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All Journal Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
Ayom Widipaminto, Ayom
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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ANALISIS METODE KOMPRESI BERDOMAIN WAVELET PADA CITRA SATELIT RESOLUSI SANGAT TINGGI Widipaminto, Ayom; Indradjad, Andy; Monica, Donna; Rokhmatullah, Rokhmatullah
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 16 No. 1 (2019)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v16i1.3348

Abstract

A problem that often arises in remote sensing images, especially very high-resolution images, is the large storage and bandwidth needed to transmit those images. On satellite images processing, a compression needs to be done on those satellites images to make it easier in terms of transmission and storage. This paper compare several wavelet-domain methods namely wavelet method, bandelet method, and CCSDS to find the best method to compress the very high-resolution satellites imageries Pleiades. Experiment results show that the method wavelet and bandelet is better in preserving the images quality with around 50 dB PSNR, while CCSDS is better in reducting the image size to the eighth of original image.
Pengembangan Tiling database untuk Penyimpanan Data Penginderaan Jauh pada Pembangunan LAPAN Engine Widipaminto, Ayom; Safitri, Yuvita Dian; Sunarmodo, Wismu; Rokhmatullah, Rokhmatullah
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 16 No. 1 (2019)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v16i1.3350

Abstract

Remote sensing image data is included in the unstructured data category which is characterized by large volumes of data and is regularly updated. Special techniques are needed in large capacity data storage and supported by high-capacity data processing machines. This study aims to find a design representation of remote sensing image data that is more efficient in storage and processing than conventional methods. The design proposed is with the concept of tiling databases, namely the method of breaking down image data into small size pieces with certain identities and then entering them into a database. The test results compared to the conventional method found that the storage volume can be reduced by up to 25%, the speed of reading the data also increases by about 21%. This system can support the development of LAPAN Engine because it offers a storage strategy that is more effective in terms of volume, and efficient in terms of the speed of reading data even though the tiling process into the database takes pretty long time.
PERANCANGAN SISTEM MONITORING CLOUD COVER UNTUK PEMANTAUAN DAN PREDIKSI CLOUD COVER MENGGUNAKAN METODE DATABASE MANAGEMENT SYSTEM DAN LONG SHORT-TERM MEMORY Hestrio, Yohanes Fridolin; Pradono, Kuncoro Adi; Widipaminto, Ayom
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 18 No. 1 (2021)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v18i1.3366

Abstract

The quality of optical satellite image data obtained by the Center for Remote Sensing Data and Technology is affected by weather conditions and cloud cover. Based on these conditions, the satellite image data obtained are divided into three categories including very cloudy, cloudy, and cloud-free. Based on annual data information, it is found that the amount of cloudy satellite image data is three times greater than the amount of cloud-free satellite imagery data. So we need a system that can monitor the percentage of the extent of cloud cover from the acquisition of satellite image data. In addition, it is hoped that the creation of a system that can predict cloud cover, where the results of this cloud cover prediction can be used as a reference at the time of the next satellite image acquisition. . Through research and development of this cloud cover monitoring system, both the user and the acquisition officer can monitor the cloud cover of the acquisition result and also determine the location of cloud-free image data acquisition with predictive data. The method used for the development of the monitoring system uses a DBMS (Database Management System), while predictive research on cloud cover in an area wear the LSTM (Long short-term memory) method for Time Series Forecasting. The results of this research and development are in the form of a monitoring system that can monitor the results of acquisitions with data management principles and predict cloud cover conditions from cloud cover monitoring data.
Co-Authors Andy Indradjad, Andy Hestrio, Yohanes Fridolin Monica, Donna Pradono, Kuncoro Adi Rokhmatullah, Rokhmatullah Safitri, Yuvita Dian Wismu Sunarmodo, Wismu