{"title":"鲸鸦搜索优化使深度卷积神经网络用于洪水检测","authors":"M. B. Mulik, J. V., Pandurangarao N. Kulkarni","doi":"10.1080/19479832.2023.2186957","DOIUrl":null,"url":null,"abstract":"ABSTRACT The satellite images are more attracted in the field of flood detection. For planning actions during emergencies, flood detection plays a vital role, but the major barrier is that using satellite images to detect flooded regions. For flood detection, this method innovates a model named Whale-crow search algorithm on the basis of deep convolutional neural network (W-CSA DCNN) approach. Pre-processing, classification, segmentation and feature extraction are the four steps which is included in this model. For obtaining sound and antiquity from the input image initially, the satellite imagery is given to pre-processing and then for obtaining the features on the basis of vegetation indices the pre-processed image is put through the feature extraction process. By means of Kernel Fuzzy Auto regressive (KFAR) model, the acquire features are subsequently used in the segmentation process. After obtaining the segments, it is given to the classification, which is carried out by means of DCNN and qualified excellently via the W-CSA that is the combination of the Crow Search Algorithm (CSA) and Whale optimisation algorithm (WOA). Based on the specificity, accuracy and sensitivity with values 0.982, 0.972 and 0.975, the efficiency of this process deliberates advanced performance than the existing process.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Whale- crow search optimisation enabled deep convolutional neural network for flood detection\",\"authors\":\"M. B. Mulik, J. V., Pandurangarao N. Kulkarni\",\"doi\":\"10.1080/19479832.2023.2186957\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT The satellite images are more attracted in the field of flood detection. For planning actions during emergencies, flood detection plays a vital role, but the major barrier is that using satellite images to detect flooded regions. For flood detection, this method innovates a model named Whale-crow search algorithm on the basis of deep convolutional neural network (W-CSA DCNN) approach. Pre-processing, classification, segmentation and feature extraction are the four steps which is included in this model. For obtaining sound and antiquity from the input image initially, the satellite imagery is given to pre-processing and then for obtaining the features on the basis of vegetation indices the pre-processed image is put through the feature extraction process. By means of Kernel Fuzzy Auto regressive (KFAR) model, the acquire features are subsequently used in the segmentation process. After obtaining the segments, it is given to the classification, which is carried out by means of DCNN and qualified excellently via the W-CSA that is the combination of the Crow Search Algorithm (CSA) and Whale optimisation algorithm (WOA). Based on the specificity, accuracy and sensitivity with values 0.982, 0.972 and 0.975, the efficiency of this process deliberates advanced performance than the existing process.\",\"PeriodicalId\":46012,\"journal\":{\"name\":\"International Journal of Image and Data Fusion\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2023-03-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Image and Data Fusion\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/19479832.2023.2186957\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"REMOTE SENSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Image and Data Fusion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/19479832.2023.2186957","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"REMOTE SENSING","Score":null,"Total":0}
Whale- crow search optimisation enabled deep convolutional neural network for flood detection
ABSTRACT The satellite images are more attracted in the field of flood detection. For planning actions during emergencies, flood detection plays a vital role, but the major barrier is that using satellite images to detect flooded regions. For flood detection, this method innovates a model named Whale-crow search algorithm on the basis of deep convolutional neural network (W-CSA DCNN) approach. Pre-processing, classification, segmentation and feature extraction are the four steps which is included in this model. For obtaining sound and antiquity from the input image initially, the satellite imagery is given to pre-processing and then for obtaining the features on the basis of vegetation indices the pre-processed image is put through the feature extraction process. By means of Kernel Fuzzy Auto regressive (KFAR) model, the acquire features are subsequently used in the segmentation process. After obtaining the segments, it is given to the classification, which is carried out by means of DCNN and qualified excellently via the W-CSA that is the combination of the Crow Search Algorithm (CSA) and Whale optimisation algorithm (WOA). Based on the specificity, accuracy and sensitivity with values 0.982, 0.972 and 0.975, the efficiency of this process deliberates advanced performance than the existing process.
期刊介绍:
International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).