A RS/GIS-Based System for Monitoring Weed Disasters

Ling Sun Resource Environment Institute

JiangSu Academy of Agricultural Sciences Nanjing, 210014, China sunl60@jlonline.com

Zesheng Zhu Research Department

Naval Command College Nanjing, 210016, China

sunl60@jlonline.com

Abstract—The main obstacle for monitoring weed disasters is how the information from RS and GIS associates with weed disaster modeling. In the information representing physical and logical fusion objects subject to weed disaster monitoring, we analyze a system architecture model for solving the related design issues, including area and type estimation of weed disaster. Besides the information or data fusion, we use the model as a tool of data fusion to avoid the parameter estimation problems in complex environments. Most surprisingly, externally gathered data from local statistics sensors can be entered into a GIS along with the imagery data for increasing the precision of weed disaster modeling.

Keywords-weed disaster;monitoring; remote sensing; data fusion

I. INTRODUCTION Several traditional weed disaster management methods

have been developed, and we concentrate on a new RS/GIS-based weed disaster monitoring. Really, weed disaster management involves monitoring the disaster activity, making the management decisions and performing the control actions to reduce the disaster influence. The use of weed disaster information is a central issue in monitoring weed disasters. The problem of using precisely weed disaster information and estimating its influence with RS and GIS techniques is major issue. Various previous methods devoted to solve this theme make unrealistic assumptions about RS image quality and other environment factors under poor image quality and complex environment. The error produced by the assumptions makes it very difficult to increase the estimation precision of weed disaster influence. Thus, one approach to weed disaster modeling, for solving this problem, based on data fusion to implement the RS/GIS weed disaster monitoring is developed. This data fusion basically assumes that the prior information in the statistics sensors is as important as the information in RS image; therefore, both the image information and the prior information are treated in data fusion procedure.

II. ARCHITECTURE MODEL As is standard in weed disaster modeling, the whole

pictures of weed disaster modeling become bewildering when

monitoring weed disasters become more heterogeneous and more hardware and software from various vendors are used. In particular, this brings out the need for a unified approach or architecture to the implementation of weed disaster modeling. To analyze weed disaster, RS images provide an immense amount of information about weed disaster with the help of a GIS. These high-resolution aerial photography and field samples can expose valuable information that may be hidden from satellite sensors due to their altitude. The combination of ground truth data with satellite images often leads to the most integrated and accurate analysis approach [1]. In the weed disaster modeling, externally gathered data from local statistics sensors can be entered into a GIS along with the imagery data. For that reason, a GIS allows for easy synthesis of the interrelated data and provides an effective means of analysis.

Semantic contextual analysis of RS images in weed disaster modeling remains still an area of open investigation, since the only effective tools for it today are well-trained human experts.

For weed disaster modeling, semantic interpretation focuses on conclusions and facts but not data and statistical features. Thus, the high quality of an image is very important. RS image taken in the same location may substantially differ due to incidental reasons, like current illumination and weather. Some other more conspicuous structural reasons will exist as a consequence of the different processes and camera parameters being used. In general, the quality of an image will depend on spectral information, camera attitude and resolution, and the elevating platform. Because parameter values are usually estimated during analysis, the primary issue then is which parameters should be included for estimation when given a particular data set, a process referred to as architecture selection. The term architecture denotes those feature selection techniques that filter out irrelevant attributes before monitoring is performed. A major issue is the specification of architecture. This architecture allows a great deal of flexibility, but it requires the technical details as follows.

We consider spatial resolution of an image to be a "gold standard" in the sense that we believe it is one of the best methods to date for obtaining reliable estimates of weed disasters explored here. Spatial resolution of an image involves the minimum distance between two individually detectable

This research was supported by the Technology Innovation and Application of Remote Sensing for Monitoring Staple Crops in Jiangsu Province for grant SX(2011)392

point objects that can be distinguished, which is expressed in meters. The sample definition of an image should not be confused with the spatial resolution. To preserve information of the image, the sample definition employed in its digitization should be smaller than the resolution. Detecting an object in weed disaster modeling will not guarantee its recognition. The characteristic size, luminance and contrast of crop lands about weed disaster in aerial images make them typically detectable at 10m resolutions. For an expert to assert that something is weed disaster (recognition) requires a resolution about two times better (5m). For the classification of weed disasters, the resolution should be better than 2.5m.

