Apisit Eiumnoh

Estimation of cropped area and grain yield of rice using remote sensing data

Abstract Studies for estimating cultivated area and yield of rice using remote sensing data acquired from the Landsat Thematic Mapper (TM) system with six bands (1, 2, 3,4, 5,7) were conducted. Three images taken on different days were visually and digitally analysed for the estimation of rice cultivated area. This was obtained with over 90 per cent classification accuracy. False colour composite of 5R, 4G, 3B was selected for the identification of rice cultivated area at maturity stage. Band combination of 1, 3, 4 and 5 was selected as an appropriate subset in the digital analysis for the estimation of rice areas. An attempt was made to develop a relationship between reflectance values and actual grain yield using an image of the fully ripened stage of the crop. High correlation was observed with reflectance values of some bands and yields. A computerized plant process model was adopted for the simulation of rice growth and yield. This was used for estimating yield per unit area.

Extraction of City Roads Through Shadow Path Reconstruction Using Laser Data

This paper presents an automatic road extraction technique that combines information from aerial photos and laser scanning data (LSD). This innovative Road Extraction Assisted by Laser (REAL) can detect the road edges shadowed by surrounding high objects. A new concept of Associated Road Line (ARL) graph from LSD is introduced to enhance Real Road Line (RRL) graph from aerial photos. The extraction process consists of three steps: The first step is analysing laser images where parameters such as height and edges of high objects are obtained. Secondly, digital images are analysed where road edges are detected. It is evident that ARL and RRL graphs are homeomorphous which provides a theoretical foundation of REAL. The gaps of RRL are bridged through their ARL with a topological transformation. Finally, shadowed parts of RRL are reconstructed by a spline-approximation algorithm. The experimental results show that this approach is effective and has potential advantages.

Band Selection for Dimension Reduction in Hyper Spectral Image Using Integrated Information Gain and Principal Components Analysis Technique

Nowadays, hyper spectral image software be- comes widely used. Although hyper spectral images provide abundant information about bands, their high dimensionality also substantially increases the computational burden. An important task in hyper spectral data processing is to reduce the redundancy of the spectral and spatial information without losing any valuable details. In this paper, we present band selection technical using principal components analysis (PCA) and information gain (IG) for hyper spectral image such as small multi-mission satellite (SMMS). Band selection method in our research not only serves as the first step of hyper spec- tral data processing that leads to a significant reduction of computational complexity, but also a invaluable research tool to identify optimal spectral for different satellite applications. In this paper, an integrated PCA and IG method is proposed for hyper spectral band selection. Based on tests in a SMMS hyper spectral image, this new method achieves good result in terms of robust clustering.

Determination of the Initialization Number of Clusters in K-means Clustering Application Using Co-Occurrence Statistics Techniques for Multispectral Satellite Imagery

 Abstract—Nowadays, clustering is a popular tool for explo- ratory data analysis, such as K-means and Fuzzy C-mean. Automatic determination of the initialization number of clus- ters in K-means clustering application is often needed in ad- vance as an input parameter to the algorithm. In this paper, a method has been developed to determine the initialization number of clusters in satellite image clustering application using a data mining algorithm based on the co-occurrence matrix technique. The proposed method was tested using data from unknown number of clusters with multispectral satellite image in Thailand. The results from the tests confirm the effectiveness of the proposed method in finding the initializa- tion number of clusters and compared with isodata algorithm. Clustering is a popular tool for data mining and explora- tory data analysis. One of the major problems in cluster analysis is the determination of the number of clusters in unlabeled data, which is a basic input for most clustering algorithms. In this paper, we propose a new easy method for automatically estimating the number of clusters in unlabeled data set. Pixel clustering technique in a color image is a process of unsupervised classification of hundreds thou- sands or millions pixels on the basis of their colors. One of the most popular and fastest clustering techniques is the k- means technique. The results of the k-means technique depend on different factors such as a method of determina- tion of initial cluster centers as shown in Fig. 1. Such sensi- tivity to initialization is an important disadvantage of the k- means technique. In this paper, a method has been devel- oped to determine the initialization number of clusters in satellite image clustering application using a data mining algorithm based on the co-occurrence matrix technique. Therefore, automatic determination of the initialization number of clusters can greatly help with the unsupervised classification of satellite Image.

