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A mixed pixel in remote sensed images is a major problem, and the super-resolution mapping is one of the approach to deal with this problem. In this paper, we address the problem of super-resolution mapping by combining a set of random forests with a Markov random field (MRF) model. Here, a random forest is trained to estimate a class proportion of only one land cover class. Thus, there are equal...
In this paper, we proposed an approach for super-resolution land cover mapping on remote sensing images based on the deep learning technique, namely Convolutional Neural Network (CNN) by combining with the level set method (LSM). Here, the CNN is used to find the probabilities that a subpixel belonging to a land cover class, and the LSM is employed to fine tune the boundaries among land cover classes...
Accurate crop start date estimation is crucial for crop yield forecasting which is important not only for a government but also for agriculture-based or trading companies. The estimation can be done using the Normalized Difference Vegetation Index (NDVI) computed from radiant energy from the crops of interest. The NDVI collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard...
In this paper, we introduced a land cover mapping algorithm that combines for unsupervised and supervised classification techniques, namely, the Restricted Boltzmann machines (RBMs) and Support Vector Machines (SVMs). The idea is to take advantage of unsupervised classifications that can segment an image into regions without any training samples, and the supervised classification that can identify...
In this paper, we introduce a new land cover mapping technique by taking advantages of a weighted random forest [1] and the level set method [2] to remove the weaknesses of each other. The weighted random forest can accurately estimate the likelihood that a pixel belonging to each classes while the level set method can capture the dependency among neighboring pixels. As a result, by combining their...
Crop yield forecasting is important either for a government, agricultural industries or a trading company for their action plans. A very important variable to be given to a crop model used for the forecast is an accurate crop cultivation date. When the area of interest is large, it is preferable to use remote sensing data such as satellite images for the crop monitoring. The estimation of the cultivation...
In this paper, we proposed a new random forest algorithm designed specifically for the land cover mapping problem. Three approaches are investigated, namely, pixel-based, neighbor-looking and combination of both. In the pixel-based approach, we use the fact that all decision trees are different whereas, in the neighbor-looking, the decisions from neighboring pixels are used when the decisions from...
The accurate rice phenological development stages estimation is important for rice yield estimation, since each phenological developmental stage possesses different increase rates of biomass and leaf area. This paper presents a method to estimate the rice phenological development stages: seedling, tillering, heading, and harvest. This method uses a vegetation index, i.e. Excessive Green (ExG) integration...
In this paper, we propose a level-set based method to identify urban areas using multitemporal Synthetic Aperture Radar (SAR) data. Our method is compared to the standard method, called the Otsu's threshold-based method. The experimental results indicate significant improvement in terms of the Kappa coefficients and the percentage of correctly classified pixels.
The actual field survey data from the Rice Department of Thailand's Ministry of Agriculture over a large area wastes a huge amount of resources. To solve this problem, this paper proposes a new approach to estimate rice phenology using SAR images derived from the RADARSAT-2 data. In this work, we divided the rice phenology into five stages, consisting of seedling, tillering, reproductive, ripening,...
In 2012, GISTDA has launched a sensor network project for monitoring agricultural fields in every region of Thailand. Integration of digital camera and weather sensors, Field Server (FS) is used to collect two types of data; image and weather condition. In this study, time-series images acquired from the rice field are used for computing and understanding the rice crop calendar. A proposed diagram...
We have proposed a new speckle filtering method in this paper. Our method uses time-series of SAR images at the same scene to perform multitemporal speckle filtering to reduce speckle noise while preserving its spatial information. We used segmentation based on the distribution of speckle noise by the Öztürk algorithm in this method for achieve a higher quality of edge areas.
In this paper, we propose a level-set method to identify urban areas using a nighttime light data of Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS). Our method is compared to two standard methods, called the Otsu's threshold-based method and the k-mean clustering method. The experimental results indicates significant improvement in terms of the Kappa...
The actual field survey data from the Rice Department of Thailand's Ministry of Agriculture over a large area wastes a huge amount of resources. To solve this problem, this paper proposes a new approach to estimate rice phenology using SAR images derived from the RADARSAT-2 data. In this work, we divided the rice phenology into five stages, consisting of seedling, tillering, reproductive, ripening,...
In this paper, we propose the speckle removal algorithm from Synthetic Aperture Radar (SAR) images via a segmentation technique based on Öztürk algorithm. The Öztürk’s algorithm is used to determine the distribution for each sample. These procedures are processed to see the relation between distribution of each pixels and segments of the image. We demonstrated the effectiveness of our algorithm by...
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...
Rice cultivation and harvest dates are very useful information since they are the key factors in rice monitoring, yield estimation and damage assessment. This paper proposes a new approach to estimate rice cultivation and harvest dates by using the 8-day composite normalized difference vegetation index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data. However, the NDVI...
In this paper, a software program is developed to monitor rice growing stages. Images are required as input data for the software. Using field server equipment, the images are obtained from two rice fields located in Suphanburi and Roi Et provinces, Thailand. Each daily image covers approximately 100 × 100 m2 recorded by 720 × 480 pixels. Typically, a rice growing cycle is separated by three stages;...
In this paper, we proposed an unsupervised algorithm to identify the flooded areas from synthetic aperture radar (SAR) images based on texture information derived from the gray-level co-occurrence matrices (GLCM) texture analysis. Here, five GLCM features, namely, energy, contrast, homogeneity, correlation and entropy, are extracted from a SAR image. These features are input to an image segmentation...
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...
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