To date, existing analysis methods of monitoring weed disasters have not been evaluated with data that contain outliers. Computer-vision and algorithms are frequently based on presumed characteristics of human vision. One of them is the hierarchical organization. This means that recognition of complex weed disasters is obtained by first recognizing elementary patterns [2], then recognizing more complex patterns based on their positional relationships.

For most of that time Image processing designers have had a choice between two options: randomized sampling and purposive selection. Image enhancement, edge detection, and thresholding are commonly applied on digital images as a first step in extracting information about weed disaster modeling. Linear or non-linear filtering is extensively used in these steps.

In fact, this same feature extraction issue arises in applying the remote sensing technology to any problem involving a choice between many weed disasters competing limited image resources. Features are geometric patterns in images. The most important basic of them is the line segment, because its occurrence in RS images is often associated with weed disaster area and types. Interesting higher-complexity features, like color, right angles, parallel line segments, and rectangles can be defined with straight-line segments. Parametric transforms are standard methods of accomplishing feature extraction about weed disaster. They map an image from a primary domain into a transformed space where it is easier to identify geometric features [3]. The transformed space, called a parametric space or transformed domain, is often a two dimensional space that can be displayed as an image as well. The area and type auxiliary information about weed disaster can be used in the parametric space to improve weed disaster estimates.

III. FUSION CONCEPTUAL MODEL Conceptual model designs have been among the most

commonly used designs for data fusion. A conceptual model of data fusion for the weed disaster modeling includes the function of multi-sensor data fusion. The “implement data fusion” modular incorporates three basic levels of processing. Level 1 fusion processing consists of weed disaster area and types, which combine weed disaster area and type data derived from all TM and statistic sensors to obtain the most accurate estimate of area and types of any weed disaster land block in a remote sensing area. An alternative technique to combat area or type fusion is of weed disasters to consider parameter estimation with some restrictions on the unknown parameters, which can be exact or stochastic restrictions. The area or type

fusion is divided into parametric association and estimation. Parametric association associates observations from multiple sensors to individual entities. Parametric association is important because an incorrect data association would affect the performance of the target (weed disaster land block) or state tracking. Here, the target refers to weed disaster land block or state. Given the association of each observation to each target, estimation techniques are then used to combine the data to obtain better estimate of the state attributes of weed disaster land blocks. Level 2 processing is aimed at situation or state assessment of weed disaster types, and a process by which a description of the relationships among all entities is developed. In this level, the outputs from level 1 processing are analyzed and examined to bring out the essential features of area and types of weed disaster land in the complex conditions. Level 3 processing is used for the precision assessment of weed disaster types. Its purpose is to determine the meaning of the fused data, such as an estimate of weed disaster land area and types, expected courses of precision or state variation, and an estimate of error of weed disaster types. The level usually employs heuristic and statistics techniques similar to those for situation assessment of weed disaster land area and types in addition to utilizing the knowledge-based system.

IV. DATA FUSION MODEL On the basis of this data fusion and the appearance of the

data association, we compare weed disaster targets with a mixture having several components by using our parametric approach. As previously stated, the most important function in the data fusion in weed disaster type modeling is the association of the measurements to the targets before any estimates can be made from the measurements. Data association is responsible for partitioning the measurements into sets that could have originated from the same targets. In general, the data association process consists of three main steps: a gating technique, an association metric and an assignment strategy. The gating technique eliminates unlikely observation-to-tracks pairings by using a priori statistical knowledge. The term track refers to status of weed disaster land block, including its area and type. This step is used to reduce the number of combinations of observations-to-tracks pairs that will be considered for data association. The second step is used to determine a similarity measure between all observations and all tracks of existing weed disaster land blocks. The final step is used to solve the problem of assigning observations to tracks.

