{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T05:43:11Z","timestamp":1769060591098,"version":"3.49.0"},"reference-count":50,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,17]],"date-time":"2019-07-17T00:00:00Z","timestamp":1563321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Automatic weed detection and classification faces the challenges of large intraclass variation and high spectral similarity to other vegetation. With the availability of new high-resolution remote sensing data from various platforms and sensors, it is possible to capture both spectral and spatial characteristics of weed species at multiple scales. Effective multi-resolution feature learning is then desirable to extract distinctive intensity, texture and shape features of each category of weed to enhance the weed separability. We propose a feature extraction method using a Convolutional Neural Network (CNN) and superpixel based Local Binary Pattern (LBP). Both middle and high level spatial features are learned using the CNN. Local texture features from superpixel-based LBP are extracted, and are also used as input to Support Vector Machines (SVM) for weed classification. Experimental results on the hyperspectral and remote sensing datasets verify the effectiveness of the proposed method, and show that it outperforms several feature extraction approaches.<\/jats:p>","DOI":"10.3390\/rs11141692","type":"journal-article","created":{"date-parts":[[2019,7,17]],"date-time":"2019-07-17T11:25:12Z","timestamp":1563362712000},"page":"1692","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Multi-Resolution Weed Classification via Convolutional Neural Network and Superpixel Based Local Binary Pattern Using Remote Sensing Images"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8616-9055","authenticated-orcid":false,"given":"Adnan","family":"Farooq","sequence":"first","affiliation":[{"name":"School of Engineering and Information Technology, University of New South Wales, Canberra 2600, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9916-6382","authenticated-orcid":false,"given":"Xiuping","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Engineering and Information Technology, University of New South Wales, Canberra 2600, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0230-1432","authenticated-orcid":false,"given":"Jiankun","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Engineering and Information Technology, University of New South Wales, Canberra 2600, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5822-8233","authenticated-orcid":false,"given":"Jun","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information and Communication Technology, University of Griffith, Nathan, Queensland 4111, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,17]]},"reference":[{"key":"ref_1","unstructured":"Invasive Plants and Animals Committee (2016). Australian Weeds Strategy 2017 to 2027."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1109\/LRA.2017.2774979","article-title":"WeedNet: Dense semantic weed classification using multispectral images and MAV for smart farming","volume":"3","author":"Sa","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Pearlstein, L., Kim, M., and Seto, W. (2016, January 18\u201320). Convolutional neural network application to plant detection, based on synthetic imagery. Proceedings of the 2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington, DC, USA.","DOI":"10.1109\/AIPR.2016.8010596"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"12037","DOI":"10.3390\/rs61212037","article-title":"Feature learning based approach for weed classification using high-resolution aerial images from a digital camera mounted on a UAV","volume":"6","author":"Hung","year":"2014","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.isprsjprs.2017.11.021","article-title":"A new deep convolutional neural network for fast hyperspectral image classification","volume":"145","author":"Paoletti","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.compag.2018.09.021","article-title":"Agroavnet for crops and weeds classification: A step forward in automatic farming","volume":"154","author":"Chavan","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.13031\/2013.2971","article-title":"Backpropagation neural network design and evaluation for classifying weed species using color image texture","volume":"43","author":"Burks","year":"2000","journal-title":"Trans. ASAE"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.13031\/2013.2723","article-title":"Classification of weed species using color texture features and discriminant analysis","volume":"43","author":"Burks","year":"2000","journal-title":"Trans. ASAE"},{"key":"ref_9","first-page":"441","article-title":"Factors affecting color-based weed detection","volume":"43","author":"Zhang","year":"2000","journal-title":"Trans. ASAE"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.compag.2013.04.010","article-title":"Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model","volume":"96","author":"Guo","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.eswa.2015.10.043","article-title":"Selecting patterns and features for between-and within-crop-row weed mapping using uav imagery","volume":"47","author":"Pena","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1109\/LGRS.2018.2869879","article-title":"Analysis of spectral bands and spatial resolutions for weed classification via deep convolutional neural network","volume":"16","author":"Farooq","year":"2018","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","article-title":"Multiresolution gray-scale and rotation invariant texture classification with local binary patterns","volume":"24","author":"Ojala","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Brahnam, S., Jain, L.C., Nanni, L., and Lumini, A. (2014). Local Binary Patterns: New Variants and Applications, Springer.","DOI":"10.1007\/978-3-642-39289-4"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1109\/TPAMI.2007.1110","article-title":"Dynamic texture recognition using local binary patterns with an application to facial expressions","volume":"29","author":"Zhao","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1109\/TIP.2015.2507408","article-title":"Robust texture image representation by scale selective local binary patterns","volume":"25","author":"Guo","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1109\/TIP.2009.2015682","article-title":"Dominant local binary patterns for texture classification","volume":"18","author":"Liao","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1016\/j.patcog.2009.08.017","article-title":"Rotation invariant texture classification using lbp variance (lbpv) with global matching","volume":"43","author":"Guo","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/S0031-3203(99)00032-1","article-title":"Rotation-invariant texture classification using feature distributions","volume":"33","author":"Pietikainen","year":"2000","journal-title":"Pattern Recognit."