, Hydrol. Earth Syst. Sci., vol. 15, no 1, 2011.
  • [3] N. Baghdadi, S. Gaultier, et C. King, “Retrieving surface roughness and soil moisture from SAR data using neural networks.”, in Retrieval of Bio-and Geo-Physical Parameters from SAR Data for Land Applications, 2002, vol. 475, p. 315 319.
  • [4] El-Hajj, M.; Baghdadi, N.; Belaud, G.; Zribi, M.; Cheviron, B.; Courault, D.; Charron, F. "Soil moisture retrieval over grassland using X-band SAR data", Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International, On page(s): 3638 – 3641.
  • [5] Buttrey, S. and Karo, C.2001. Using k-nearest-neighbor classification in the leaves of a tree. Computational Statistics & Data Analysis, 40 (2002) 27-37.
  • [6] Li, X., Nie, P., Jun,Z. and He, Y. 2011.Using wavelet transform and multi-class least square support vector machine in multi-spectral imaging classification of Chinese famous tea. Expert Systems with Applications 38(9):11149-11159.
  • [7] N. Dalal and B. Triggs "Histograms of oriented gradients for human detection", Proc. IEEE Computer Soc. Conf. Comput. Vis. Pattern Recognit., pp.886 -893 2005.
  • [8] R. Kadota and H. Sugano "Hardware architecture for HOG feature extraction", Proc. 5th Int. Conf. Intell. Inf. Hiding Multimedia Signal, pp.1330 -1333 2009
  • [9] O. Ludwig, D. Delgado, V. Goncalves, and U. Nunes, 'Trainable Classifier-Fusion Schemes: An Application To Pedestrian Detection,' In: 12th International IEEE Conference On Intelligent Transportation Systems, 2009, St. Louis, 2009. V. 1. P. 432-437.
  • [10] Acar, E. and Özerdem, M.S., “Image Classification of Kiziltepe cropland by using Gabor Wavelet Transform”, 20th Signal Processing and Communications Applications Conference (SIU), On page(s): 1 – 4, Muğla, 2012.
There are 10 citations in total.

Details

Subjects Computer Software
Journal Section PAPERS
Authors

Emrullah Acar

Mehmet Siraç Özerdem

Publication Date December 1, 2016
Submission Date September 8, 2016
Acceptance Date October 20, 2016
Published in Issue Year 2016 Volume: 1 Issue: 1

Cite

APA Acar, E., & Özerdem, M. S. (2016). The Texture Feature Extraction of Agricultural Field Images by HOG Algorithms and Soil Moisture Estimation based on the Texture Features. Computer Science, 1(1), 1-7.

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