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Sritarapipat T.,Geo Informatics and Space Technology Development Agency Public Organization | Rakwatin P.,Geo Informatics and Space Technology Development Agency Public Organization | Kasetkasem T.,Kasetsart University
Sensors (Basel, Switzerland) | Year: 2014

Rice crop height is an important agronomic trait linked to plant type and yield potential. This research developed an automatic image processing technique to detect rice crop height based on images taken by a digital camera attached to a field server. The camera acquires rice paddy images daily at a consistent time of day. The images include the rice plants and a marker bar used to provide a height reference. The rice crop height can be indirectly measured from the images by measuring the height of the marker bar compared to the height of the initial marker bar. Four digital image processing steps are employed to automatically measure the rice crop height: band selection, filtering, thresholding, and height measurement. Band selection is used to remove redundant features. Filtering extracts significant features of the marker bar. The thresholding method is applied to separate objects and boundaries of the marker bar versus other areas. The marker bar is detected and compared with the initial marker bar to measure the rice crop height. Our experiment used a field server with a digital camera to continuously monitor a rice field located in Suphanburi Province, Thailand. The experimental results show that the proposed method measures rice crop height effectively, with no human intervention required.

Chaimatanan S.,Geo Informatics and Space Technology Development Agency Public Organization | Vongsantivanich W.,Geo Informatics and Space Technology Development Agency Public Organization
ACRS 2015 - 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, Proceedings | Year: 2015

Mission planning plays a crucial role for space-based remote sensing, as it is one of the key processes that defines the success rate of each daily mission. From user's request, the Earth observation satellite mission-planner concerns with scheduling the acquisition plan that will be used to prepare the satellite command queue to perform the image acquisitions. On a long-term basis, the mission-plan should optimize a set of pre-defined goals, while on a daily basis the mission plan must be adapted to the observation condition. This paper presents a mission-planning tool that is being developed for Thaichote Earth observation satellite to improve and optimize Gistda's space-based remote sensing resources. It is designed to take into account the problems from a real operation point of view, where rescheduling is necessary due to the change of observation conditions or the requirements for acquisition modification, while the changes to the long-term goal scheduling should be minimized. The tool allows the mission-planner to manage the utilization of the satellite in four different time levels; long-term, medium-term, short-term, and post-mission. It consists of four main modules, which are user interface, feasibility assessment, planning optimization, and data management modules. It was implemented and tested with real input data based on Thaichote operation. The perspective to use this tool for satellite constellation and regional area acquisition is also discussed at the end of this paper.

Tangpattanakul P.,Geo Informatics and Space Technology Development Agency Public Organization
2015 4th International Conference on Informatics, Electronics and Vision, ICIEV 2015 | Year: 2015

This paper presents two algorithms, which are a nondominated sorting genetic algorithm II (NSGA-II) and an indicator-based multi-objective local search (IBMOLS), for solving a bi-objective p-Median problem. The bi-objective p-Median problem is a problem of finding p location points to install facilities from a set of m candidates. This problem considers two objectives: minimizing the sum of the distances from each customer to the nearest facility and minimizing the sum of the costs to install each facility in the selected location points. NSGA-II and IBMOLS are efficient algorithms in the area of multi-objective optimization. Experiments are conducted on generated instances. Hypervolume values of the approximate Pareto fronts are computed and the obtained results from IBMOLS and NSGA-II are compared. © 2015 IEEE.

