Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal International Journal of Remote Sensing and Earth Sciences (IJReSES) Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
Anis Kamilah Hayati, Anis Kamilah
Unknown Affiliation
Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
CLOUD IDENTIFICATION FROM MULTITEMPORAL LANDSAT-8 USING K-MEANS CLUSTERING Sunarmodo, Wismu; Hayati, Anis Kamilah
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (593.999 KB) | DOI: 10.30536/j.ijreses.2019.v16.a3285

Abstract

In the processing and analysis of remote-sensing data, cloud that interferes with earth-surface data is still a challenge. Many methods have already been developed to identify cloud, and these can be classified into two categories: single-date and multi-date identification. Most of these methods also utilize the thresholding method which itself can be divided into two categories: local thresholding and global thresholding. Local thresholding works locally and is different for each pixel, while global thresholding works similarly for every pixel. To determine the global threshold, two approaches are commonly used: fixed value as threshold and adapted threshold. In this paper, we propose a cloud-identification method with an adapted threshold using K-means clustering. Each related multitemporal pixel is processed using K-means clustering to find the threshold. The threshold is then used to distinguish clouds from non-clouds. By using the L8 Biome cloud-cover assessment as a reference, the proposed method results in Kappa coefficient of above 0.9. Furthermore, the proposed method has lower levels of false negatives and omission errors than the FMask method.
IDENTIFIKASI AWAN PADA DATA TIME SERIES MULTITEMPORAL MENGGUNAKAN PERBANDINGAN DATA SEKUENSIAL Hayati, Anis Kamilah; Sunarmodo, Wismu
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.v18i1.3363

Abstract

Cloud identification is an important pre-processing step of remote sensing data.Generally, cloud identifications could be classified into single-date and multi-date methods. Furthermore, the single-date method could be divided into physical-rules-based and machine-learning-based. Physical-rules-based method generally need data with sufficient spectral resolution while machine-learning-based method depend on training dataset. While the multi-date method usually using clear data as a reference. The clear data itself could be a whole scene or built from many scenes. Processing cloud-free data is a challenge in areas with high cloud coverage such as Indonesia. In this paper, a cloud identification method using multi-date time series scenes is proposed. This method only uses RGB channels which are common in remote sensing visual data. In addition, this method does not require or process cloud-free data mosaics in advance. A pixel value from an examined scene is compared to other pixel values from other scenes in the same position. The other scenes are the scenes that were acquired before and after the examined scene. The value differences between the examined pixel and it's before and after then evaluated using some thresholds to determine whether the pixel is a cloud or not. Assessment is done by using L8 Biome as a reference. The result shows that using some thresholds in our proposed method has a Kappa coefficient higher than 0.9.
Co-Authors Wismu Sunarmodo, Wismu