Intelligent Geospatial Feature Discovery System (igfds) User Guide

Transcription

1 Supported by: DOE DE-NA Developed, operated, and maintained by: CSISS at GMU Intelligent Geospatial Feature Discovery System (igfds) User Guide Version 1.0 Center for Spatial Information Science and Systems (CSISS) George Mason University (GMU)

2 Table of Contents 1. Introduction Framework Client Servlet Service Usage Elementary Feature Extraction Image segmentation Image classification Elementary feature evaluation Post process of classification results Complex Feature Discovery Exploring complex features in our elementary feature database Exploring complex features in your classified elementary features Online Catalog of Geospatial Images and Web Services Uploading & Registering Data in igfds Catalog Registering & Removing Web services from igfds catalog Online Tools for Data processing Projection Transformation Coordinates Projection Transformation Time Conversion in Different Units Converting Raster to Vector Filtering Vector Features by Shape Combining a RGB Image to an One-layer Image Eliminating small polygons in an One-layer Image Viewing Vector and Raster files to an Online OpenLayers Map...42

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4 1. Introduction With the recent development of Earth sensors, tremendous volumes of Earth images are captured, collected, sorted, stored and published. The information of the images is rich but hidden. Traditional methods for geospatial feature discovery from those images usually require human interpretations or involvements, which limit many applications. igfds, an intelligent online system to automatically discover complex features from high resolution Earth surface images, aims to overcome some limitations of existing methods by leveraging advanced technologies. igfds is a prototypical ontology-supported system for nuclear proliferation detection. It has been implemented with many easy-to-use online tools for processing high-resolution Earth surface images. In its blue print, once an image is transferred to igfds, it can automatically extract elementary features like buildings, roads, waters and railways from the image, match the elementary features into the spatial organized patterns of complex features, filter out the qualified elementary features and determine what kind of complex features the image most probably contains. The development of igfds consists of two major phases: extracting elementary features from an image (Phase I), and discovering complex features from elementary features (Phase II). The Phase-I work has been completed and currently the project is on Phase II only two kinds of complex features, school and nuclear power plant, can be identified by igfds at this stage. igfds has great potential to change the way for geospatial feature discovery, particularly for detecting suspected nuclear locations. The nuclear proliferation is a continuing threat. There is an increasing demand on fast and reliable detection of suspected nuclear locations from remotely sensed images. igfds can intelligently judge the possibilities that an image contains nuclear facilities on a knowledge database, automatically provide the required information and relieve the burden on the human experts of manually doing the work if without igfds. Another convenience igfds brings us is that people can use it no matter where they are, what kind of devices they are using (smart devices at least) and when they want to use, because it s online and written in html. Personal computers, most smart phones and mobile platforms could access igfds if they are connected to Internet. igfds has an image database (we intend to use the titles from google map api) to make sure the function goes as well even if an user only provides the location information instead of an image. Thus, a user with a smart mobile device on a field mission can upload the location to igfds to quickly know the possibilities of a group of buildings being a nuclear facility and decide his next step of actions. 1

5 2. Framework igfds is composed of three main modules: client, servlet and service. Client JSP Ajax HTML5 CSS3 OpenLayers Servlet Message Transfer Request Generator Service Invoker File Uploader Catalog Manager Service Image Segmentation Image Classification Image to Vector Feature Calculation Vector Buffering Figure 1. The framework of igfds 2.1 Client The duty of the client module is communicating messages with users. Users get to learn igfds through the introduction provided by the client, and operate client interfaces to transform their requirements to eligible requests. The message sending & receiving component transfers the requests to the servlet module through Internet, parses the responses and displays the results in HTML tables or other forms to users. 2.2 Servlet The servlet module is responsible for processing on all the requests from the client module and returning proper responses. It also bridges the client and the services in other domains because the security mechanism of browsers doesn t allow the scripts in Web pages to directly visit the Web services in other domains. The servlet provides many tools to process kinds of requests separately. The tools include: complex feature discovery switching view mode acquiring ontology knowledge uploading data getting basic feature types executing query SQL sentences in online database instantiating a feature discovery rule into an executable workflow, clearing temporary files (for administrator only) registering new users 2

