RBF Neural Network for Landmine Detection in H Yperspectral Imaging

Makki, Ihab; Younès, Rafic; Khodor, Mahdi; Khoder, Jihan; Francis, Clovis; Bianchi, Tiziano; Rizk, Patrick; Zucchetti, Massimo

doi:10.1109/euvip.2018.8611652

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…Radial Basis Function (RBF) adalah metode jaringan syaraf tiruan yang menggunakan fungsi aktivasi radial basis yang cocok diterapkan pada kasus klasifikasi [14]. Jaringan syaraf tiruan RBF dapat diartikan sebagai jaringan yang berbentuk multilayer perceptron dengan struktur utama yaitu nilai pembobotan, nilai tengah serta jarak antara data yang digunakan sebagai reduksi error pada output jaringan [15]. Jaringan RBF memanfaatkan fungsi aktivasi basis radial atau dikenal dengan Gaussian yang digunakan di dalam hidden layer.…”

Section: Pendahuluanunclassified

“…Fungsi ini bersifat lokal, yakni jika input berdekatan dengan titik pusat maka fungsi tersebut bernilai 1 sebaliknya jika jauh dengan titik pusat maka akan bernilai 0. Algoritma RBF memiliki kemampuan dalam pemelajaran dan pengenalan pola yang relatif cepat, ini disebakan jaringan yang digunakan pengelolaanya dilakukan secara lokal [15]. Jaringan syaraf RBF memiliki 3 (tiga) lapisan yang bersifat feedforward yang memiliki kemampuan dalam melakukan pembelajaran sehingga dapat digunakan dalam menyelesaikan kasuskasus klasifikasi ataupun identifikasi [16].…”

Section: Pendahuluanunclassified

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Klasifikasi Jenis Tanaman Fast Growing Species Menggunakan Algoritma Radial Basis Function Berdasarkan Citra Daun

Nuraini

Harlena

Amalya

et al. 2023

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Indonesia has vast forests, even ranked as the third largest forest in the world. However, currently many forest areas have been deforested or the phenomenon of losing tree cover and forest areas. Forest rehabilitation programs develop by prioritizing plant or tree species that have fast growth or are called fast growing species. However, many people do not know about these fast growing species. Even though knowledge about the types of fast growing plant species is very important for the community to have so that the community can find out which plants can accelerate forest rehabilitation. Fast growing species of plants can actually be identified from the shape of the leaves. This study aims to build a classification model for fast growing species plant images based on leaf images by applying the Radial Basis Function (RBF) artificial neural network algorithm with morphological feature extraction. Morphological feature extraction is used to identify the shape of an object in order to obtain feature values based on predetermined parameters. These features then become input for the RBF artificial neural network to obtain learning patterns. The RBF network has three layers that are feedforward so that it can support solving classification or pattern recognition problems. Based on the results of accuracy testing, an accuracy value of 87.50% was obtained. This means that the Radial Basis Function (RBF) neural network is able to classify fast growing plant species based on leaf images.

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Section: Pendahuluanunclassified

Klasifikasi Jenis Tanaman Fast Growing Species Menggunakan Algoritma Radial Basis Function Berdasarkan Citra Daun

Nuraini

Harlena

Amalya

et al. 2023

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“…Some data collected by fixed-wing plane are available for Africa [14,15] and in Germany [16]. Ground-based hyperspectral data collection of landmines has also been carried out in Lebanon [34][35][36].…”

Section: The Direct Detection Of Explosive Targets and Detection Of Their Secondary Indicatorsmentioning

confidence: 99%

Modeling and Simulation of Very High Spatial Resolution UXOs and Landmines in a Hyperspectral Scene for UAV Survey

Bajić

2021

Remote Sensing

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This paper presents methods for the modeling and simulation of explosive target placement in terrain spectral images (i.e., real hyperspectral 90-channel VNIR data), considering unexploded ordnances, landmines, and improvised explosive devices. The models used for landmine detection operate at sub-pixel levels. The presented research uses very fine spatial resolutions, 0.945 × 0.945 mm for targets and 1.868 × 1.868 cm for the scene, where the number of target pixels ranges from 52 to 116. While previous research has used the mean spectral value of the target, it is omitted in this paper. The model considers the probability of detection and its confidence intervals, which are derived and used in the analysis of the considered explosive targets. The detection results are better when decreased target endmembers are used to match the scene resolution, rather than using endmembers at the full resolution of the target. Unmanned aerial vehicles, as carriers of snapshot hyperspectral cameras, enable flexible target resolution selection and good area coverage.

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