Dears,
I am looking for research papers leveraging Deep Neural Network to delineate field and parcel boundaries based on satellite or UAV images.
I am especially interested in Sub-Saharan Africa context and very high resolution images (
Dear Christophe Bocquet ,
below you'll find a list of UAV-based crop detection papers that's part of a literature review we've performed a few months ago. There are also several papers that refer to traditional methods and not CNNs, but I think it is interesting to compare both approaches. Several papers are in Portuguese because there is a lot of research in this area going on in Brazil and the agricultural community there tends to publish in their own language.
There's also this paper: Deep Learning with unsupervised data labeling for weeds detection on UAV images - Preprint Deep Learning with Unsupervised Data Labeling for Weeds Dete...
List:
Andrade, L. N., Vieira, T. G., Lacerda, W. S., Davis Junior, C., Volpato, M., and Alves, H. M. Identificac¸a˜o automa´tica de a´reas cafeeiras em imagens de sate´lite utilizando redes neurais artificiais. In Embrapa Cafe´-Artigo em anais de congresso (ALICE). In.: Congresso De Po´ s-Graduac¸a˜o Da UFLA, 19, 2010, Lavras.
Chen, L.-r. and Ji, R.-h. (2010). Detection center of the crop row by the gradient-based random hough transform [j]. Hubei Agricultural Sciences, 9:071.
Comba, L., Gay, P., Primicerio, J., and Aimonino, D. R. (2015). Vineyard detection from unmanned aerial systems images. computers and Electronics in Agriculture, 114:78–87.
de Souza, C. H. W., Lamparelli, R. A. C., Rocha, J. V., and Magalha˜es, P. S. G. (2017). Mapping skips in sugarcane fields using object-based analysis of unmanned aerial vehi- cle (uav) images. Computers and Electronics in Agriculture, 143:49–56.
Delgado, R. C., Sediyama, G. C., Costa, M. H., Soares, V. P., and Andrade, R. G. (2012).Classificac¸a˜o espectral de a´rea plantada com a cultura da cana-de-ac¸u´ car por meio da a´rvore de decisa˜o. Embrapa Monitoramento por Sate´lite-Artigo em perio´ dico indexado (ALICE).
Ferreira, A. d. S. (2017). Redes neurais convolucionais profundas na detecc¸a˜o de plantas daninhas em lavoura de soja. Master’s thesis.
Guijarro, M., Pajares, G., Riomoros, I., Herrera, P., Burgos-Artizzu, X., and Ribeiro, A. (2011). Automatic segmentation of relevant textures in agricultural images. Computers and Electronics in Agriculture, 75(1):75–83.
Hamuda, E., Mc Ginley, B., Glavin, M., and Jones, E. (2017). Automatic crop detec- tion under field conditions using the hsv colour space and morphological operations. Computers and Electronics in Agriculture, 133:97–107.
Honkavaara, E., Saari, H., Kaivosoja, J., Po¨ lo¨ nen, I., Hakala, T., Litkey, P., Ma¨kynen, J., and Pesonen, L. (2013). Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight uav spectral camera for precision agriculture. Remote Sensing, 5(10):5006–5039.
Lambert, J., Hicks, H., Childs, D., and Freckleton, R. (2018). Evaluating the potential of unmanned aerial systems for mapping weeds at field scales: a case study with alope- curus myosuroides. Weed research, 58(1):35–45.
Louargant, M., Jones, G., Faroux, R., Paoli, J.-N., Maillot, T., Ge´e, C., and Villette, S. (2018). Unsupervised classification algorithm for early weed detection in row-crops by combining spatial and spectral information. Remote Sensing, 10(5):761.
Oliveira, H. C., Guizilini, V. C., Nunes, I. P., and Souza, J. R. (2018). Failure detection in row crops from uav images using morphological operators. IEEE Geoscience and Remote Sensing Letters.
Pe´rez-Ortiz, M., Pen˜ a, J., Gutie´rrez, P. A., Torres-Sa´nchez, J., Herva´s-Mart´ınez, C., and Lo´ pez-Granados, F. (2015). A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method. Applied Soft Computing, 37:533–544.
Sa, I., Chen, Z., Popovic´, M., Khanna, R., Liebisch, F., Nieto, J., and Siegwart, R. (2018). weednet: Dense semantic weed classification using multispectral images and mav for smart farming. IEEE Robotics and Automation Letters, 3(1):588–595.
Sankaran, S., Khot, L. R., Espinoza, C. Z., Jarolmasjed, S., Sathuvalli, V. R., Vandemark, G. J., Miklas, P. N., Carter, A. H., Pumphrey, M. O., Knowles, N. R., et al. (2015). Low- altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. European Journal of Agronomy, 70:112–123.
Tellaeche, A., Burgos-Artizzu, X. P., Pajares, G., and Ribeiro, A. (2008). A vision- based method for weeds identification through the bayesian decision theory. Pattern Recognition, 41(2):521–530.
Vidovic´, I., Cupec, R., and Hocenski, Zˇ . (2016). Crop row detection by global energy minimization. Pattern Recognition, 55:68–86.
One of the recent successful solution for segmenting satellite images is U-Net architecture even though it is first proposed for biomedical application (Article U-Net: Convolutional Networks for Biomedical Image Segmentation
).A good read on how to use U-net for satellite image can be found at https://towardsdatascience.com/dstl-satellite-imagery-contest-on-kaggle-2f3ef7b8ac40
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