Textural information in SAR images for land-cover applications

Investigates the existence of textural information in spaceborne SAR images. ERS-1 SAR images processed at DLR (Germany) and NRSA (Hyderabad) are used for texture based analyses. The possibility of textural discrimination of different cover types such as paddy, sugarcane, water, urban areas, bush and shrubs are explored. Texture measures based on both the first and the second order image statistics are computed. The effectiveness of coefficient of variation (CV) as a texture measure is evaluated. The usefulness of gray-level co-occurrence matrices (GLCM) as a second order statistical measure of texture is investigated. The temporal plots of sample land cover categories using two texture features namely contrast and inverse difference moment (IDM), are used to qualitatively evaluate the separability of different cover types. Quantitative methods of separability, using two class separability measures are used to assess the usefulness of GLCM derived texture images. It is found that improvement in separability of some land-cover categories is obtained using these texture features.

Parameter-Free K-Means Clustering Algorithm for Satellite Imagery Application

Unsupervised classification is a popular tool for unlabeled datasets in data mining and exploratory data analysis, such as K-means and Fuzzy C-mean. Although these unsupervised techniques have demonstrated substantial success for satellite imagery, they have some limitations. The initialization number of clusters in K-means clustering application is often needed in advance as an input parameter to the algorithm. Our previous paper regarding the initialization number of clusters in K-means clustering application with a co-occurrence matrix technique has been published. Although our previous approach regarding the number of cluster was discovered, but it was limited to count a number of peaks in occurrence matrix as the number of clusters by manual counting. The best of our previous approach need to automatically find and count a number of peaks in occurrence matrix. In this research, we assume that the satellite imagery is given and we have no knowledge beforehand for segmentation. Hence, this paper presents a simple, parameter-free K-means method for K-means in satellite imagery clustering application to determine the initialization number of clusters with image processing algorithms based on the co-occurrence matrix technique. A maxima technique is proposed for automatic counting a number of peaks in occurrence matrix as the number of clusters. The parameter-free method was tested with hyperspectral imagery and multispectral imagery. The results from the tests confirm the effectiveness of the proposed method in K-means method and compared with isodata algorithm.

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.

A knowledge based classification of multitemporal ERS-1 and JERS-1 SAR images over the tropics

Presents a knowledge based classification of multitemporal SAR images for land-cover application in general and agricultural applications in particular, by adapting methodologies more commonly applied to digital segmentation. The knowledge requirements are obtained from the temporal signature variations of the various land-cover types present in the area, with signature curves being based on ether radiometry or texture of the objects concerned. Use of textural information is made only in those cases when the use of object radiometry proves to be inadequate. Results show that classification accuracies are close to that obtained from maximum likelihood classification for the landsat TM data of the same area.<<ETX>>

Fusion of Ascending and Descending Pass SAR Intensity and Coherence Images for Mapping Volcanic Deposits

Abstract The advancement of satellite remote sensing has offered greater potential for mapping volcanic deposits. Although the development of weather‐independent microwave remote sensing has made the frequent detection over large area detection of deposits using SAR intensity image is sometimes hindered by ambiguities and noise. The ambiguities occur in volcanic deposit areas covered by young vegetation and that give either high or low backscatter depending upon their orientation. For this reason coherent images were integrated with SAR intensity images to extract more reliable information about volcanic deposited area. Besides, the layover areas due to the viewing geometry of SAR make difficulties to map the volcanic deposits on every side of the mountain. To avoid the influence of layover effects fusion techniques of ascending and descending pass SAR intensity and coherent images were developed. Using the fused images with an optical image, a color composite was developed to identify the areas affected by an eruption. In this color composite, especially vegetation damages can be easily identified.

Modelling electrical conductivity of soil from backscattering coefficient of microwave remotely sensed data using artificial neural network

Soil salinity is one of the main agricultural problems which expand to larger areas. Soil scientists categorize salinity level by electrical conductivity (EC) measurement. However, field measurements of EC require extensive time, cost and experiences. Remote sensing is one suitable option to investigate and collect spatial data in larger areas. Many researches estimated soil moisture through microwave, but there are fewer studies which mentioned about direct relationship between EC and backscattering coefficient (BC). Thus, this study aims to propose the estimation of EC directly from BC of microwave. The relationship between EC obtained from field survey and BC from microwave is non-linear, artificial neural network (ANN) is one technique proposed in this study to figure out EC and BC relationship. ANN uses multilayer of interconnected processing resulting in EC value with high accuracy which is acceptable. For this reason, ANN model can be successfully utilized as an effective tool for EC estimation from microwave.