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.1109\/LGRS.2013.2267531","article-title":"Assessment of binary coding techniques for texture characterization in remote sensing imagery","volume":"10","author":"Musci","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1109\/TGRS.2014.2381602","article-title":"Local binary patterns and extreme learning machine for hyperspectral imagery classification","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, M.-Y., Tuzel, O., Ramalingam, S., and Chellappa, R. (2011, January 20\u201325). Entropy rate superpixel segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Washington, DC, USA.","DOI":"10.1109\/CVPR.2011.5995323"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/34.868688","article-title":"Normalized cuts and image segmentation","volume":"22","author":"Shi","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"Slic superpixels compared to state-of-the-art superpixel methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6663","DOI":"10.1109\/TGRS.2015.2445767","article-title":"Classification of hyperspectral images by exploiting spectralspatial information of superpixel via multiple kernels","volume":"53","author":"Fang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5338","DOI":"10.1109\/TGRS.2015.2421638","article-title":"Efficient superpixel-level multitask joint sparse representation for hyperspectral image classification","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/TGRS.2017.2754511","article-title":"Local binary pattern-based hyperspectral image classification with superpixel guidance","volume":"56","author":"Jia","year":"2018","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, Z., and Wang, B. (2014, January 7\u20139). An improved spectral-spatial classification framework for hyperspectral remote sensing images. Proceedings of the International Conference on Audio, Language and Image Processing (ICALIP), Shanghai, China.","DOI":"10.1109\/ICALIP.2014.7009850"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, Z., Shen, Y., Zhang, M., Wang, Q., Wang, Y., and Yu, R. (2014, January 12\u201315). Spectralspatial hyperspectral image classification via svm and superpixel segmentation. Proceedings of the International Proceedings on Instrumentation and Measurement Technology Conference (I2MTC), Montevideo, Uruguay.","DOI":"10.1109\/I2MTC.2014.6860780"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1002\/rob.21675","article-title":"Effective vision based classification for separating sugar beets and weeds for precision farming","volume":"34","author":"Lottes","year":"2017","journal-title":"J. Field Robot."},{"key":"ref_31","unstructured":"Lottes, P., Khanna, R., Pfeifer, J., Siegwart, R., and Stachniss, C. (June, January 29). UAV based crop and weed classification for smart farming. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Singapore."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.1109\/LRA.2017.2667039","article-title":"Mixtures of lightweight deep convolutional neural networks: applied to agricultural robotics","volume":"2","author":"McCool","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Milioto, A., Lottes, P., and Stachniss, C. (2018, January 21\u201326). Real-time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in CNNs. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8460962"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.eswa.2019.04.006","article-title":"Hyperspectral imagery classification based on semi-supervised 3D deep neural network and adaptive band selection","volume":"129","author":"Sellami","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, H., and Shen, Q. (2017). Spectral\u2013spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sens., 9.","DOI":"10.3390\/rs9010067"},{"key":"ref_36","unstructured":"Mortensen, A.K., Dyrmann, M., Karstoft, H., Jorgensen, R.N., and Gislum, R. (2016, January 28\u201329). Semantic segmentation of mixed crops using deep convolutional neural network. Proceedings of the International Conference on Agricultural Engineering, Aarhus, Denmark."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Potena, C., Nardi, D., and Pretto, A. (2016, January 3\u20137). Fast and accurate crop and weed identification with summarized train sets for precision agriculture. Proceedings of the International Conference on Intelligent Autonomous Systems, Shanghai, China.","DOI":"10.1007\/978-3-319-48036-7_9"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.compag.2017.10.027","article-title":"Weed detection in soybean crops using convnets","volume":"143","author":"Freitas","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep learning for remote sensing data: A technical tutorial on the state of the art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote. Sens. Mag."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_41","unstructured":"Wilson, A.C., Roelofs, R., Stern, M., Srebro, N., and Recht, B. (2017). The marginal value of adaptive gradient methods in machine learning. Advances in Neural Information Processing Systems, The MIT Press."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014, January 6\u201312). Visualizing and understanding convolutional networks. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patcog.2017.05.015","article-title":"How deep learning extracts and learns leaf features for plant classification","volume":"71","author":"Lee","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_44","unstructured":"Chen, Z., Lam, O., Jacobson, A., and Milford, M. (2014). Convolutional neural network-based place recognition. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Oquab, M., Bottou, L., Laptev, I., and Sivic, J. (2014, January 24\u201327). Learning and transferring mid-level image representations using convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.222"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Sun, X., Zhang, F., Yang, L., Zhang, B., and Gao, L. (2015, January 2\u20135). A hyperspectral image spectral unmixing method integrating slic superpixel segmentation. Proceedings of the 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Tokyo, Japan.","DOI":"10.1109\/WHISPERS.2015.8075428"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1109\/TGRS.2011.2165957","article-title":"Locality-preserving dimensionality reduction and classification for hyperspectral image analysis","volume":"50","author":"Li","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","first-page":"93","article-title":"Composite kernels for hyperspectral image classification","volume":"3","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., and Lenc, K. (2015, January 26\u201330). Matconvnet: Convolutional neural networks for matlab. Proceedings of the 23rd ACM International Conference on Multimedia, Brisbane, Australia.","DOI":"10.1145\/2733373.2807412"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/14\/1692\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:06:30Z","timestamp":1760187990000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/14\/1692"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,17]]},"references-count":50,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["rs11141692"],"URL":"https:\/\/doi.org\/10.3390\/rs11141692","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,17]]}}}