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All Journal Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
Rokhmatullah, Rokhmatullah
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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.
KLASIFIKASI PENUTUP LAHAN MENGGUNAKAN DATA LIDAR DENGAN PENDEKATAN MACHINE LEARNING Hariyono, Mochamad Irwan; Dewi, Ratna Sari; Rokhmatullah, Rokhmatullah; Tambunanan, Mangapul P
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.v18i1.3365

Abstract

Lidar is a remote sensing technology. Lidar data is widely used and has been developed for mapping, detailed spatial planning, and natural disaster analysis. In its development for Lidar data management, software applications are widely used as well as by using built algorithms such as machine learning. The research aims to utilize Lidar data for land cover classification using machine learning, namely Support Vector Machine (SVM). The research location is Tanjung Karang village, Mataram City, Lombok. The classification applied is a supervised classification in which the training data is needed to perform the classification. The predicted land cover class in this study is limited to buildings, vegetation, roads, open land. The data used for classification is derived from Lidar, namely DTM, DSM, nDSM, and Intensity. The classification scheme used is one data input and a combination of data. The reference data used is a topographic map (Topographic map of Indonesia). The results showed that the classification with a data combination scheme had a better accuracy value than the one data classification scheme, which increased accuracy by about 15-20%. This shows that there are complementary factors between the data to be able to identify objects in the classification process.
Co-Authors Andy Indradjad, Andy Ayom Widipaminto, Ayom Hariyono, Mochamad Irwan Monica, Donna Ratna Sari Dewi Safitri, Yuvita Dian Tambunanan, Mangapul P Wismu Sunarmodo, Wismu