{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T13:34:53Z","timestamp":1777988093322,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T00:00:00Z","timestamp":1629417600000},"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>Identification of optimal spectral bands often involves collecting in-field spectral signatures followed by thorough analysis. Such rigorous field sampling exercises are tedious, cumbersome, and often impractical on challenging terrain, which is a limiting factor for programmable hyperspectral sensors mounted on unmanned aerial vehicles (UAV-hyperspectral systems), requiring a pre-selection of optimal bands when mapping new environments with new target classes with unknown spectra. An innovative workflow has been designed and implemented to simplify the process of in-field spectral sampling and its realtime analysis for the identification of optimal spectral wavelengths. The band selection optimization workflow involves particle swarm optimization with minimum estimated abundance covariance (PSO-MEAC) for the identification of a set of bands most appropriate for UAV-hyperspectral imaging, in a given environment. The criterion function, MEAC, greatly simplifies the in-field spectral data acquisition process by requiring a few target class signatures and not requiring extensive training samples for each class. The metaheuristic method was tested on an experimental site with diversity in vegetation species and communities. The optimal set of bands were found to suitably capture the spectral variations between target vegetation species and communities. The approach streamlines the pre-tuning of wavelengths in programmable hyperspectral sensors in mapping applications. This will additionally reduce the total flight time in UAV-hyperspectral imaging, as obtaining information for an optimal subset of wavelengths is more efficient, and requires less data storage and computational resources for post-processing the data.<\/jats:p>","DOI":"10.3390\/rs13163295","type":"journal-article","created":{"date-parts":[[2021,8,22]],"date-time":"2021-08-22T22:59:27Z","timestamp":1629673167000},"page":"3295","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Particle Swarm Optimization Based Approach to Pre-tune Programmable Hyperspectral Sensors"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5542-3751","authenticated-orcid":false,"given":"Bikram Pratap","family":"Banerjee","sequence":"first","affiliation":[{"name":"Agriculture Victoria, Grains Innovation Park, 110 Natimuk Road, Horsham, VIC 3400, Australia"},{"name":"School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney, NSW 2052, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simit","family":"Raval","sequence":"additional","affiliation":[{"name":"School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney, NSW 2052, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"408","DOI":"10.3390\/rs1030408","article-title":"Evaluating Principal Components Analysis for Identifying Optimal Bands Using Wetland Hyperspectral Measurements From the Great Lakes, USA","volume":"1","author":"Torbick","year":"2009","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the mean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.rse.2005.04.020","article-title":"Identifying optimal spectral bands from in situ measurements of Great Lakes coastal wetlands using second-derivative analysis","volume":"97","author":"Becker","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.rse.2006.11.005","article-title":"A classification-based assessment of the optimal spectral and spatial resolutions for Great Lakes coastal wetland imagery","volume":"108","author":"Becker","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yue, J., Yang, G., Li, C., Li, Z., Wang, Y., Feng, H., and Xu, B. (2017). Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models. Remote Sens., 9.","DOI":"10.3390\/rs9070708"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Aasen, H., Honkavaara, E., Lucieer, A., and Zarco-Tejada, P.J. (2018). Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows. Remote Sens., 10.","DOI":"10.3390\/rs10071091"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4136","DOI":"10.1080\/01431161.2020.1714771","article-title":"UAV-hyperspectral imaging of spectrally complex environments","volume":"41","author":"Banerjee","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Saari, H., Aallos, V.-V., Akuj\u00e4rvi, A., Antila, T., Holmlund, C., Kantoj\u00e4rvi, U., M\u00e4kynen, J., and Ollila, J. (2009, January 22). Novel miniaturized hyperspectral sensor for UAV and space applications. Proceedings of the Sensors, Systems, and Next-Generation Satellites XIII, Berlin, Germany.","DOI":"10.1117\/12.830284"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kaivosoja, J., Pesonen, L., Kleemola, J., P\u00f6l\u00f6nen, I., Salo, H., Honkavaara, E., Saari, H., M\u00e4kynen, J., and Rajala, A. (2013, January 16). A case study of a precision fertilizer application task generation for wheat based on classified hyperspectral data from UAV combined with farm history data. Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XV, Dresden, Germany.","DOI":"10.1117\/12.2029165"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"P\u00f6l\u00f6nen, I., Saari, H., Kaivosoja, J., Honkavaara, E., and Pesonen, L. (2013, January 16). Hyperspectral imaging based biomass and nitrogen content estimations from light-weight UAV. Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XV, Dresden, Germany.","DOI":"10.1117\/12.2028624"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"139","DOI":"10.5194\/isprsarchives-XL-1-W1-139-2013","article-title":"New light-weight stereosopic spectrometric airborne imaging technology for high-resolution environmental remote sensing\u2014Case studies in water quality mapping","volume":"XL-1\/W1","author":"Honkavaara","year":"2013","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"15467","DOI":"10.3390\/rs71115467","article-title":"Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level","volume":"7","author":"Honkavaara","year":"2015","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Minet, J., Taboury, J., P\u00e9alat, M., Roux, N., Lonnoy, J., and Ferrec, Y. (2010, January 11). Adaptive band selection snapshot multispectral imaging in the VIS\/NIR domain. Proceedings of the Electro-Optical Remote Sensing, Photonic Technologies, and Applications IV, Toulouse, France.","DOI":"10.1117\/12.864578"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1109\/36.934069","article-title":"A new search algorithm for feature selection in hyperspectral remote sensing images","volume":"39","author":"Serpico","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., and de Ridder, D. (2006). Hyperspectral data selection from mutual information between image bands. Structural, Syntactic, and Statistical Pattern Recognition, Proceedings of the Joint IAPR International Workshops, SSPR 2006 and SPR 2006, Hong Kong, China, 17\u201319 August 2006, Springer.","DOI":"10.1007\/11815921"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhuo, L., Zheng, J., Li, X., Wang, F., Ai, B., and Qian, J. (2008, January 7). A genetic algorithm based wrapper feature selection method for classification of hyperspectral images using support vector machine. Proceedings of the Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, Guangzhou, China.","DOI":"10.1117\/12.813256"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Huang, R., and Zhou, L. (2009, January 25). Hyperspectral feature selection and classification with a RBF-based novel Double Parallel Feedforward Neural Network and evolution algorithms. Proceedings of the 2009 4th IEEE Conference on Industrial Electronics and Applications, Xi\u2019an, China.","DOI":"10.1109\/ICIEA.2009.5138290"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1109\/LGRS.2011.2158185","article-title":"Semisupervised Band Clustering for Dimensionality Reduction of Hyperspectral Imagery","volume":"8","author":"Su","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yang, H., and Du, Q. (2011, January 24\u201329). Particle swarm optimization-based dimensionality reduction for hyperspectral image classification. Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada.","DOI":"10.1109\/IGARSS.2011.6049683"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.knosys.2010.07.003","article-title":"An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine","volume":"24","author":"Li","year":"2011","journal-title":"Knowl.-Based Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1080\/2150704X.2013.777485","article-title":"Hybrid genetic algorithm for feature selection with hyperspectral data","volume":"4","author":"Pal","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.engappai.2013.07.010","article-title":"A novel approach to hyperspectral band selection based on spectral shape similarity analysis and fast branch and bound search","volume":"27","author":"Li","year":"2014","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2659","DOI":"10.1109\/JSTARS.2014.2312539","article-title":"Optimized hyperspectral band selection using particle swarm optimization","volume":"7","author":"Su","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1109\/LGRS.2010.2053516","article-title":"An Efficient Method for Supervised Hyperspectral Band Selection","volume":"8","author":"Yang","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/LGRS.2014.2337320","article-title":"Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization","volume":"12","author":"Ghamisi","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1080\/2150704X.2018.1446564","article-title":"Alignment of UAV-hyperspectral bands using keypoint descriptors in a spectrally complex environment","volume":"9","author":"Banerjee","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"111830","DOI":"10.1016\/j.rse.2020.111830","article-title":"Machine learning estimators for the quantity and quality of grass swards used for silage production using drone-based imaging spectrometry and photogrammetry","volume":"246","author":"Oliveira","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_28","unstructured":"Commonwealth of Australia (2014). Temperate Highland Peat Swamps on Sandstone: Ecological Characteristics, Sensitivities to Change, and Monitoring and Reporting Technique."},{"key":"ref_29","unstructured":"National Parks & Wildlife Services (2003). The Native Vegetation of the Woronora, O\u2019Hares and Metropolitan Catchments."},{"key":"ref_30","unstructured":"Orfanidis, S.J. (1996). Introduction to Signal Processing, Prentice Hall."},{"key":"ref_31","unstructured":"Eberhart, R., and Kennedy, J. (1995, January 4). A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS95, Nagoya, Japan."},{"key":"ref_32","first-page":"155","article-title":"Support vector regression machines","volume":"9","author":"Drucker","year":"1997","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3374","DOI":"10.1109\/TGRS.2006.880628","article-title":"Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images","volume":"44","author":"Bazi","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","first-page":"203","article-title":"Support vector regression","volume":"11","author":"Basak","year":"2007","journal-title":"Neural Inf. Process. Lett. Rev."},{"key":"ref_35","first-page":"401","article-title":"Estimating the kappa coefficient and its variance under stratified random sampling","volume":"62","author":"Stehman","year":"1996","journal-title":"Photogramm. Eng. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3295\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:48:02Z","timestamp":1760165282000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3295"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,20]]},"references-count":35,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13163295"],"URL":"https:\/\/doi.org\/10.3390\/rs13163295","relation":{"has-preprint":[{"id-type":"doi","id":"10.36227\/techrxiv.14058233.v1","asserted-by":"object"}]},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,20]]}}}