6 login existing users transferring messages to quick-response Web services transferring messages to slow-response Web services moving files from other servers to igfds server registering & un-registering Web services into igfds catalog moving a file to an accessible place on Internet through a URL address reading the content of an online text file 2.3 Service igfds has more than 20 Web services to complete various kinds of data processing tasks. A list of Web services can be found at the address: Each Web service has a description file following WSDL 2.0 standard. Users can visit and integrate them into their applications from any node on the Internet. In igfds, these Web services are used as actual processors to finish more than 90% of geospatial data processing tasks. Most of the requests from users are reformatted and transferred through the servlet module to Web services. The capabilities of igfds Web services include: getting projection of a vector or raster file splitting geospatial features by their class properties getting metadata of an image filtering geospatial features by their properties, e.g. length, area, shape getting metadata of geospatial features publishing an image or a vector file onto a MapServer selecting features from geospatial dataset A by dataset B under a spatial relation creating buffered geometries for geospatial features combining three bands of a RGB image into a single band image registering a vector or raster file into igfds data catalog extracting elementary features from a high resolution land surface image converting a raster file into a vector file merging the features with the same class property into a new layer removing small polygons in a vector file classifying an image by K nearest neighbor algorithm converting a projection from proj4 to EPSG code calculating the convex hull of geospatial features calculating minimum area bounding shapes of geospatial features evaluating the classification results judging whether an image or a vector file is projected transforming a vector file among different formats calculating several feature properties for geospatial features (please check the supported feature properties on this page : ) 3

7 segmenting an image into regions of pixel blocks. 4

8 3. Usage 3.1 Elementary Feature Extraction Image segmentation (1) Open the segmentation page Figure 3.1. The image segmentation page (2) There are two ways to let igfds receive your target images. First, if you already have the URL of an image, copy the URL into the Input the URL of an image field. Please make sure the link is accessible on the Web. If you only have a local image instead of online image, you could upload your image onto our server and an URL of the upload image will be returned. Click the button import a local image, and an image uploading window (Fig.3.2) will shows up. Note: Input images should have correct geospatial projections. If no projection image is provided, the following segmentation and classification is not guaranteed to be successfully done. 5

9 Figure 3.2. The image uploading window Click the Browse button to browse the local files, and choose the target file. Clicking the open button will save the file path to the uploading window (Fig.3.3). Then click the submit button to upload the image onto the server. The server will return a link of the uploaded image. Click the Load button which copies the image link into the Input the URL of an image field. Figure 3.3 Click submit to upload an image Figure 3.4 Click load to copy the image link into the Input the URL of an image field 6

10 Figure 3.5 The image link is copied to the Input the URL of an image field (3) Adjust segmentation parameters (Figure 3.6). Basically, users only need to modify three parameters. The first parameter Spatial (2h+1) is used in mean shift algorithm to create a (2h+1)*(2h+1) spatial window for pixel shift. The larger the value is, the bigger the segmented blocks are and the less the number of blocks is. The second parameter Color (2h) is to create a 2h*2h color window for pixel shift. It has a similar effect on the segmented blocks as the Spatial (2h+1) parameter. The Minimum Region parameter gives an approximate threshold for the minimum area of the segmented blocks, but it is possible that smaller blocks than the Minimum Region appear in the segmentation results. Figure 3.6 The basic image segmentation parameters Figure 3.7 Some other segmentation parameters Other segmentation parameters like weight map and speed up mode are suggested to be set as they are. Otherwise, they will make the segmentation course much complex and take much more time for users to wait. (3) Set the output format into GeoTiff. If BMP is set, the image can only be segmented without classification. 7

