Description of the process of presentation and recognition of forest vegetation objects on multispectral space images

Nazmutdinova, A. I.; Itskov, A.G.; Milich, V. N.

doi:10.1134/s1054661817010084

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…In [23], the authors presented a method of describing and classifying multispectral images that allows recognition of classes of different organisms was presented. The algorithm has been successfully applied to forest plants.…”

Section: Introductionmentioning

confidence: 99%

Multispectral Image Classification Based on the Bat Algorithm

Khaleel

Al-Khalidy

2022

Int. j. electr. comput. eng. syst. (Online)

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There are many traditional classification algorithms used to classify multispectral images, especially those used in remote sensing. But the challenges of using these algorithms for multispectral image classification are that they are slow to implement and have poor classification accuracy. With the development of technologies that mimic nature, many researchers have resorted to using intelligent algorithms instead of traditional algorithms because of their great importance, especially when dealing with large amounts of data. The bat algorithm (BA) is one of the most important of these algorithms. This study aims to verify the possibility of using the BA to classify the multispectral images captured by the Landsat-5 TM satellite image of the study area. The study area represents the Mosul area located in the Nineveh Governorate in northwestern Iraq. The purpose is not only to study the ability of the BA to classify multispectral images but also to obtain a land cover map of this region. The BA showed efficiency in the classification results compared to Maximum Likelihood (ML), where the overall accuracy of classification when using the BA reached (82.136%), while MLreached (79.64%)

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Section: Introductionmentioning

confidence: 99%

Multispectral Image Classification Based on the Bat Algorithm

Khaleel

Al-Khalidy

2022

Int. j. electr. comput. eng. syst. (Online)

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“…A method has been presented to describe and classify multispectral images that allow the recognition of hardly different object classes. The algorithm is successful in its application to forest vegetation objects [7]. Using multispectral sensors, suggested a novel technique to register images taken and presented a new fusion scheme that fuses the images differently for high and low frequencies [8].…”

Section: Introductionmentioning

confidence: 99%

Multi-spectral images classification based on intelligent water drops algorithm

Khaleel

2021

IJEECS

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Mosul's city land covers soil, cultivated land, stony, pastoral land, water, and ploughed agricultural land. We have classified multispectral images captured by the sensor (TM) carried on the Landsat satellite. Integrated approach of intelligent water drops (IWDs) algorithm is used to identify natural terrain. In this research, IWDs have been suggested to find the best results for multispectral image classification. The purpose of using an algorithm, give accurate and fast results by comparing the IWD algorithm with the K-mean algorithm. The IWD algorithm is programmed using the Matlab2017b software environment to demonstrate the proposed methodology's effectiveness. The proposed integrated concept has been applied to satellite images of Mosul city in Iraq. By comparing the IWD with the K-mean, we found clear time superiority of the IWD algorithm, equal 1.4122 with (K-mean) time equal 18.9475. Furthermore, the water drop algorithm's classification accuracy is 95%, while the K-mean classification accuracy is 83.3%. Based on the analysis and results, we conclude the IWD is a robust promising and approach to detecting remote sensing image changes and multispectral image classification.

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Description of the process of presentation and recognition of forest vegetation objects on multispectral space images

Cited by 2 publications

References 2 publications

Multispectral Image Classification Based on the Bat Algorithm

Multispectral Image Classification Based on the Bat Algorithm

Multi-spectral images classification based on intelligent water drops algorithm

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