Gating was done on a weed disaster composed of real data from survey. Given a set of attributes{ }iY , gates are formed around the predicted attributes{ }jV of weed disaster land block, represented as a function of the state vectors { }jX . The attributes of weed disaster land block are parameters, such as area, vigor, and types. If some attributes fall within the gate of a track, they may be associated with the corresponding track. All other attributes outside the gate cannot be associated to the track. If a single observation falls within a gate and does not fall within any other track’s gate, that observation will be associated to the corresponding track and no further action is

needed in the data association process (no conflict situation). If more than one observation fall within a gate or if an observation falls within more than one gate, further processing is needed (conflict situations). Thus, the gating techniques reduce the number of computations by providing only feasible pairs of observations and tracks. A rectangular gating technique is used here to implement the data association. The rectangular gating defines a region such that an observation is associated to a track if all the attributes { }kY satisfy the following relation: k k kY Y B− ≤

Where kB is the residual standard deviation and is given by: 2 2

kB Gk a b= × + Where 2a is the measured standard deviation and 2b is the standard deviation obtained from the kalman filter. The gating constant Gk depends on the density of the distributed observations, the interpretation probability and the dimension of the state vector. The rules from the relationship operation and the data from images are used to derive artificial data fusion. For each feasible observation-to-track pair, an association metric expressed as distance is computed. The association metric represents the degree of similarity between two entities. There are four standard criteria that can be used to determine whether or not a metric d is a true metric. There are four types of distance metric: Correlation coefficients, distance measures, association coefficients, and probabilistic similarity coefficients. The selection of a similarity measure depends on the application. The distance measure is the simplest and widely used as association measure. Unlike the correlation coefficient, the distance measure is a true metric and sensitive to difference in the magnitude of the attributes, and suitable to our application. The distance measure has no upper bound and is applicable only to continuous variables. The Euclidean distance is used in the representation of distance measures.

A real extrusion experiment was conducted to illustrate the construction of an assignment design and data from the experiment were analyzed using the association matrix methods proposed. The assignment strategies determine the actual association of the observations to the tracks. This can be done after constructing an association matrix between all the observations and all the tracks. Each element in the association matrix is determined by using one of the similarity measures. The optimal solution is to choose the assignment that maximizes the summed total similarity measure. For a typical association matrix, its rows represent tracks and the columns represent observations. The matrix may become the association matrix after applying the rectangular gating technique. The values in latter matrix are obtained by using the Euclidean distance measure. The optimal solution for this assignment is to choose the assignment that minimizes the summed total distance. In the case of two or three targets and observations, developing the optimal solution can easily be determined by enumeration. In case of a large number of targets and observations, finding the optimal solution is time

consuming, thus suboptimal solutions are applied. A possible suboptimal solution is to search the association matrix for the minimum distance measure and make the indicated assignment. This step is repeated until all the tracks are assigned to observations.

V. TRACK FUSION Track fusion plays an important role in analyzing data from

weed disasters. Assume that two sensors observe three targets in an overlapping coverage scenario and report four tracks to the fusion center. The statistic sensor provides the track of target 1, target 2, and target 3. Each reported track iR ,

1, 2,3,4i = , has two attributes, the x (area) and y (type) of the observed targets. At each scan, the data can be represented as a matrix as shown in a table, where columns represent tracks and rows represent attributes. The current goal is to decide which tracks are similar, in the sense that they represent the same target, and when they are similar, it is required to fuse them into a single track. With a small data matrix, the data matrix and the similar and dissimilar tracks can be simply found. Two tracks that have about the same values are more similar than two tracks that do not. However, for a large data matrix, the visual inspection is very difficult, so that cluster analysis becomes essential. Once two or more tracks have been associated to the same target, the next step is to combine them into a single track. This can be done either by adopting the superior track, or by fusing the tracks into a single one. The superior track can be chosen according to the characteristics of the sensors in terms of sensor resolutions. If the sensors have the same resolution, the superior track is chosen according to the operating conditions such as the relative resolution to the target. The higher relative resolution the more accurate is the sensor track estimate. In the case of fuzzy track fusion, the tracks can be combined according to the corresponding degrees of membership. In this way, the fused track estimate can be defined as fR . The proposed fuzzy track-to-track association and track fusion approach is show in right side in Figure 3. The iiμ represents the degree of membership of the resolution of sensor, and ijμ ( i j≠ ) represents the degree of membership of the difference between two tracks. The CORR represents the correlation matrix for determining the different targets (the related element=0) or the same targets (the related element=1). The data fusion provides surveillance of weed disaster type, and it has been adopted, where it has been used successfully for the weed disaster type modeling such as detecting and tracking the status of a weed disaster land block. On the other hand, the multiple sensors cause tracks per target to be observed in a different way. This problem, which is called the multiple tracks problem, causes degradation in the performance of target tracking and identification. An association approach is essentially needed to merge the multiple tracks into unique set of tracks that represent the true number of targets.

