DOI: https://doi.org/10.14710/jtsiskom.2022.14346

Klasifikasi Citra Satelit menggunakan Lightweight Ensemble Convolutional Network

1Dept. of Computer Engineering, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo Surabaya, Indonesia 60111, Indonesia

2Dept. of Computer Engineering, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya, Indonesia 60111, Indonesia

Received: 16 Oct 2021; Revised: 2 Apr 2022; Accepted: 22 Jul 2022; Available online: 31 Jul 2022; Published: 24 Sep 2024.
Open Access Copyright (c) 2024 Reza Fuad Rachmadi, Kentani Langgalih Prioko, Supeno Mardi Susiki Nugroho, I Ketut Eddy Purnama
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
Citra satelit dapat digunakan salah satunya sebagai pengamatan kondisi atmosfer dan permukaan pada bumi. Dengan semakin berkembangnya teknologi citra satelit, waktu untuk pengambilan citra satelit menjadi lebih efisien. Makalah ini melakukan eksperimen menggunakan klasifier ensemble convolutional network untuk melakukan pengenalan kondisi atmosfer pada citra satelit. Empat buah arsitektur Convolutional Neural Network (CNN) digunakan dalam eksperimen ini, yaitu MobileNetV2, ResNet18, ResNet18Half, dan SqueezeNet. Keempat arsitektur CNN tersebut dipilih karena mempunyai jumlah parameter yang tidak terlalu besar (lightweight) serta dapat diterapkan pada banyak perangkat keras tertanam. Eksperimen yang dilakukan dengan menggunakan dataset USTC SmokeRS memperlihatkan bahwa klasifier ensemble memperoleh hasil yang baik dengan akurasi rata-rata tertinggi sebesar 97.06 %.
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Keywords: ensemble CNN, citra satelit, lightweight CNN

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