Preesan Rakwatin

Restoration of Aqua MODIS Band 6 Using Histogram Matching and Local Least Squares Fitting

The MODerate resolution Imaging Spectrometer (MODIS) aboard Terra and Aqua platforms is performing well overall, except for Aqua MODIS band 6. Fifteen of the 20 detectors in Aqua MODIS band 6 are nonfunctional or noisy. Without correction, it will cause problems in the higher MODIS products. This paper develops a restoration algorithm to restore the missing data of Aqua MODIS band 6 by combining a histogram-matching algorithm with local least squares fitting. Histogram matching corrects detector-to-detector striping of the functional detectors. Local least squares fitting restores the missing data of the nonfunctional detector based on a cubic polynomial derived from the relationship between Aqua MODIS bands 6 and 7. The Aqua MODIS image data used in this research are in digital number format and are not georectified. The proposed restoring algorithm can be used on both 1000- and 500-m pixel resolutions. The algorithm was tested on both Terra and Aqua MODIS images. For Terra MODIS images, results of restoring the synthetic nonfunctional detectors of band 6 demonstrate that local least squares fitting can fill in the missing data with little distortion. For Aqua MODIS images, the results of the restoring algorithm with and without applying histogram matching were compared to evaluate the capability in removing detector-to-detector stripe noise. To evaluate the performance of the proposed method, quantitative and qualitative analyses were carried out by visual inspection and quality index. For all the scenes used in this research, the correlation coefficients were near 0.99 and root mean square error between the original Terra band 6 and its simulated one was 2times10-5. The proposed algorithm can thus be used satisfactorily for restoring Aqua MODIS band 6.

Assessment of ALOS PALSAR 50 m Orthorectified FBD Data for Regional Land Cover Classification by Support Vector Machines

From its launch in 2006, the phased array L-band synthetic aperture radar (PALSAR) onboard the advanced land observing satellite (ALOS) has acquired many dual-polarized (FBD) images with a 70-km swath width, aiming to produce spatially consistent coverage over tropical rainforest. This paper investigates the relevancy of PALSAR orthorectified FBD product at 50-m resolution for regional land cover classification by the support vector machines (SVM). Our test site is the Riau province, Sumatra island, Indonesia, known to hold vast area of natural peatland forest with an extreme biodiversity threatened by industrial deforestation. Since it is demonstrated the radiometric information (HH and HV channels) cannot be solely used to achieve a good classification, the spatial information in these orthorectified data is investigated. A new tool using the recursive feature elimination SVM-based process and the textural Haralicks parameters is introduced. The real contribution of textures within the land cover classification can be understood. A small set of textural parameters is determined at local scale while being optimal for the land cover discrimination. The SVM-based classifier is carried out across the whole Riau province and its results are compared with a Landsat-based estimation. The agreement is over 70% with six classes and 86% for the natural forest map. These results are remarkable since only one PALSAR FBD product is used and this assessment is performed on more than 40 million pixels. The results confirm the high potential of the PALSAR sensor for forest monitoring at regional, if not global scale.

Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project

Rice is the most important food security crop in Asia. Information on its seasonal extent forms part of the national accounting of many Asian countries. Synthetic Aperture Radar (SAR) imagery is highly suitable for detecting lowland rice, especially in tropical and subtropical regions, where pervasive cloud cover in the rainy seasons precludes the use of optical imagery. Here, we present a simple, robust, rule-based classification for mapping rice area with regularly acquired, multi-temporal, X-band, HH-polarized SAR imagery and site-specific parameters for classification. The rules for rice detection are based on the well-studied temporal signature of rice from SAR backscatter and its relationship with crop stages. We also present a procedure for estimating the parameters based on “temporal feature descriptors” that concisely characterize the key information in the rice signatures in monitored field locations within each site. We demonstrate the robustness of the approach on a very large dataset. A total of 127 images across 13 footprints in six countries in Asia were obtained between October 2012, and April 2014, covering 4.78 m ha. More than 1900 in-season site visits were conducted across 228 monitoring locations in the footprints for classification purposes, and more than 1300 field observations were made for accuracy assessment. Some 1.6 m ha of rice were mapped with classification accuracies from 85% to 95% based on the parameters that were closely related to the observed temporal feature descriptors derived for each site. The 13 sites capture much of the diversity in water management, crop establishment and maturity in South and Southeast Asia. The study demonstrates the feasibility of rice detection at the national scale using multi-temporal SAR imagery with robust classification methods and parameters that are based on the knowledge of the temporal dynamics of the rice crop. We highlight the need for the development of an open-access library of temporal signatures, further investigation into temporal feature descriptors and better ancillary data to reduce the risk of misclassification with surfaces that have temporal backscatter dynamics similar to those of rice. We conclude with observations on the need to define appropriate SAR acquisition plans to support policies and decisions related to food security.

Oil Palm Tree Detection with High Resolution Multi-Spectral Satellite Imagery

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.

Automatic Rice Crop Height Measurement Using a Field Server and Digital Image Processing

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.