11 Figure 3.8 Set the output format to GeoTiff (4) Click the button click to segment to segment the provided image. If the segmentation is successful, the user will be redirected to a Web page containing five result images: raw image, filtered image, fused image, segmented image and boundary image. Each kind of images has a short introduction in the top-right blanket. The images can be downloaded through clicking the Download links below the images. Figure 3.9 A result page of image segmentation 8

12 Figure 3.10 The function provided for the fused image (5) If the raw image is projected, there will be two function links below the fused image: Post Process and Post Process 3. If click the link Post Process, the fused image will go through a series processes like band merging, small polygon removing and raster-to-vector transforming. Because the small polygon removing function only removes some of the small polygons, we provide another workflow Post Process 3 to run the removing function by three times and eliminate the small polygons into an endurable range. Figure 3.11 The post process workflow behind the link Post Process 3 We usually click the Post Process 3 link and a new window is redirected (Fig. 3.11). The URL of the predefined post process workflow can be seen in the only text field. Users can copy the link and download the workflow to their local computers. The workflow is written by BPEL and invokes three Web services provided by CSISS. If you are not satisfied with the workflow, you could create your own workflow through our online workflow designer ( (6) Once click the parse button, the workflow package will be automatically unzipped, imported and parsed. At the same time, the inputs of the post process workflow are automatically filled if you reach here through the Post Process or Post Process 3 link. Click the execute button, the workflow will be executed with the filled inputs by our workflow engine (supported by BPELPower). Figure 3.12 A parsed workflow window containing the automatically filled inputs 9

13 (7) A result page is shown in Fig The result of the post process workflow is an ESRI shapefile which can be downloaded. You can compare the shapefile with the raw image using your own GIS software to check if they are exactly matched and the segmented blocks are satisfying for your applications (Fig. 3.14). If the outputted shapefile is not fitting for you, for example, the segmented blocks are too small, you need go back to the image segmentation page, repeat the above processes until good results emerge. Figure 3.13 The result page after executing the post process workflow of image segmentation Figure 3.14 Compare the segmented blocks with the raw image for problem checking Image classification Following the result page shown in Fig. 3.13, let s begin the journey to classify the segmented blocks into meaningful basic geospatial features (elementary features). Here we use k nearest neighbor algorithm for image classification. 10

14 (1) First, click the link Classify the Vector Image and user will be redirected to the image classification page. The three fields in the first column are automatically filled and suggested not to be modified if users are unaware of them. Because the three fields give the links of the raw image, the segmented vector file and the source image from which the vector file is transformed, and the latter two files must be generated from the same raw image. Figure 3.15 The image classification page (2) Set an integer value to the k parameter. Quoting the instruction of KNN from Wikipedia, k is a user defined constant and an unlabeled vector is classified by assigning the label which is most frequent among the k training smaples nearest to that query point The best choice of k depends upon the data; generally, larger values of k reduce the effect of noise on the classification, but make boundaries between classes less distinct. Figure 3.16 Set the k parameter (3) Design your classification hierarchy. Provide the basic classes for elementary features into a classification tree. 11

15 Figure 3.17 Create a class 1) Add a Class: Type a class name, a short description (optional) and select a color for it. Click the button "Add Class", and a new class will appear as a node in the right class tree; 2) Delete a Class: Choose a class node in the tree and click the button "Delete Class"; 3) Non-classified features: If you don't want to classify the whole image, check the box after the statement "Whether allow non-classified features in the result?". At the same time, set a threshold value (default as 0.8) which will be used to judge whether a feature should be classified. If the threshold goes higher, more features will be unclassified. If the threshold goes lower, less features will be unclassified. 4) Save the current class hierarchy into a local text file: Click the save as a file button (Fig. 3.19), and a new window containing a class string (Fig. 3.20) pops up. Press Ctrl+S to save the string to a local file. 5) Import an existing class hierarchy from a local text file: Click the import from a file and an upload window pops up. Select a local class hierarchy file (saved by the button save as a file ) and click submit (Figure 3.21). After the file is uploaded, clicking load will import the hierarchy into the class tree. 12

