Avaliação da técnica de modelo linear de mistura espectral como subsídio à classificação do uso e ocupação do solo

• Ano de defesa: 2023
• Autor(a) principal: Fernanda Paula Bicalho Pio
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; Brasil; IGC - INSTITUTO DE GEOCIENCIAS; Programa de Pós-Graduação em Análise e Modelagem de Sistemas Ambientais; UFMG
• Palavras-chave em Português: Mistura espectral; Modelo linear de mistura espectral; Classificação de imagens; Sentinel-2; Modelagem de dados – Aspectos ambientais; Solo – Uso – Minas Gerais; Processamento de imagens; Classificação
• Link de acesso: http://hdl.handle.net/1843/54383

The constant development of technologies for the acquisition and processing of remote sensing data has encouraged the application of digital images in various circumstances, including environmental studies and monitoring of the Earth's surface. Considering the development of anthropic actions and the intensification of alternative land use for various activities, studies of land use based on remote sensing data have become more common and geotechnologies are considered important tools for providing data that help territorial management work. The application of images provided with free access also demonstrates greater use, including data from the sentinel satellite. The multispectral images obtained through remote sensing allow the application of analysis techniques to obtain qualitative and quantitative information on a given region. A significant application of these data is observed in analyzes of the use and coverage of the earth's surface, which is knowledge of great importance for territorial management. For land use and occupation classification studies, several image processing and classification techniques can be applied. These, in turn, vary depending on the characteristics of the scene acquisition sensors and the purpose of the experiment. In image classification studies, it is common to encounter spectral mixing problems that are observed due to the spatial resolution of the sensors. Depending on the purpose and parameters of the study to be carried out, the spectral mixture may be characterized as a limitation of the data and processes carried out. Thus, working methods are used to extract information from images with greater detail, considering the properties of the materials present within a pixel. The study of the spectral mixture is used to help the classification techniques of digital images. In the analysis of the spectral mixture, the spectrum presented inside a pixel is decomposed and different methods can be applied for this purpose. The most frequently used method is the linear model of spectral mixing (MLME). The ease of operation of the method makes it used in studies in different parts of the world with good results. The present work aims to address the linear model of spectral mixing based on the method of least squares with restriction. This model assumes that the spectral response at each pixel in any spectral band is due to a linear combination of the spectral responses of each component present in the mixture. Thus, in this study, we sought to compare two classification results of land use and occupation for the Ribeirão Jirau microbasin region, located in the municipalities of Itabira and Santa Maria de Itabira, in the interior of Minas Gerais, with a mapping generated from from conventional classification with scenes of digital images from the Sentinel-2 satellite and the other result generated from fraction images obtained in the linear model of spectral mixture. The results were compared based on concordance coefficients extracted from the error matrix, where general precision analyzes were considered (global accuracy and Kappa coefficient) and the individualized analysis by class with the study of user and producer precisions, seeking to know detail the performance of the classifiers. The overall precision values for classifications derived from Sentinel-2 and MLME data were 65% and 62%, respectively. The Kappa coefficient presented a result of 0,53 for the classification obtained from the satellite spectral data and 0,62 for the classification obtained from the mixture model, indicating that the quality of the two mappings can be considered as moderate, good enough or good. The analysis of the classifications by category revealed that the two mappings presented specificities, mainly for the classes of natural forest and planted forest. The study allowed attesting the functionality of using MLME fraction images as a subsidy for image classification and provided possibilities for new types of analysis for the application of the model.