{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T01:37:39Z","timestamp":1768095459255,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2009,11,9]],"date-time":"2009-11-09T00:00:00Z","timestamp":1257724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Nearest neighbor techniques are commonly used in remote sensing, pattern recognition and statistics to classify objects into a predefined number of categories based on a given set of predictors. These techniques are especially useful for highly nonlinear relationship between the variables. In most studies the distance measure is adopted a priori. In contrast we propose a general procedure to find an adaptive metric that combines a local variance reducing technique and a linear embedding of the observation space into an appropriate Euclidean space. To illustrate the application of this technique, two agricultural land cover classifications using mono-temporal and multi-temporal Landsat scenes are presented. The results of the study, compared with standard approaches used in remote sensing such as maximum likelihood (ML) or k-Nearest Neighbor (k-NN) indicate substantial improvement with regard to the overall accuracy and the cardinality of the calibration data set. Also, using MNN in a soft\/fuzzy classification framework demonstrated to be a very useful tool in order to derive critical areas that need some further attention and investment concerning additional calibration data.<\/jats:p>","DOI":"10.3390\/rs1040875","type":"journal-article","created":{"date-parts":[[2009,11,9]],"date-time":"2009-11-09T10:24:39Z","timestamp":1257762279000},"page":"875-895","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Supervised Classification of Agricultural Land Cover Using a Modified k-NN Technique (MNN) and Landsat Remote Sensing Imagery"],"prefix":"10.3390","volume":"1","author":[{"given":"Luis","family":"Samaniego","sequence":"first","affiliation":[{"name":"Department of Computational Hydrosystems, UFZ\u2013Helmholtz-Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig, Germany"}]},{"given":"Karsten","family":"Schulz","sequence":"additional","affiliation":[{"name":"Department of Geography, Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen, Luisenstr. 37, 80333 Munich, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2009,11,9]]},"reference":[{"key":"ref_1","first-page":"1895","article-title":"Quantification of impervious surface in the Snohomish water resources inventory area of Western Washington from 1972-2006","volume":"112","author":"Powell","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.rse.2006.04.010","article-title":"Vegetation cover mapping in India using multi-temporal IRS Wide Field Sensor (WiFS) data","volume":"103","author":"Joshi","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3708","DOI":"10.1016\/j.rse.2008.05.006","article-title":"Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss","volume":"112","author":"Potapov","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1646","DOI":"10.1016\/j.rse.2009.03.010","article-title":"Daytime fire detection using airborne hyperspectral data","volume":"113","author":"Dennison","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.rse.2005.08.012","article-title":"Spatial and temporal patterns of China\u2019s cropland during 1990-2000: an analysis based on Landsat TM data","volume":"98","author":"Liu","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1874","DOI":"10.1109\/TGRS.2005.848706","article-title":"An adaptive fuzzy evidential nearest neighbor formulation for classifying remote sensing images","volume":"43","author":"Zhu","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1109\/72.914517","article-title":"An introduction to kernel-based learning algorithms","volume":"12","author":"Muller","year":"2001","journal-title":"IEEE Trans. Neural Networks"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Richards, J.A., and Jia, X. (2006). Remote Sensing Digital Image Analysis: an Introduction, Springer-Verlag. [4th ed.].","DOI":"10.1007\/3-540-29711-1"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1109\/36.992798","article-title":"Fuzzy rule-based classification of remotely sensed imagery","volume":"40","author":"Samaniego","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/S0165-0114(02)00220-8","article-title":"A study of parameter values for a Mahalanobis distance fuzzy classifier","volume":"137","author":"Deer","year":"2003","journal-title":"Fuzzy Sets Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1023\/A:1007465528199","article-title":"Bayesian network classifiers","volume":"29","author":"Friedman","year":"1997","journal-title":"Mach. Learn."