16 Figure 3.18 Design a classification hierarchy Figure 3.19 Save a class hierarchy Figure 3.20 The string of a class hierarchy Figure 3.21 Import a class hierarchy from a local file Figure 3.22 Choose sample features (4) Choose some sample features. The k nearest neighbor classification is a kind of supervised classification, which needs samples and the accuracy is greatly influenced by the samples. Previously, the sample choosing is mostly done on the desktop based software. Here we develop a module supporting online sample selection which is an absolutely initial move in engineering views. The sample choosing is divided into the following steps: 13

17 1) Click the Load displayed in Figure 3.22 and the image data is loaded into an online map and the class hierarchy is imported into a class tree (Figure 3.23). Figure 3.23 Load the data into an OpenLayers map and a class tree 2) Choose a class in the class tree by clicking the corresponding class node. Then, dragging and zooming the map to click several features that belong to the chosen class. If you unintentionally click an incorrect feature, clicking the feature again will remove the selection of the feature (Figure 3.24). There is a line of log below the OpenLayers map so you could know what you just did. Figure 3.24 Selecting samples While you selecting features on the OpenLayers map, a sample table is dynamically changing with your operations. A new record is timely added to the table for each selected feature. If you cancel a chosen sample feature, the corresponding record will be removed from the table (Fig. 3.25). 14

18 Figure 3.25 A table recording the selected features 3) Save the sample table into a local file. Click the button save in Fig to save the current samples into a text file as a string (Fig. 3.26). Figure 3.26 A sample string 4) Read a sample table from a local file. The reading course is very similar to reading a class hierarchy from a local file. Click the read button in Fig. 3.25, select a local sample text file, submit it onto the server and load into the sample table. Figure 3.27 Load samples from a local text file 5) Delete a sample feature or clear the whole table. If a sample is abandoned, it can be deleted by clicking the button Del at the last column of the sample record. If all the samples are abandoned, click the button Clear the Table. (5) Set a feature space for the classification. Please take your notice here. The "feature" in "feature space" is different from the "feature" in "geospatial feature". The former has a similar meaning with "property". The latter means a geospatial object like a building, a road, a river. Each "geospatial feature" has several "property features". 15

19 Figure 3.28 Set a feature space In this step, the chosen "property features" of the geospatial features in the input shapefile will be calculated in the following classification. In Fig the left tree lists all the property features that we support. The right tree contains your chosen features. The calculated feature values are used in the classification. The choosing is very simple. Choose one leaf node in the left feature tree, then click the button with > and you will have the feature in your right tree. If a feature is abandoned, choose it in the right tree and click the button with <. 16

20 Figure 3.29 Create a feature space (6) Save the current classification session or load a classification session from a local file. Generally, if users close or leave the classification Web page for any reason, the class hierarchy, sample table and feature space will be gone and cannot be found. When the users come back and want to continue his classification, they have to redo all the above steps. That is very inconvenience. In these situations, users are suggested to use the following two functions: Figure 3.30 A screenshot of the classification buttons 1) Save the current classification request. Click the button Save This Request in Fig and a new Web page will show up just like the others. Save the content of the new Web page to a local file. In the file, the class hierarchy, sample table and feature space are all included. Figure 3.31 The content of a classification request 2) Load a classification session from a local file. Similar to other loading steps, click the button Load An Old Request in Fig. 3.30, choose a local request file, submit it to the server, and load the request into the classification page. Figure 3.32 Load an old classification request (7) Classify the image. If all the above preparation steps are done, click the button Click to Classify in Fig The time the classification takes depends on the size of the raw image and the number of geometries in the shapefile. Commonly, the waiting time should be no longer than 5 seconds for a 20MB raw image. 17