Associating the multiple tracks is especially useful and more practical when weed disaster monitoring is based on a previous study, since the data from the previous study may

behave quite differently from the data in the current study. The fuzzy clustering approach can be used to associate the multiple tracks that belong to the same target. For example, the same target is tracked simultaneously by the sensors. The detected tracks are reported to a data fusion center. The data fusion center receives several tracks. Each track consists of area and type information of the observed target. The sensor uncertainties are represented by the covariance matrix, which relate to the variances of the measurement errors of area and type information respectively. Otherwise, the interpretation analysis causes additional errors in area and type measurements. These errors are assumed to be normally distributed with zero mean and non-zero variances in area and type respectively. The comparison between tracks obtained before fusion and the fused tracks shows that the proposed approach seems to yield more satisfactory results.

VI. APPLICATION A case study is presented on weed disaster monitoring in a

cotton producing process, including a cotton weed injury design used and the analysis of the data from the field. In light of these discussions, we applied the RS/GIS system to monitor whether a weed disaster in cotton farmland can be detected. We also investigated other variables that might influence the weed disaster monitoring. Thus, in addition to weed disasters as entities for cotton weed injury (CWI) blocks, we included area and severity tracks as the measuring data of weed disasters, as a lower rate of cotton weed injury should increase severity in weed disasters. Similarly, all sensors reported the information about observed entities of CWI block to a data fusion center so that the redundant tracks might enter into empty tracks. In all of these scans, the reported information underwent CWI block name, time of report, tracking status (sensor type), track number, sensor track number, track quality, disaster area and severity. The proposed track-to-track association and track fusion approach was applied to real data collected from RS images and statistics sensors. Our results have several implications for the use of RS and GIS in studies of weed disaster monitoring. First, our results confirm that the data fusion model successfully fuses the redundant tracks and displays the superior tracks in all scenarios, which is consistent with field verification. Second, low efficiency was not consistently associated with high levels to associate and fuse tracks obtained from different RS sensors and statistic sensors. because the data fusion performance is heavily influenced by other factors, such as data fusion framework or architecture. Thus, while low efficiency may indicate low levels, high efficiency is high levels. This may indicate that its many advantages include simple model construction, easy correctness verification, management and maintenance of weed disaster modeling system, and easy integration with other software packages, such as expert system, machine learning systems, large database systems and special simulation systems.

Thus the biases in the fitted targets that possibly exist for these weed disasters are likely to be due to the high response probabilities of a large proportion of these synthetic weed

disasters. A prototype system called WDMS (weed disaster modeling system) has been developed to analyze and process a number of field data from RS image and statistic sensor for completing weed disaster modeling with GIS [6] and RS [7] software packages. The primary consideration of WDMS implementation is to allow its users access and use all available relevant RS and statistic data, and various interpretation tools provided by WDMS to estimate accurately the weed disaster of JiangSu province by using complex data processing functions in WDMS. The WDMS tasks include the estimation of weed disaster area and type. The final experiment results from the weed disaster modeling system show that this application architecture framework has very satisfactory performance and fast data fusion speed with comparison of our past traditional methods. Figure 2 shows application architecture for weed disaster modeling based on RS and GIS techniques.

VII. CONCLUSIONS The method presented in this paper has focused basically

on the use of RS, GIS and data fusion techniques, including weed disaster modeling, measurement-to-statistics result association, track fusion, and decision fusion, to implement the system of monitoring weed disasters in complex environment. A fusion technique for measurement-to-statistics result association is providing new opportunities to monitor weed disasters in a RS and GIS context. Simultaneously, methodological developments in weed disaster modeling are providing the tools to combine output of RS image and local statistics sensors in a more precision way. The success of this system for monitoring weed disasters will depend on how well the fusion methods work for particular types of weed disasters and under different RS conditions.

ACKNOWLEDGMENT This paper was supported in part through the Technology

Innovation and Application of Remote Sensing for Monitoring Staple Crops in Jiangsu Province, grant SX(2011)392.

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