Srestasathiern P.,Geo Informatics and Space Technology Development Agency Public Organization | Rakwatin P.,Geo Informatics and Space Technology Development Agency Public Organization
Remote Sensing | Year: 2014

Oil palm tree is an important cash crop in Thailand. To maximize the productivity from planting, oil palm plantation managers need to know the number of oil palm trees in the plantation area. In order to obtain this information, an approach for palm tree detection using high resolution satellite images is proposed. This approach makes it possible to count the number of oil palm trees in a plantation. The process begins with the selection of the vegetation index having the highest discriminating power between oil palm trees and background. The index having highest discriminating power is then used as the primary feature for palm tree detection. We hypothesize that oil palm trees are located at the local peak within the oil palm area. To enhance the separability between oil palm tree crowns and background, the rank transformation is applied to the index image. The local peak on the enhanced index image is then detected by using the non-maximal suppression algorithm. Since both rank transformation and non-maximal suppression are window based, semi-variogram analysis is used to determine the appropriate window size. The performance of the proposed method was tested on high resolution satellite images. In general, our approach uses produced very accurate results, e.g., about 90 percent detection rate when compared with manual labeling. © 2014 by the authors.

Koedkurang K.,Beihang University | Koedkurang K.,Geo Informatics and Space Technology Development Agency Public Organization | Cao X.,Beihang University
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2015

Coastal bathymetry data are great important environmental resources and disaster management. Today, there are many technologies used to explore the bathymetry, depending on the purpose of surveying. But almost technologies can be time consuming, complicated, and quite expensive. Remote sensing by satellite imagery is one of technology used to estimate the coastal bathymetry in term of accuracy, quality and up to datedness, timely availability and cost effectiveness. Especially the Coastal Blue Band of WorldView-2 for bathymetric measurements will improve both in depth and accuracy. From investigated the relation between surface reflectance and coastal bathymetry of the eight bands of WorldView-2 that band 1,2,3 and 4 are good reflection and when the depth are increase, the digital number (DN) will decrease. The objective of this research is to developing the classification of the coastal bathymetry estimation from satellite imagery. By utilized WorldView-2 satellite images along with regressive function and improving the accuracy result by comparing with in-situ truth depth to assess a coastal bathymetry. © 2015 SPIE.

Tangpattanakul P.,Geo Informatics and Space Technology Development Agency Public Organization | Chaimatanan S.,Geo Informatics and Space Technology Development Agency Public Organization
ACRS 2015 - 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, Proceedings | Year: 2015

This work proposes a biased random key genetic algorithm (BRKGA) for solving a multiple priorities task scheduling problem. Generally, some chromosomes are initially generated by encoding as key vectors of real value between [0, 1] in BRKGA process. To obtain the solution, the chromosome has to be decoded. The multiple priorities task scheduling problem, which needs to schedule strips of Earth observation before sending the sequence to the satellite, is considered in this work. Strips are required from customers with different priorities and each strip has its own priority. The objective of this scheduling problem is to minimize the finishing date for taking all strips. For encoding, each gene in the chromosome represents each strip. The gene value of each strip is a real number in different intervals depending on the priority of related strip. Let si be a strip with priority P[i], where i ϵ {1, 2,⋯, n} and P[i] € {1, 2,⋯, np}, n is number of strips and np is number of different priorities. Thus, gene values of strip si with priority P[i] is a real value in the interval [(P[i]-1)/np, P[i]/np). For decoding, the sequence of strip acquisition can be obtained from the represented gene values. The strip with the lowest gene value is considered firstly. It is assigned in the sequence of strip acquisition as early as possible and it must also satisfy the constraints. Then, the strip with the next higher gene value is considered and it can be inserted to the available spaces of the sequence that satisfy the constraints. Moreover, the considered strip is assigned in the sequence as early as possible. Experiments are conducted on a realistic instance, which concerns strips with two different priorities. All strips are assigned in the sequence and then, this sequence will be sent to the Earth observing satellite.

Jitkajornwanich K.,Geo Informatics and Space Technology Development Agency Public Organization | Elmasri R.,University of Texas at Arlington
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Large amounts of "big data" are generated every day, many in a "raw" format that is difficult to analyze and mine. This data contains potential hidden meaningful concepts, but much of the data is superfluous and not of interest to the domain experts. Thus, dealing with big raw data solely by applying a set of distributed computing technologies (e.g., MapReduce, BSP [Bulk Synchronous Parallel], and Spark) and/or distributed storage systems, namely NoSQL, is generally not sufficient. Extracting the full knowledge that is hidden in the raw data is necessary to efficiently enable analysis and mining. The data needs to be processed to remove the superfluous parts and generate the meaningful domainspecific concepts. In this paper, we propose a framework that incorporates conceptual modeling and EER principle to effectively extract conceptual knowl‐ edge from the raw data so that mining and analysis can be applied to the extracted conceptual data. © Springer International Publishing Switzerland 2015.