21 Figure 3.33 The waiting page for users while a classification is going on If the classification is done, users will be redirected back to the image classification page with a new result section attached. In the section, a link is provided for users to download the result shapefile to their own computers. In the result shapefile, each feature harvests a class property which contains the geospatial class that the feature belongs to. igfds also provides users with a function to check the result shapefile online. Click the button Display shown in Fig An OpenLayers map will display the result shapefile and render the geometries with their classes colors. You could select any geometry on the map and check its properties including the class property in the right property table. If the classification result is not satisfying, you could analyze the reasons from the result display section, adjust the classification inputs on the same page and send a new classification request. Repeat these steps until you get good classification results. Figure 3.34 The result page after a classification is done 18

22 Figure 3.35 Display the classification result shapefile Elementary feature evaluation Although we could judge the quality of classification results by our eyes and brains, an accurate index is still needed to mathematically evaluate the results. igfds provides you with two evaluation indices: overall accuracy and kappa coefficient. (1) Following the classification steps, once a classification is successfully done, two external links are provided at the bottom of the result display section (refer to Figure 3.36). They are two kinds of suggested next operations on classification results. Here we introduce the second one: Evaluate the accuracy of the classification results. Click the link Evaluate the accuracy of the classification results and redirect to an evaluation page (Fig. 3.37). Click the Load to import the raw image and classified shapefile and the class hierarchy. Figure 3.36 Two suggested next operations on classification results 19

23 Figure 3.37 The evaluation page of classification results Figure 3.38 Choose some sample features for evaluation (2) Choose some sample features for each class. Note that the features that were chosen for classification should be avoided in this sample set. While users are choosing samples, a sample table is also changing according to users operations. (3) After enough sample features are collected, click the button Click To Evaluate. A new section containing an overall accuracy value and a kappa coefficient value will be attached in the result page (Fig. 3.40). Generally, if the kappa coefficient is larger than 0.85, it means the classification result is fairly accurate and acceptable. In Fig. 20

24 3.40, the value is that is much smaller than 0.85, so we surely go back to modify the classification request for a better result. Figure 3.39 Evaluate the classification result on the chosen samples Figure 3.40 An evaluation result Post process of classification results Generally, plenty of useless small polygons exist in classification results, which need to go through a set of post processes before they can be used. Here we connect all the process processes into a workflow which is very easy and convenient to be executed. Three operations are concluded in the workflow: 1) remove small polygons (e.g., area < 20m 2 ); 2) merge the features with the same classes; 3) sort the features with the same class into a single layer. The details are as follows. Figure 3.41 The link of post process for classification results (1) After the classification is done, you will find two suggested links at the bottom of the result page. One of them (Evaluate the accuracy of the classification results) is 21

25 already introduced in Section This section will show you the usage of the other one. Click Post Process the Classification Results. A new window pops up as Fig displays. Figure 3.42 The Web page of the post process workflow (2) Click parse. An input window of the post process workflow shows up. Commonly, if you reach here though the classification page, the parameters are predefined and need no changes. Figure 3.43 The input window for the post process workflow (3) Click execute. Figure 3.44 The result page containing the outputs of the post process workflow 22

26 If the workflow is well done, the result page will show some log information and a file table (Fig. 3.44). Various classes of features are sorted and listed as single layer files. For example, in the first row, the features belonging to building are collected and saved as one shapefile with a building layer (Fig. 3.45). Figure 3.45 A building layer with a background image 23

