| Jurnal Angkasa Tahun 2010 Oleh Dwi Nugraheny |
PENERAPAN PRINCIPAL COMPONENT ANALYSIS (PCA) UNTUK MEMBANGUN MODEL DETEKSI AWAN |
| ABSTRAK |
| Principal Component Analysis (PCA) is an algorithm that can classify the principle features of an image. The basic principle of the PCA algorithm is to project an image into the field of its eigenspace by finding the eigenvector of the image. This research aims at building a modelling system that can detect clouds using the PCA algorithm. In the world of aviation, clouds can interfere with aviation safety, especially clouds of type Cumulonimbus (Cb) clouds. These clouds are feared in aviation because they may cause updraft (current ride), downdraft (flow down), and windshear (changes in wind speed). Cloud imagery using PCA the cloud feature extraction phase (training) and detection (recognition) phase. The results show that the PCA algorithm can be used to analyze and detect cloud images. Keywords: Principal Component Analysis (PCA), Cumulonimbus (Cb), eigenspace, eigen vector, detection. |
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