{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:28:46Z","timestamp":1760243326864,"version":"build-2065373602"},"reference-count":13,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2014,8,15]],"date-time":"2014-08-15T00:00:00Z","timestamp":1408060800000},"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>Traditional image classification algorithms are mainly divided into unsupervised and supervised paradigms. In the first paradigm, algorithms are designed to automatically estimate the classes\u2019 distributions in the feature space. The second paradigm depends on the knowledge of a domain expert to identify representative examples from the image to be used for estimating the classification model. Recent improvements in human-computer interaction (HCI) enable the construction of more intuitive graphic user interfaces (GUIs) to help users obtain desired results. In remote sensing image classification, GUIs still need advancements. In this work, we describe our efforts to develop an improved GUI for selecting the representative samples needed to estimate the classification model. The idea is to identify changes in the common strategies for sample selection to create a user-driven sample selection, which focuses on different views of each sample, and to help domain experts identify explicit classification rules, which is a well-established technique in geographic object-based image analysis (GEOBIA). We also propose the use of the well-known nearest neighbor algorithm to identify similar samples and accelerate the classification.<\/jats:p>","DOI":"10.3390\/rs6087580","type":"journal-article","created":{"date-parts":[[2014,8,15]],"date-time":"2014-08-15T11:25:11Z","timestamp":1408101911000},"page":"7580-7591","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Improvements in Sample Selection Methods for Image Classification"],"prefix":"10.3390","volume":"6","author":[{"given":"Thales","family":"K\u00f6rting","sequence":"first","affiliation":[{"name":"Image Processing Division, Brazil's National Institute for Space Research, Av. dos Astronautas, 1758 S\u00e3o Jos\u00e9 dos Campos, Brazil"}]},{"given":"Leila","family":"Fonseca","sequence":"additional","affiliation":[{"name":"Image Processing Division, Brazil's National Institute for Space Research, Av. dos Astronautas, 1758 S\u00e3o Jos\u00e9 dos Campos, Brazil"}]},{"given":"Emiliano","family":"Castejon","sequence":"additional","affiliation":[{"name":"Image Processing Division, Brazil's National Institute for Space Research, Av. dos Astronautas, 1758 S\u00e3o Jos\u00e9 dos Campos, Brazil"}]},{"given":"Laercio","family":"Namikawa","sequence":"additional","affiliation":[{"name":"Image Processing Division, Brazil's National Institute for Space Research, Av. dos Astronautas, 1758 S\u00e3o Jos\u00e9 dos Campos, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2014,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic object-based image analysis\u2014Towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1007\/3-540-45424-1_32","article-title":"What\u2019s in an image?","volume":"2205","author":"Egenhofer","year":"2001","journal-title":"Lect. Notes Comput. Sci"},{"key":"ref_4","unstructured":"Landgrebe, D. (1998). Multispectral Data Analysis: A Signal Theory Perspective, Purdue University."},{"key":"ref_5","first-page":"12","article-title":"Whats wrong with pixels? Some recent developments interfacing remote sensing and GIS","volume":"6","author":"Blaschke","year":"2001","journal-title":"GeoBIT\/GIS"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Blaschke, T., Lang, S., and Hay, G. (2008). Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, Springer-Verlag.","DOI":"10.1007\/978-3-540-77058-9"},{"key":"ref_7","unstructured":"Theodoridis, S., and Koutroumbas, K. (2008). Pattern Recognition, Academic Press. [4th ed.]."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.compenvurbsys.2012.01.003","article-title":"Knowledge-based region labeling for remote sensing image interpretation","volume":"36","author":"Forestier","year":"2012","journal-title":"Comput. Environ. Urban Syst"},{"key":"ref_9","unstructured":"Baatz, M., and Sch\u00e4pe, A. (2000). Angewandte Geographische Informationsverarbeitung, Herbert Wichmann Verlag."},{"key":"ref_10","unstructured":"Witten, I., and Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques, Diane Cerra. [2nd ed.]."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.cageo.2013.02.007","article-title":"GeoDMA\u2014Geographic data mining analyst","volume":"57","author":"Fonseca","year":"2013","journal-title":"Comput. Geosci"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/978-3-540-74831-1_12","article-title":"TerraLib: An open source GIS library for large-scale environmental and socio-economic applications","volume":"2","author":"Vinhas","year":"2008","journal-title":"Open Source Approaches Spatial Data Handl"},{"key":"ref_13","unstructured":"Quinlan, J. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/8\/7580\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:14:51Z","timestamp":1760217291000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/8\/7580"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,8,15]]},"references-count":13,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2014,8]]}},"alternative-id":["rs6087580"],"URL":"https:\/\/doi.org\/10.3390\/rs6087580","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2014,8,15]]}}}