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Feature Extraction and Image classification
2018
International Journal for Research in Applied Science and Engineering Technology
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The meaningful categorizing of the image pixel values so as to obtain the classification for satellite image which has become most important aspect for Map making and map updating . operations like information ...
Artificial Neural Network is used for the Edge Detection of the satellite image rather than using the conventional methods . ...
The different types of unsupervised classification techniques are K-means clustering algorithm , ISODATA clustering.
D. ...
doi:10.22214/ijraset.2018.6099
fatcat:rfofm2qrvbem3hmu3si4frft64
Remote sensing imagery in vegetation mapping: a review
2008
Journal of Plant Ecology
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Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level. ...
This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover. ...
In general, both of them assign an arbitrary initial cluster vector first. The second step classifies each pixel to the closest cluster. ...
doi:10.1093/jpe/rtm005
fatcat:z22ciuewtzctbdv34w5j5axpye
Detection of Urban and Environmental Changes via Remote Sensing
2021
Circular Economy and Sustainability
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A detailed understanding of the characteristics of land exploitation and land structure is indispensable for the study of their influences on life and nature. ...
Rapid climate and environmental change at limited, regional, and general scales have been a major concern for researchers in a number of fields, such as topography, economy, environment, and sustainable ...
Heuristic unsupervised classification is based on the creation of some mathematical designs and, subsequently, the improvement of a predefined indicator to determine the cluster information and the centroids ...
doi:10.1007/s43615-021-00035-y
pmid:34888554
pmcid:PMC7981390
fatcat:pxp36qdzwfcr5krrfsw64bfcqy
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art
2017
IEEE Geoscience and Remote Sensing Magazine
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Hence, rigorous and innovative methodologies are required for hyperspectral image and signal processing and have become a center of attention for researchers worldwide. ...
However, a huge number of factors, such as high dimensions and size of the hyperspectral data, the lack of training samples, mixed pixels, light scattering mechanisms in the acquisition process, and different ...
In addition, the authors would like to thank the National Center for Airborne Laser Mapping (NCALM) at the University of Houston for providing the CASI Houston data set, and the IEEE GRSS Image Analysis ...
doi:10.1109/mgrs.2017.2762087
fatcat:6ezzye7yyvacbouduqv2f2c7gi
Hyperspectral Image Classification for Land Cover Based on an Improved Interval Type-II Fuzzy C-Means Approach
2018
Sensors
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This paper addresses image classification based on a hyperspectral remote-sensing technique using an improved interval type-II fuzzy c-means (IT2FCM*) approach. ...
clustering accuracy. ...
Hongyuan Huo wrote the manuscript and was responsible for the research design, data preparation, and analysis. ...
doi:10.3390/s18020363
pmid:29373548
pmcid:PMC5856092
fatcat:ugs54wt2a5csxhf4l73jj63io4
Security and Privacy of Cloud- and IoT-Based Medical Image Diagnosis Using Fuzzy Convolutional Neural Network
2021
Computational Intelligence and Neuroscience
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The decrypted images are used for classification of cancer levels with different layers of training. ...
Secondly, a fuzzy convolutional neural network (FCNN) algorithm is proposed for effective classification of images. ...
pixel values in the differential attack. ...
doi:10.1155/2021/6615411
pmid:33790958
pmcid:PMC7997756
fatcat:le6j4aedn5e2fl6dcj2su25iji
Change detection from remotely sensed images: From pixel-based to object-based approaches
2013
ISPRS journal of photogrammetry and remote sensing (Print)
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The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. ...
With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection. Ó ...
Acknowledgement This research is supported by a grant from the Natural Sciences and Engineering Research Council (NSERC) of Canada. ...
doi:10.1016/j.isprsjprs.2013.03.006
fatcat:dl2bpktsibbdxkdzw5dpbaop5i
Using a hybrid fuzzy classifier (HFC) to map typical grassland vegetation in Xilin River Basin, Inner Mongolia, China
2008
International Journal of Remote Sensing
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Acknowledgements The authors wish to thank The Center for Ecological Research, Institute of Botany, Chinese Academy of Sciences (CAS) for the financial support through The One Hundred Scholars -Distinguished ...
The authors are also grateful to the research staff and graduate assistants at CAS -Inner Mongolia Grassland Research Station (IMGERS) who assisted in collecting the field samples for this research. ...