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1080\/014311697218700","article-title":"Introduction neural networks in remote sensing","volume":"18","author":"Atkinson","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1109\/TGRS.2004.827257","article-title":"A relative evaluation of multiclass image classification by support vector machines","volume":"42","author":"Foody","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2084","DOI":"10.1109\/TGRS.2005.853186","article-title":"A classification approach based on SVM for electromagnetic subsurface sensing","volume":"43","author":"Massa","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1080\/01621459.1988.10478639","article-title":"Locally weighted regression: an approach to regression analysis by local fitting","volume":"83","author":"Cleveland","year":"1988","journal-title":"J. Amer. Statist. Assoc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/S0034-4257(01)00209-7","article-title":"Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method","volume":"77","author":"Ek","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"McLachlan, G. (1992). Discriminant Analysis and Statistical Pattern Recognition, John Wiley & Sons.","DOI":"10.1002\/0471725293"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1080\/01431169608948706","article-title":"Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data","volume":"17","author":"Foody","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1901","DOI":"10.1109\/TGRS.2005.852163","article-title":"Supervised segmentation of remote sensing images based on a tree-structured MRF model","volume":"43","author":"Poggi","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1109\/TGRS.2004.842292","article-title":"Exploiting manifold geometry in hyperspectral imagery","volume":"43","author":"Bachmann","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1109\/TGRS.2006.886177","article-title":"Extraction of spectral channels from hyperspectral images for classification purposes","volume":"45","author":"Serpico","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","first-page":"W08404","article-title":"Modeling data relationships with a local variance reducing technique: applications in hydrology","volume":"41","author":"Pegram","year":"2005","journal-title":"Water Resour. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2112","DOI":"10.1109\/TGRS.2008.916629","article-title":"Supervised classification of remotely sensed imagery using a modified k-nn technique","volume":"46","author":"Samaniego","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1109\/34.506411","article-title":"Discriminant adaptive nearest neighbor classification","volume":"18","author":"Hastie","year":"1996","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1109\/TPAMI.2004.1273978","article-title":"Adaptive quasiconformal kernel nearest neighbor classification","volume":"26","author":"Peng","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1162\/neco.1995.7.1.72","article-title":"Similarity metric learning for a variable-kernel classifier","volume":"7","author":"Lowe","year":"1995","journal-title":"Neural Computat."},{"key":"ref_28","first-page":"4955","article-title":"On the generalised distance in statistics","volume":"2","author":"Mahalanobis","year":"1936","journal-title":"Proc. Natl. Inst. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fukunaga, K. (1990). Introduction to Statistical Pattern Recognition, Academic Press Professional, Inc.. [2nd ed.].","DOI":"10.1016\/B978-0-08-047865-4.50007-7"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1109\/TGRS.2003.815674","article-title":"The effect of solar illumination angle and sensor view angle on observed patterns of spatial structure in tallgrass prairie","volume":"42","author":"Goodin","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","unstructured":"Isaaks, E.H., and Srivastava, R.M. (1989). An Introduction to Applied Geostatistics, Oxford University Press."},{"key":"ref_32","unstructured":"Aarts, E., and Korst, J. (1989). Simulated Annealing and Boltzmann Machines: a Stochastic Approach to Combinatorial Optimization and Neural Computing, John Wiley & Sons."},{"key":"ref_33","unstructured":"Falkenauer, E. (1997). Genetic Algorithms and Grouping Problems, John Wiley & Sons."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Tolson, B.A., and Shoemaker, C.A. (2007). Dynamically dimensioned search algorithm for computationally efficient watershed model calibration. Water Resour. Res., 43.","DOI":"10.1029\/2005WR004723"},{"key":"ref_35","first-page":"661","article-title":"Some comments on Cp","volume":"15","author":"Mallows","year":"1973","journal-title":"Technometrics"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/1\/4\/875\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T22:11:36Z","timestamp":1760220696000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/1\/4\/875"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2009,11,9]]},"references-count":35,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2009,12]]}},"alternative-id":["1040875"],"URL":"https:\/\/doi.org\/10.3390\/rs1040875","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2009,11,9]]}}}