{"title":"基于高空间分辨率图像的森林监测烟尘分类","authors":"Julia Åhlén","doi":"10.5593/sgem2022/2.1/s08.16","DOIUrl":null,"url":null,"abstract":"Forest fires cause major damage to human habitats and forest ecosystems. Early detection may prevent serious consequences of fast fire spread. Although there are many smoke detection algorithms employed by various optical remote sensing systems, there is still a major misdetection of images containing fog. Fog exhibits similar visual characteristics to smoke. Furthermore, when monitoring dense forests many smoke detection algorithms would fail in robust recognition due to fog covering the trees at dawn. There have been more or less successful attempts to separate smoke from a fog in optical imagery however, these algorithms are strongly connected to a specific application area or use a semiautomatic approach. This work aims to propose a novel smoke and fog separation algorithm based on color space model calculation followed by rule-based shape analysis. In addition, the internal properties of the smoke candidate areas are examined for linear attenuation towards higher energy wavelength. Those areas are then investigated for internal shape properties such as convex hull and eccentricity. Several tests conducted on various high-resolution aerial images suggest that the system is effective in differentiating smoke and fog and thus considered to be robust in early fire detection in forest areas.","PeriodicalId":375880,"journal":{"name":"22nd SGEM International Multidisciplinary Scientific GeoConference Proceedings 2022, Informatics, Geoinformatics and Remote Sensing","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"SMOKE AND FOG CLASSIFICATION IN FOREST MONITORING USING HIGH SPATIAL RESOLUTION IMAGES\",\"authors\":\"Julia Åhlén\",\"doi\":\"10.5593/sgem2022/2.1/s08.16\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Forest fires cause major damage to human habitats and forest ecosystems. Early detection may prevent serious consequences of fast fire spread. Although there are many smoke detection algorithms employed by various optical remote sensing systems, there is still a major misdetection of images containing fog. Fog exhibits similar visual characteristics to smoke. Furthermore, when monitoring dense forests many smoke detection algorithms would fail in robust recognition due to fog covering the trees at dawn. There have been more or less successful attempts to separate smoke from a fog in optical imagery however, these algorithms are strongly connected to a specific application area or use a semiautomatic approach. This work aims to propose a novel smoke and fog separation algorithm based on color space model calculation followed by rule-based shape analysis. In addition, the internal properties of the smoke candidate areas are examined for linear attenuation towards higher energy wavelength. Those areas are then investigated for internal shape properties such as convex hull and eccentricity. Several tests conducted on various high-resolution aerial images suggest that the system is effective in differentiating smoke and fog and thus considered to be robust in early fire detection in forest areas.\",\"PeriodicalId\":375880,\"journal\":{\"name\":\"22nd SGEM International Multidisciplinary Scientific GeoConference Proceedings 2022, Informatics, Geoinformatics and Remote Sensing\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"22nd SGEM International Multidisciplinary Scientific GeoConference Proceedings 2022, Informatics, Geoinformatics and Remote Sensing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5593/sgem2022/2.1/s08.16\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"22nd SGEM International Multidisciplinary Scientific GeoConference Proceedings 2022, Informatics, Geoinformatics and Remote Sensing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5593/sgem2022/2.1/s08.16","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
SMOKE AND FOG CLASSIFICATION IN FOREST MONITORING USING HIGH SPATIAL RESOLUTION IMAGES
Forest fires cause major damage to human habitats and forest ecosystems. Early detection may prevent serious consequences of fast fire spread. Although there are many smoke detection algorithms employed by various optical remote sensing systems, there is still a major misdetection of images containing fog. Fog exhibits similar visual characteristics to smoke. Furthermore, when monitoring dense forests many smoke detection algorithms would fail in robust recognition due to fog covering the trees at dawn. There have been more or less successful attempts to separate smoke from a fog in optical imagery however, these algorithms are strongly connected to a specific application area or use a semiautomatic approach. This work aims to propose a novel smoke and fog separation algorithm based on color space model calculation followed by rule-based shape analysis. In addition, the internal properties of the smoke candidate areas are examined for linear attenuation towards higher energy wavelength. Those areas are then investigated for internal shape properties such as convex hull and eccentricity. Several tests conducted on various high-resolution aerial images suggest that the system is effective in differentiating smoke and fog and thus considered to be robust in early fire detection in forest areas.