In:
Jurnal Ilmu Komputer dan Informasi, Faculty of Computer Science, Universitas Indonesia, Vol. 12, No. 2 ( 2019-07-08), p. 103-
Abstract:
Various methods are available to perform feature extraction on satellite images. Among the available alternatives, deep convolutional neural network (ConvNet) is the state of the art method. Although previous studies have reported successful attempts on developing and implementing ConvNet on remote sensing application, several issues are not well explored, such as the use of depthwise convolution, final pooling layer size, and comparison between grayscale and RGB settings. The objective of this study is to perform analysis to address these issues. Two feature learning algorithms were proposed, namely ConvNet as the current state of the art for satellite image classification and Gray Level Co-occurence Matrix (GLCM) which represents a classic unsupervised feature extraction method. The experiment demonstrated consistent result with previous studies that ConvNet is superior in most cases compared to GLCM, especially with 3x3xn final pooling. The performance of the learning algorithms are much higher on features from RGB channels, except for ConvNet with relatively small number of features.
Type of Medium:
Online Resource
ISSN:
2502-9274
,
2088-7051
DOI:
10.21609/jiki.v12i2.752
Language:
Unknown
Publisher:
Faculty of Computer Science, Universitas Indonesia
Publication Date:
2019
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