{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:24:46Z","timestamp":1742912686366,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030177706"},{"type":"electronic","value":"9783030177713"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-17771-3_7","type":"book-chapter","created":{"date-parts":[[2019,4,30]],"date-time":"2019-04-30T17:06:30Z","timestamp":1556643990000},"page":"83-90","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["UAV Downwash-Based Terrain Classification Using Wiener-Khinchin and EMD Filters"],"prefix":"10.1007","author":[{"given":"Jo\u00e3o P.","family":"Matos-Carvalho","sequence":"first","affiliation":[]},{"given":"Andr\u00e9","family":"Mora","sequence":"additional","affiliation":[]},{"given":"Ra\u00fal T.","family":"Rato","sequence":"additional","affiliation":[]},{"given":"Ricardo","family":"Mendon\u00e7a","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9 M.","family":"Fonseca","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,4,16]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","DOI":"10.1201\/b22485","volume-title":"Intelligent Autonomy of UAVs: Advanced Missions and Future Use","author":"Y Bestaoui Sebbane","year":"2018","unstructured":"Bestaoui Sebbane, Y.: Intelligent Autonomy of UAVs: Advanced Missions and Future Use. CRC Press, Boca Raton (2018)"},{"issue":"02","key":"7_CR2","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1142\/S1793536909000138","volume":"01","author":"A Linderhed","year":"2009","unstructured":"Linderhed, A.: Image Empirical Mode Decomposition: A New Tool For Image Processing. Adv. Adapt. Data Anal. 01(02), 265\u2013294 (2009)","journal-title":"Adv. Adapt. Data Anal."},{"issue":"1","key":"7_CR3","doi-asserted-by":"publisher","first-page":"1074","DOI":"10.3390\/rs70101074","volume":"7","author":"Q Feng","year":"2015","unstructured":"Feng, Q., Liu, J., Gong, J.: UAV remote sensing for urban vegetation mapping using random forest and texture analysis. Remote Sens. 7(1), 1074\u20131094 (2015)","journal-title":"Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Khan, Y.N., Komma, P., Bohlmann, K., Zell, A.: Grid-based visual terrain classification for outdoor robots using local features. In: IEEE SSCI 2011: CIVTS 2011 (2011)","key":"7_CR4","DOI":"10.1109\/CIVTS.2011.5949534"},{"key":"7_CR5","series-title":"CIVI","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-85729-748-8","volume-title":"Computer Vision Using Local Binary Patterns","author":"M Pietik\u00e4inen","year":"2011","unstructured":"Pietik\u00e4inen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns. CIVI, vol. 40. Springer, London (2011). https:\/\/doi.org\/10.1007\/978-0-85729-748-8"},{"doi-asserted-by":"crossref","unstructured":"Ebadi, F., Norouzi, M.: Road Terrain detection and Classification algorithm based on the Color Feature extraction. In: Artificial Intelligence and Robotics, pp. 139\u2013146. IEEE (2017)","key":"7_CR6","DOI":"10.1109\/RIOS.2017.7956457"},{"key":"7_CR7","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.rse.2014.11.001","volume":"158","author":"WY Yan","year":"2015","unstructured":"Yan, W.Y., Shaker, A., El-Ashmawy, N.: Urban land cover classification using airborne LiDAR data: a review. Remote Sens. Environ. 158, 295\u2013310 (2015)","journal-title":"Remote Sens. Environ."},{"doi-asserted-by":"crossref","unstructured":"Wallace, L., Lucieer, A., Malenovsky, Z., Turner, D., Vop\u011bnka, P.: Assessment of forest structure using two UAV techniques: a comparison of airborne laser scanning and structure from motion (SfM) point clouds (2016)","key":"7_CR8","DOI":"10.3390\/f7030062"},{"key":"7_CR9","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.isprsjprs.2017.02.015","volume":"126","author":"W GruszczynSki","year":"2017","unstructured":"GruszczynSki, W., Matwij, W., \u0106wi\u0105ka\u0142a, P.: Comparison of low-altitude UAV photogrammetry with terrestrial laser scanning as data-source methods for terrain covered in low vegetation. ISPRS Photogramm. Remote Sens. 126, 168\u2013179 (2017)","journal-title":"ISPRS Photogramm. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Pombeiro, R., et al.: Water detection from downwash-induced optical flow for a multirotor UAV. In: OCEANS 2015, pp. 1\u20136. IEEE (2015)","key":"7_CR10","DOI":"10.23919\/OCEANS.2015.7404458"},{"doi-asserted-by":"crossref","unstructured":"Matos-Carvalho, J.P., Fonseca, J.M., Mora, A.D.: UAV downwash dynamic texture features for terrain classification on autonomous navigation. In: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems. Annals of Computer Science and Information Systems, vol. 15, pp. 1079\u20131083. IEEE (2018)","key":"7_CR11","DOI":"10.15439\/2018F185"},{"key":"7_CR12","doi-asserted-by":"publisher","first-page":"147","DOI":"10.3390\/info8040147","volume":"8","author":"A Mora","year":"2017","unstructured":"Mora, A., et al.: Land cover classification from multispectral data using computational intelligence tools: a comparative study. Information 8, 147 (2017)","journal-title":"Information"},{"key":"7_CR13","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.geoderma.2015.11.014","volume":"265","author":"B Heung","year":"2016","unstructured":"Heung, B., Ho, H.C., Zhang, J., Knudby, A., Bulmer, C.E., Schmidt, M.G.: An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping. Geoderma 265, 62\u201377 (2016)","journal-title":"Geoderma"},{"issue":"2","key":"7_CR14","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1109\/LRA.2015.2509024","volume":"1","author":"A Giusti","year":"2016","unstructured":"Giusti, A., et al.: A machine learning approach to visual perception of forest trails for mobile robots. IEEE Robot. Autom. Lett. 1(2), 661\u2013667 (2016)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"7_CR15","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1098\/rspa.1998.0193","volume":"454","author":"NE Huang","year":"1998","unstructured":"Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Roy. Soc. Lond. A 454, 903\u2013995 (1998)","journal-title":"Proc. Roy. Soc. Lond. A"},{"unstructured":"Oonincx, P.J.: Empirical mode decomposition: a new tool for S-wave detection. In: CWI Reports of Probability, Networks and Algorithms (PNA) (2002). PNA-R0203, ISSN 1386\u20133711","key":"7_CR16"},{"issue":"6","key":"7_CR17","doi-asserted-by":"publisher","first-page":"1374","DOI":"10.1016\/j.ymssp.2007.11.028","volume":"22","author":"RT Rato","year":"2008","unstructured":"Rato, R.T., Ortigueira, M.D., Batista, A.G.: On the HHT, its problems, and some solutions. Mech. Syst. Sig. Process. 22(6), 1374\u20131394 (2008)","journal-title":"Mech. Syst. Sig. Process."}],"container-title":["IFIP Advances in Information and Communication Technology","Technological Innovation for Industry and Service Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-17771-3_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,17]],"date-time":"2022-09-17T05:29:54Z","timestamp":1663392594000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-17771-3_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030177706","9783030177713"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-17771-3_7","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"type":"print","value":"1868-4238"},{"type":"electronic","value":"1868-422X"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"16 April 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DoCEIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Doctoral Conference on Computing, Electrical and Industrial Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Costa de Caparica","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 May 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 May 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"doceis2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/doceis.dee.fct.unl.pt\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"73","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"36","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"49% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"3.17","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"n\/a","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}