Nilnarong T.,Geo Informatics and Space Technology Development Agency Public Organization | Tangpattanakul P.,Geo Informatics and Space Technology Development Agency Public Organization
Proceedings - IEEE COMNETSAT 2014: 2014 IEEE International Conference on Communication, Network and Satellite | Year: 2014

In order to achieve the next level services of earth observation satellite data, the web-based tool for satellite mission planning is developed. The objectives are joint-usage spare resources of earth observation satellite amongst ASEAN, customer-self mission planning and multi-objective optimization for mission plan. The tools is developed in three segments; webbased application for world-wide service, space mechanics and Earth model for the computation of feasible accessibility and multi-objective optimization for effective mission planning. The project started with the web-based development in order to make the tool available as soon as possible. The projection of satellite resulting from the computation in the later phase is now temporarily compensated by the database of satellite position created by STK (Satellite Tool Kit). At the moment, the tool is functional without neither orbit propagator nor earth model. For further enhancement, space mechanics and earth models as well as algorithm for mission plan optimization will be taken into account very soon in order to relieve the interaction between human and tool, make it self-sufficient for operation and increase mission planning efficiency. © 2014 IEEE.

Khobkhun B.,King Mongkut's University of Technology Bangkok | Prayote A.,King Mongkut's University of Technology Bangkok | Rakwatin P.,Geo Informatics and Space Technology Development Agency Public organization | Dejdumrong N.,King Mongkut's University of Technology Thonburi
Proceedings - 10th International Conference Computer Graphics, Imaging, and Visualization, CGIV 2013 | Year: 2013

This paper presents the method to determine rice cropping pattern in Thailand for future prediction of water supply demand, pricing, and other related issues including governmental policies. Datasets was obtained from an orbital instrument called a Moderate-Resolution Imaging Spectroradiometer (MODIS) operated by NASA. A Normalized Difference Vegetation Index (NDVI) was derived from MODIS datasets once every 16 days. This image data has been analyzed using image processing techniques in order to determine rice cropping area in Thailand. Rice cropping data is represented as a time series displaying type of rice crop in which peak data points indicate rice cropping cycle in each year. A Progressive Iterative Approximation (PIA) is used for signal smoothing and reducing noise by providing a Bézier curve representation of time-series data. The experimental results show that using PIA technique for noise reduction yields better results comparing with a common filtering method like Savitzky Golay filter. © 2013 IEEE.

Tawala J.,Geo Informatics and Space Technology Development Agency Public Organization
34th Asian Conference on Remote Sensing 2013, ACRS 2013 | Year: 2013

Car robbery is one the serious problems in a large city like Bangkok, the capital of Thailand, and statistics showed that one or two vehicles were stolen every day. The objective of this study is to identify risk area for car robbery in Lat Phrao district of Bangkok, The excel file of 5-year period car stolen records collected during 2005 - 2009 by Lat Phrao police station were converted into GIS format, to identify and describe spatial and temporal distribution of the incident. Next, the data were spatially interpolated using various algorithms, and the outputs were compared to select the most appropriate output. The result showed that the kernel density estimation described spatial distribution of the robbery very well, and Bang Kapi and Happy Land sub districts had the highest risk for car robbery. To make the output more understandable for general users, the kernel density estimation was overlaid on high resolution satellite imageries in Google Earth. The results provide a basis for further study on risk areas for the car robbery, analysis of factors leading to the risk, and help law enforcement administrators in making better decisions.

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