Improvement of segmentation quality of multispectral images by increasing resolution

Hryvachevskyi, Andrii; Prudyus, Ivan; Lazko, Leonid; Fabirovskyy, Sergiy

doi:10.1109/cpee.2016.7738750

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…It consists of two rounding pixel values, based on a specific threshold, this is: 0 -corresponding to black and 1 -corresponding to white colour. The next step of the algorithm is the image segmentation process [5,20]. As a result, the fragments separated from the whole image are created, characterized by the uniformity in relation to colour, texture, etc.…”

Section: Figure 1 General Scheme For Detecting Objects In Imagesmentioning

confidence: 99%

Recognition of alphanumeric characters using artificial neuron networks and MSER algorithm

Matuszewski

Zając

2020

Radioelectronic Systems Conference 2019

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The article presents a method of recognizing alphanumeric characters located in the image, based on a previously created database of patterns using neural networks. For this purpose the convolutional networks were used, which independently search for features that allow to distinguish characters in the image. A larger number of convolution layers allows us to recognize a greater number of features and thus to increase the probability of correctly recognized characters. The main purpose of the paper is to present software that recognizes the alphanumeric characters in images and to investigate the impact of the size of this database on the program's speed and character recognition efficiency. This software can also be used in more complex structures, such as automatic translators or as a computer reader. The calculation of the first program that recognizes single character and the second program that reads all the text from the image have been made in the MATLAB environment. The paper describes the components of this software, such as the learning subsystem and the character recognition subsystem. The results of the program were presented in the form of screenshots showing the results of the learning process and character recognition process. The speed of the software and the effectiveness of recognizing alphanumeric characters using the artificial neural networks and maximally stable extremal regions (MSER) algorithm are presented in the table and figures. Attention was also paid to the impact of the size of the database used to learn the network on the speed of calculations and recognition efficiency.

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Section: Figure 1 General Scheme For Detecting Objects In Imagesmentioning

confidence: 99%

Recognition of alphanumeric characters using artificial neuron networks and MSER algorithm

Matuszewski

Zając

2020

Radioelectronic Systems Conference 2019

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“…The scanning process can be performed to get the acquired images of an area with different frequencies, which frequencies give different information based on the applications. Generally, there are 4 types of resolutions available for multispectral or hyper spectral resolutions such as spatial, spectral, temporal and radiometric resolutions [6,8]. The MS images used in various applications and the multiple bands increase the utilization of satellites [2].…”

Section: Introductionmentioning

confidence: 99%

Speckle Noise Detection and Removing by Machine Learning Algorithms in Multisensory Images for 5G Transmission

Dharani,

Nagendranath,

Rafee

et al. 2024

SCPE

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The multispectral satellite sensor images have multibands, which have some typical noise. There is difficult to detect this tipical noise with low resolution image. The satellite local or gloval pixel information and quantificationare degraded due to this noise. Many standard transformations and filtering operations are developed for detection and removing of non-gaussion noise, which are not given sophisticated results with existing methods. These statistical characteristics are applied to those samples to identify and quantify present tipical noise. The higher-order statistical based machine learning algorithm is developing to remove the speckle noise from satellite image. In this proposed algorithm, implemented the higher order statistical approache such as skewness, kurtosis based adaptive curvelet methods are implemented for the detection and suppression of speckle noise with retrieve of spectral and spectral values. The proposed algorithm preserves smooth and sharp details and maintains the tradeoff level in multispectral bands is suitable for advanced high speed 5G communication with the effective rate of transmission. The proposed results are verified with suitable statistical parameters such as PSNR, Entropy and ERGAS values.

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“…Some methods are done to increase the resolution, such as direct image synthesis, weighted average method, HIS transformation, principal component analysis, arithmetic combination based on Brovey transform, Gram-Schmidt transformation, and wavelet transform. The best improvement result was shown using Brovey transform for the segmentation remote sensing image [13].…”

Section: Introductionmentioning

confidence: 99%

Mixed Pixel Classification on Hyperspectral Image Using Imbalanced Learning and Hyperparameter Tuning Methods

Purwadi,

Abu,

Mohd

et al. 2023

JOIV : Int. J. Inform. Visualization

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Hyperspectral image technology in land classification is a distinct advantage compared to ordinary RGB and multispectral images. This technology has a wide spectrum of electromagnetic waves, which can be more detailed than other types of imagery. Therefore, with its hyperspectral advantages, the characteristics of an object should have a high probability of being recognized and distinguished. However, because of the large data, it becomes a challenge to lighten the computational burden. Hyperspectral has a huge phenomenon that makes computations heavy compared to other types of images because this image is 3D. The problem faced in hyperspectral image classification is the high computational load, especially if the spatial resolution of the image also has mixed pixel problems. This research uses EO-1 satellite imagery with a spatial resolution of 30 meters and a mixed pixel problem. This study uses a classification method to lighten the computational burden and simultaneously increase the value of classification accuracy. The method used is satellite image pre-processing, including geometric correction and image enhancement using FLAASH while the corrections are geometric correction and atmospheric correction. Then to lighten the computational burden, the steps carried out are using the Slab and PCA method. After obtaining the characteristics, they are entered into a guided learning model using a support vector machine (SVM) for the five-class or multiclass classification. Moreover, the imbalance learning method is proven to produce increased accuracy. The best results were achieved by the ADASYN method with an accuracy of 96.58%, while the computational time became faster with the feature extraction method.

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Improvement of segmentation quality of multispectral images by increasing resolution

Cited by 4 publications

References 4 publications

Recognition of alphanumeric characters using artificial neuron networks and MSER algorithm

Recognition of alphanumeric characters using artificial neuron networks and MSER algorithm

Speckle Noise Detection and Removing by Machine Learning Algorithms in Multisensory Images for 5G Transmission

Mixed Pixel Classification on Hyperspectral Image Using Imbalanced Learning and Hyperparameter Tuning Methods

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