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All Journal The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Katmoko Ari Sambodo
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Earth & Planetary Sciences

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SEMI-AUTOMATIC SHIP DETECTION USING PI-SAR-L2 DATA BASED ON RAPID FEATURE DETECTION APPROACH Katmoko Ari Sambodo
International Journal of Remote Sensing and Earth Sciences Vol. 9 No. 2 (2012)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2012.v9.a1832

Abstract

Synthetic Aperture Radar (SAR) satellite an active sensor offering unique high spatial resolution regardless of weather conditions can operate both day and night time with wide area coverage. Therefore, SAR satellite can be used for monitoring ship on sea surface. This study showed on an alternative method for ship detection of SAR data using Pi-SAR-L2 (L-band, JAXA-Airborne SAR) data. The ship detection method is this study was consisted of eight main stages. After the Pi-SAR data was registered and speckle was filtered, then the land was masked using SRTM-DEM (Shuttle Radar Topography Mission-Digital Elevation Model) data since most ship detectors produced false detections when it applied to land areas. A ship sample image was then selected (cropped). The next step was to detect some unique keypoints of ship sample image using Speeded Up Robust Features (SURF) detector. The maximum distance (‘MaxDist’) of keypoints was also calculated. The same detector was then applied to whole Pi-SAR imagery to detect all possible keypoints. Then, for each detected keypoint, we calculated distance to other keypoint (‘Dist’). If ‘Dist’ was smaller than ‘MaxDist’, then we marked these two (or more) keypoints as neighboring keypoints. If the number of neighbor keypoints was equal or greater than two, finally we marked these keypoints as ‘Detected Ship’ (draw rectangle and show its geographic position). Results showed that our method can detect successfully 32 ‘possible ships’ from Pi-SAR-L2 data acquired on the area of North Sulawesi, Indonesia (August 8, 2012).
LAND COVER CLASSIFICATION OF ALOS PALSAR DATA USING SUPPORT VECTOR MACHINE Katmoko Ari Sambodo; Novie Indriasari
International Journal of Remote Sensing and Earth Sciences Vol. 10 No. 1 (2013)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2013.v10.a1836

Abstract

Land cover classification is one of the extensive used applications in the field of remote sensing. Recently, Synthetic Aperture Radar (SAR) data has become an increasing popular data source because its capability to penetrate through clouds, haze, and smoke. This study showed on an alternative method for land cover classification of ALOS-PALSAR data using Support Vector Machine (SVM) classifier. SVM discriminates two classes by fitting an optimal separating hyperplane to the training data in a multidimensional feature space, by using only the closest training samples. In order to minimize the presence of outliers in the training samples and to increase inter-class separabilities, prior to classification, a training sample selection and evaluation technique by identifying its position in a horizontal vertical–vertical horizontal polarization (HV-HH) feature space was applied. The effectiveness of our method was demonstrated using ALOS PALSAR data (25 m mosaic, dual polarization) acquired in Jambi and South Sumatra, Indonesia. There were nine different classes discriminated: forest, rubber plantation, mangrove & shrubs with trees, oilpalm & coconut, shrubs, cropland, bare soil, settlement, and water. Overall accuracy of 87.79% was obtained, with producer’s accuracies for forest, rubber plantation, mangrove & shrubs with trees, cropland, and water class were greater than 92%.
RANDOM FOREST CLASSIFICATION OF JAMBI AND SOUTH SUMATERA USING ALOS PALSAR DATA Mulia Inda Rahayu; Katmoko Ari Sambodo
International Journal of Remote Sensing and Earth Sciences Vol. 10 No. 2 (2013)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2013.v10.a1852

Abstract

Recently, Synthetic Aperture Radar (SAR) satellite imaging has become an increasing popular data source especially for land cover mapping because its sensor can penetrate clouds, haze, and smoke which a serious problem for optical satellite sensor observations in the tropical areas. The objective of this study was to determine an alternative method for land cover classification of ALOSPALSAR data using Random Forest (RF) classifier. RF is a combination (ensemble) of tree predictors that each tree predictor depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. In this paper, the performance of the RF classifier for land cover classification of a complex area was explored using ALOS PALSAR data (25m mosaic, dual polarization) in the area of Jambi and South Sumatra, Indonesia. Overall accuracy of this method was 88.93%, with producer’s accuracies for forest, rubber, mangrove & shrubs with trees, cropland, and water classes were greater than 92%.
POLARIMETRIC-SAR CLASSIFICATION USING FUZZY MAXIMUM LIKEHOOD ESTIMATION CLUSTERING WITH CONSIDERATION OF COMPLEMENTARY INFORMATION BASED ON PHYSICAL POLARIMETRIC PARAMETERS, TARGET SCATTERING CHARACTERISTIK, AND SPATIAL CONTEXT KATMOKO ARI SAMBODO; ANIATI MURNl; RATIH DEWANTI; MAHDI KARTASASMITA
International Journal of Remote Sensing and Earth Sciences Vol. 5 (2008)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2008.v5.a1225

Abstract

This paper shows a study on an alternative method for unsupervised classification of polarimetric-Syenthetic Aperture Radar (SAR) data. The first step was to extract several main physical polarimetric parameters (polarization power, coherence, and phase difference) from polarimetric covariance matrix (or coherency matrix) and physical scattering characteristics of land use/cover based on polarimetric decomposition (Cloude decomposition model). In this paper, we found that these features have complementary information which can be integrated in order to improve the discrimination of different land use or cover types. Classification stage was performed using Fuzzy Maximum Likelihood Estimation (FMLE) clustering algorithm. FMLE algorithm allows for ellipsoidal clusters of arbitrary extent and is consequently more flexible than standard Fuzzy K-Means clustering algorithm. Hoever, basic FMLE algorithm makes use exclusively the spectral (or intensity) properties of the individual pixel vectors and spatial-contextual information of the image was not taken into account. Hence, poor(noisy) classification result is ussualy obtained from SAR data due to speckle noise. In this paper, we propose a modified FMLE which integrate basic FMLE clustering with spatial-contextual information by statistical analysis of local neightbourhoods. The effectiveness of the proposed method was demonstrated using E-SAR polarimetric data acquired on the area of Penajam, East Kalimantan, Indonesia. Result showed classified images improving land-cover discrimination performance. Exhibiting homogeneous region, and preserving edge and other fine structures.
CLASSIFICATION OF POLARIMETRIC-SAR DATA WITH NEURAL NETWORK USING COMBINED FEATURES EXTRACTED FROM SCATTERING MODELS AND TEXTURE ANALYSIS Katmoko Ari Sambodo; Aniati Murni; Mahdi Kartasasmita
International Journal of Remote Sensing and Earth Sciences Vol. 4 (2007)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2007.v4.a1212

Abstract

This paper shows a study on an alternative method for classification of polarimetric-SAR data. The method is designed by integrating the comined features extracted from two scattering models(i.e., freeman decomposition model and cloud decomposition model) and textural analysis with distribution-free neural network classifier. The neural network classifier (wich is based on a feedforward back-propagation neural network architecture) properly exploits the information in the combined features for providing high accuracy classification result. The effectiveness of the proposed method is demonstrated using E-SAR polarimetric data acquired on the area of Penajam, East Kalimantan, Indonesia.
Co-Authors Aniati Murni ANIATI MURNl Mahdi Kartasasmita Mulia Inda Rahayu Novie Indriasari Ratih Dewanti