Using multi-temporal remote-sensing data to estimate 2011 flood area and volume over Chao Phraya River basin, Thailand

In 2011, when Thailand faced its most severe flood disaster in 50 years, the Geo-Informatics and Space Technology Development Agency provided flood affected data to support government agencies during the crisis, specifically synthetic aperture radar (SAR) imagery, optical satellite imagery and a digital elevation model (DEM). These data were combined with water level data from gauge stations to map the area flooded and to estimate water volume in near real time to support decision-making for flood relief operations. However, difficulties were encountered when dealing with different kinds of spatial data and different application techniques. Problems included inconsistent acquisition schedules for different satellites, different image resolutions and different data acquisition modes, i.e. ScanSAR Wide and Wide modes. DEM accuracy also proved to be an issue. Current work is underway to improve the satellite image acquisition planning and DEM accuracy and increase the number of gauge stations in the flood affected area so as to improve the accuracy, reliability and usefulness of geoinformatics data for future disaster management.

Using multiscale texture information from ALOS PALSAR to map tropical forest

This research investigated the ability of the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) to map tropical forest in central Sumatra, Indonesia. The study used PALSAR 50 m resolution orthorectified HH and HV data. As land-cover discrimination is difficult with only two bands (HH and HV), we added textures as additional information for classification. We calculated both first- and second-order texture features and studied the effects of texture window size, quantization scale and displacement length on discrimination capability. We found that rescaling to a lower number of grey levels (8 or 16) improved discrimination capability and that equal probability quantization was more effective than uniform quantization. Increasing displacement tended to reduce the discrimination capability. Low spatial resolution increased the discrimination capability because low spatial resolution features reduce the effects of noise. A larger number of features also improved discrimination capability. However, the amount of improvement depended on the window size. We used the optimum combination of backscatter amplitude and textures as input data into a supervised multi-resolution maximum likelihood classification. We found that including texture information improved the overall classification accuracy by 10%. However, there was significant confusion between natural forest and acacia plantations, as well as between oil palm and clear cuts, presumably because the backscatter and texture of these class pairs are very similar.

An agricultural monitoring system: Field server data collection and analysis on paddy field

The paper presents an agricultural monitoring system developed for Thailand. Various species of plants have been directly observed from the agricultural fields which mainly consist of economic crops of Thailand such as rice, cassava, rubber, sugar cane, corn, etc. An equipment used to obtain the data is called field server, which has been installed at the observed field for a long period. The collected data is separated to two parts: daily images (acquired twice per day) and weather information (recorded every five minutes). The weather information is as follows: temperature, rain volume, light density, humidity, soil moisture, wind speed and direction. Since the beginning of 2014, twenty-four field servers have been deployed in every region of Thailand. The data from the field servers is uploaded to a central server. Users can access and obtain the data via a web browser. Given the images and weather information (temperature and soil moisture), the data recorded from paddy fields is preliminarily analyzed as a guideline for further development.

Fusion and registration of THEOS multispectral and panchromatic images

This article presents a new method for the fusion and registration of THEOS (Thailand Earth Observation Satellite) multispectral and panchromatic images in a single step. In the usual procedure, fusion is an independent process separated from the registration process. However, both image registration and fusion can be formulated as estimation problems. Hence, the registration parameters can be automatically tuned so that both fusion and registration can be optimized simultaneously. Here, we concentrate on the relationship between low-resolution multispectral and high-resolution panchromatic imagery. The proposed technique is based on a statistical framework. It employs the maximum a posteriori (MAP) criterion to jointly solve the fusion and registration problem. Here, the MAP criterion selects the most likely fine resolution multispectral and mapping parameter based on observed coarse resolution multispectral and fine resolution panchromatic images. The Metropolis algorithm was employed as the optimization algorithm to jointly determine the optimum fine resolution multispectral image and mapping parameters. In this work, a closed-form solution that can find the fused multispectral image with correcting registration is also derived. In our experiment, a THEOS multispectral image with high spectral resolution and a THEOS panchromatic image with high spatial resolution are combined to produce a multispectral image with high spectral and spatial resolution. The results of our experiment show that the quality of fused images derived directly from misaligned image pairs without registration error correction can be very poor (blurred and containing few sharp edges). However, with the ability to jointly fuse and register an image pair, the quality of the resulting fused images derived from our proposed algorithm is significantly improved, and, in the simulated cases, the fused images are very similar to the original high resolution multispectral images, regardless of the initial registration errors.

A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field Model

Traditionally, image registration of multi-modal and multi-temporal images is performed satisfactorily before land cover mapping. However, since multi-modal and multi-temporal images are likely to be obtained from different satellite platforms and/or acquired at different times, perfect alignment is very difficult to achieve. As a result, a proper land cover mapping algorithm must be able to correct registration errors as well as perform an accurate classification. In this paper, we propose a joint classification and registration technique based on a Markov random field (MRF) model to simultaneously align two or more images and obtain a land cover map (LCM) of the scene. The expectation maximization (EM) algorithm is employed to solve the joint image classification and registration problem by iteratively estimating the map parameters and approximate posterior probabilities. Then, the maximum a posteriori (MAP) criterion is used to produce an optimum land cover map. We conducted experiments on a set of four simulated images and one pair of remotely sensed images to investigate the effectiveness and robustness of the proposed algorithm. Our results show that, with proper selection of a critical MRF parameter, the resulting LCMs derived from an unregistered image pair can achieve an accuracy that is as high as when images are perfectly aligned. Furthermore, the registration error can be greatly reduced.