Numerous factors affect the potential success of image classification using satellite images (Salovaara et al. 2005) . ...
doi:10.1080/01431160701408436
fatcat:jikuhjrkyvbwdfr6rnqui64w7u
Machine Vision Identification of Plants
[chapter]
2011
Recent Trends for Enhancing the Diversity and Quality of Soybean Products
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Analytical tools are improving for classifying plant species. The artificial neural network (ANN) has been proposed for many classification activities. ...
Beichel, et al. (1999) discussed the use of an unsupervised Gath-Geva clustering method for Landsat thermatic mapper (TM) images. ...
doi:10.5772/18690
fatcat:znx2wjt75rga5ajhgnc5osapqm
An Object-Oriented Approach to Extracting Productive Fossil Localities from Remotely Sensed Imagery
2015
Remote Sensing
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Author Contributions: Bryan Bommersbach was primarily responsible for the object-based analysis of the high resolution imagery that was used in the 2013 field season. ...
Charles Emerson adjusted the methodology for a basin-wide analysis using Landsat 8 imagery in 2014. ...
Conroy used an unsupervised classification approach [20] and Conroy and colleagues [21] classified Landsat ETM+ imagery using a maximum likelihood approach in the Uinta Basin of Utah. ...
doi:10.3390/rs71215848
fatcat:etzfgruwxbgudk2kbsjbqgnl3q
Different types of Rainfall Prediction Models and Precipitation Analysis Algorithms
2018
International Journal for Research in Applied Science and Engineering Technology
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Rain fall prediction models also useful in agriculture sector. ...
This paper discuss about different types of rainfall prediction models. And also discuss about precipitation analysis algorithms and their results also published in this article. ...
In Unsupervised Classification the computer Program automatically groups the pixels in the image into spectral clusters depending on their spectral Features. ...
doi:10.22214/ijraset.2018.1274
fatcat:zq5tpimzjbaylhfuyydifpwolm
Machine Learning Methods for Forest Image Analysis and Classification: A Survey of the State of the Art
2022
IEEE Access
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The paper is presented in five parts: (1) a review of methods used for forest image detection and classification; (2) challenges faced by object detection methods; (3) analysis of segmentation techniques ...
Ontologies should be used to support the future of remote sensing in forest object classification. ...
RECOMMENDATION Pixel-based techniques have been commonly used for image analysis and classification for a very long time. ...
doi:10.1109/access.2022.3170049
fatcat:k4igwev3bbfyjgcfbxfksynmem
Multi-agent Systems in Remote Sensing Image Analysis
2019
Proceedings of the 11th International Conference on Agents and Artificial Intelligence
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Hence, besides effective strategies for data mining and image data retrieval, reliable and efficient methods of image analysis with a high degree of automation are needed in order to extract the information ...
To increase the degree of automation, the application of multi-agent systems in remote sensing image analysis is recently under research. ...
For analysis purposes each pixel of an image is assigned to a meaningful real-world class, that is, pixels are classified by an arbitrary supervised or unsupervised classification method. ...
doi:10.5220/0007381201780185
dblp:conf/icaart/Hofmann19
fatcat:mofbv7wutjcinn5msin4tn4d7q
Automated Detection and Classification of Cervical Cancer Using Pap Smear Microscopic Images: A Comprehensive Review and Future Perspectives
2022
Engineered Science
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Dedicated image analysis algorithms provide mathematical description of the region of interest which provide a great support to pathologists for decision making. ...
Computerized image analysis methods are primarily of great interest as they provide significant benefits for clinicians with reliable and timely diagnosis of the samples. ...
We also thank Department of Pathology and Institutional Ethics Committee (IEC 472/2013) for providing permission to use the Images for the study. ...
doi:10.30919/es8d633
fatcat:j2kjggmqjrbz3dvy25faw2zki4
Actual Pathogen Detection: Sensors and Algorithms - a Review
2009
Algorithms
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Satellite images have reduced their temporal resolution, but are expensive and must become cheaper for their use world-wide. ...
In the last 30 years, a lot of research has been carried out in nondestructive sensors for food quality. ...
Image classification is defined as the process of creating thematic maps from satellite imagery [124] . Image classification classifies each pixel of an image into land cover categories. ...
doi:10.3390/a2010301
fatcat:rdyjdiuyanetneeiiyqsklumlu
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