27 3.2 Complex Feature Discovery igfds provides two entrances to discover complex features. The first is for those people who want to discover from the features stored in our database. The second one is for those people who want make a further process on the results of the operations in Section Exploring complex features in our elementary feature database (1) Open the home page ( (Fig. 3.46). Click Feature Explorer. An interface for complex feature explorer (Fig. 3.47) pops up. (2) Set a complex feature type. Select a complex feature type in the Feature 1 selection box. Click Add, the selected feature type will be added into the statement, e.g. Find (npp), in the text area on the right (Fig. 3.48). The abbreviation npp is short for nuclear power plant. (3) Set a spatial extent. Click Google Map to get a map window (Fig 3.39). Click the in the map and delineate a rectangle to cover the interesting region (Fig. 3.40). After that, close the window. The extent of the drawn rectangle will automatically appear in the bounding box coordinate fields. Another method to set spatial extent is choosing the state/county name (of U.S.) on the right sub panel. Once a state/county is selected, its bounding box coordinates will be filled in the coordinate fields on the left sub panel. Figure 3.46 The entrance button of complex feature discovery 24

28 Figure 3.47 The query interface of the complex feature explorer Figure 3.48 Set a complex feature type Figure 3.49 Set a spatial extent 25

29 Figure 3.40 Pop up a window displaying Google map Figure 3.41 The coordinates of the drawn rectangle Figure 3.42 Set the spatial extent of a state/county 26

30 (4) Set a time extent. Click on the end of the BeginDataTime time field. A time selecting window will pop up. After you click a day as the beginning date of your time range, the time window closes and the chosen date will be set in the BeginDateTime field. In the same way set a day to the EndDateTime field. The extent will be used to filter the observation date of the elementary features. If the raw data of an elementary feature is observed out of that time range, the elementary feature will not be considered in the discovery. Figure 3.43 Set a temporal extent Figure 3.44 Time selecting window (5) Select a number to set the records that each result page displays (Fig. 3.45). Figure 3.45 Set the number of records per result page (6) Click Make Query. igfds will search the specific kind of complex features in a rule driven way in the set spatial and time extent based on our elementary feature database. If some records qualified the requests, a result page as Fig shows will be displayed. igfds provides three mode to view the results: List View, Grid View and Map View. The List View gives a large thumbnail and a short introduction. The Grid View, aiming at giving users a quick overview of the results, shows the results 27

31 in small thumbnails without any introduction (Fig. 3.48). The Map View mode overlays all the result of the current page (no more than the number you set in Step (5)) on a Google Map to give users a clear and easy understanding of the geospatial locations of the suspected features (Fig. 3.49). Figure 3.46 Make a discovery in the set spatial and time extent Figure 3.47 A result page containing suspected components of the required complex feature type Figure 3.48 A result page in Grid View mode 28

32 Figure 3.49 A result page in Map View mode (7) Each result record can be clicked on the image for further details. A detailed page includes a property table illustrating the metadata of the result (Fig. 3.50) and a comparison table giving an overview on the actual terrain status covered by the suspected elementary features (Fig. 3.51). Figure 3.50 A detail page of single result record 29

33 Figure 3.51 A comparison between the discovered elementary feature and the background image Exploring complex features in your classified elementary features This function is only available after an image is successfully classified and post processed. Based on the results shown in Fig. 3.44, you can find a link Find npp from elementary features at the bottom. Click the link and a new workflow page pops up as Fig displays. Click parse to proceed to a window including the input fields of the finding workflow (Fig. 3.53). If the inputs have been checked (usually automatically filled without any changes), you can execute the workflow by clicking the execute button. A result page judging whether the classified elementary features contain a npp will appear (Fig. 3.54). Figure 3.52 The workflow finding npp 30

34 Figure 3.53 The input window of the finding npp workflow Figure 3.54 The result of finding npp among the extracted elementary features 31

