{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T09:58:10Z","timestamp":1784195890333,"version":"3.55.0"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319501147","type":"print"},{"value":"9783319501154","type":"electronic"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-50115-4_41","type":"book-chapter","created":{"date-parts":[[2017,3,20]],"date-time":"2017-03-20T03:32:46Z","timestamp":1489980766000},"page":"465-477","source":"Crossref","is-referenced-by-count":119,"title":["Deep Multispectral Semantic Scene Understanding of Forested Environments Using Multimodal Fusion"],"prefix":"10.1007","author":[{"given":"Abhinav","family":"Valada","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriel L.","family":"Oliveira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Brox","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wolfram","family":"Burgard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2017,3,21]]},"reference":[{"key":"41_CR1","unstructured":"Badrinarayanan, V., et al.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint (2015). arXiv:1511.00561"},{"key":"41_CR2","unstructured":"Bradley, D.M., et al.: Vegetation detection for mobile robot navigation. Technical report CMU-RI-TR-05-12, Carnegie Mellon University (2004)"},{"key":"41_CR3","doi-asserted-by":"publisher","unstructured":"Eitel, A., et al.: Multimodal deep learning for robust RGB-D object recognition. In: International Conference on Intelligent Robots and Systems (2015)","DOI":"10.1109\/IROS.2015.7353446"},{"issue":"6","key":"41_CR4","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1145\/358669.358692","volume":"24","author":"MA Fischler","year":"1981","unstructured":"Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis. Comm. ACM 24(6), 381\u2013395 (1981)","journal-title":"Comm. ACM"},{"key":"41_CR5","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprint (2015). arXiv:1512.03385"},{"key":"41_CR6","doi-asserted-by":"publisher","unstructured":"Hirschm\u00fcller, H.: Accurate and efficient stereo processing by semi-global matching and mutual information. In: CVPR (2005)","DOI":"10.1109\/CVPR.2005.56"},{"key":"41_CR7","unstructured":"Huete, A., Justice, C.O., van Leeuwen, W.J.D.: MODIS vegetation index (MOD 13), Algorithm Theoretical Basis Document (ATBD), Version 3.0, p. 129 (1999)"},{"key":"41_CR8","unstructured":"Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. arXiv preprint (2014). arXiv:1408.5093"},{"key":"41_CR9","unstructured":"Liu, F., Shen, C., Lin, G.: Deep convolutional neural fields for depth estimation from a single image (2014). arXiv:1411.6387"},{"key":"41_CR10","unstructured":"Liu, W., et al.: ParseNet: looking wider to see better. preprint (2015). arXiv:1506.04579"},{"key":"41_CR11","doi-asserted-by":"publisher","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, November 2015","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"2","key":"41_CR12","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"D Lowe","year":"2004","unstructured":"Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91\u2013110 (2004)","journal-title":"Int. J. Comput. Vis."},{"key":"41_CR13","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: MICCAI (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"41_CR14","doi-asserted-by":"crossref","unstructured":"Oliveira, G.L., Burgard, W., Brox, T.: Efficient deep methods for monocular road segmentation. In: International Conference on Intelligent Robots and Systems (2016)","DOI":"10.1109\/IROS.2016.7759717"},{"key":"41_CR15","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS (2015)"},{"key":"41_CR16","doi-asserted-by":"publisher","unstructured":"Schwarz, M., Schulz, H., Behnke, S.: RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features. In: ICRA (2015)","DOI":"10.1109\/ICRA.2015.7139363"},{"key":"41_CR17","unstructured":"Sermanet, P., et al.: Overfeat: integrated recognition, localization and detection using convolutional networks. arXiv preprint (2013). arXiv:1312.6229"},{"key":"41_CR18","unstructured":"Socher, R., et al.: Convolutional-recursive deep learning for 3D object classification. In: NIPS, vol. 25 (2012)"},{"key":"41_CR19","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv:1409.1556"},{"issue":"9","key":"41_CR20","first-page":"661","volume":"23","author":"S Thrun","year":"2006","unstructured":"Thrun, S., Montemerlo, M., Dahlkamp, H., et al.: Stanley: the robot that won the DARPA grand challenge. JFR 23(9), 661\u2013692 (2006)","journal-title":"JFR"}],"container-title":["Springer Proceedings in Advanced Robotics","2016 International Symposium on Experimental Robotics"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-50115-4_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,19]],"date-time":"2019-09-19T19:40:20Z","timestamp":1568922020000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-50115-4_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319501147","9783319501154"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-50115-4_41","relation":{},"ISSN":["2511-1256","2511-1264"],"issn-type":[{"value":"2511-1256","type":"print"},{"value":"2511-1264","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}