35 3.3 Online Catalog of Geospatial Images and Web Services Uploading & Registering Data in igfds Catalog (1) To upload your raster & vector files to our server, open the entry page of uploading window < ). Click Upload to proceed to the uploading dialog. Figure 3.55 The entry interface of uploading data (2) Three steps to submit your data. First, select a type for your data. If your data is an image, check Raster; if your data is a shapefile, check Vector. (Note: your vector data must contain a property named Class.) Secondly, if you select data type as Vector, you must tell us about the feature type of the vector data. If the Class properties of the features in your vector data have the same value, the vector is considered as Single; otherwise, it is seen to be Integrated. If Single is selected, type the shared class value in the field after the Single ratio. Finally, provide a URL of your data or upload your data to our server. The latter function is disabled for security reasons and will be enabled soon. Raster data should be data in raw format without packaged or zipped. Rector data, taking ESRI Shape file as example, should be directly packaged in a zip without parent folders. (3) Click Submit to upload the data into our metadata database. If the data is successfully uploaded, the identity of the data record (Rid) will be returned. Using the identity, you can easily find your data from our database. Actually, upload here is very similar to register, which captures and stores the metadata of the uploaded data into a catalog database. 32

36 Figure 3.56 The data uploading dialog Figure 3.57 The response of a data uploading request Registering & Removing Web services from igfds catalog To register a Web service into our service catalog: (1) Open See Fig Figure 3.58 The service registration page (2) Copy the address of the WSDL of your Web service to the blank field in Fig Click register. 33

37 Figure 3.59 Copy the WSDL of a service and click register (3) If the service is registered successfully, a page as Fig shows will appear. Figure 3.60 A web service is successfully registered To un-register a Web service from our service catalog: (1) Open Figure 3.61 The service un-registration page (2) Copy the URL of the service WSDL into the blank field in Fig Click unregister. 34

38 Figure 3.62 Copy the WSDL URL and click unregister (3) If the service is successfully unregistered, a page as Fig shows will appear. Figure 3.63 A Web service is successfully un-registered 35

39 3.4 Online Tools for Data processing igfds has many Web services developed for completing feature discovery tasks. However, each of the Web services is an independent individual and can serve a kind of small requests. You can use the Web services for whatever goal you are pursuing. In other words, igfds obtains capabilities to finish many kinds of geospatial data processing tasks because of its service-oriented characteristics. All the functions igfds can perform are generally listed in this page We will introduce some commonly used functions in this section Projection Transformation (1) Open (2) Copy a proj4 line of a projection to the blank field shown in Fig Figure 3.64 The interface of projection transformation (3) Click transform to get EPSG number of the projection. Figure 3.65 The EPSG code of the inputted proj4 projection 36

40 3.4.2 Coordinates Projection Transformation (1) Open (2) Type some coordinates in the input text area, and also input the EPSG codes of the source and target projections (Fig. 3.66). Figure 3.66 Input your coordinates and the source & target projections EPSG codes (3) Click transform to get the coordinates under the target projection. Figure 3.67 The transformed coordinates 37

41 3.4.3 Time Conversion in Different Units To convert a time stamp from milliseconds to MM:SS:MS format: (1) Open (2) Type a millisecond value into the blank field in Fig Figure 3.68 The interface of time format convertion (3) Click convert to transform the millisecond value into a new format (Fig. 3.69). Figure 3.69 The inputted time in Hour/Min/Sec format Converting Raster to Vector To do vector quantization of a raster file: (1) Open (2) Copy the URL of your image into the field in Fig Figure 3.70 The interface transforming a raster file to a vector 38

42 (3) Click transform to get a vector file delineating the border of pixel blocks in the inputted image (Fig. 3.71). An example pair of vector and raster files is shown in Fig Figure 3.71 The result page of the transformation Figure 3.72 An image overlaid by its vector file Filtering Vector Features by Shape (1) Open (2) Type the URL of your vector file in the first field in Fig (3) Select the target shape type which you want to retain from the overall features. Currently the supported shape types include equilateral triangle, rectangle, circle and equilateral pentagon. (4) Set a threshold for the judgment of similarity between geospatial features and the target shape type. If the threshold is higher, fewer features will be retained; otherwise, more features will be retained. 39

43 (5) Answer Yes or No to the question whether you want to save the results permanently on the server. If you only download the results once, you are suggested to choose No and the results may be removed at the next time the system is being cleaned. (6) Click filter to get the features similar to the chosen target shape. An example vector file and its filtered features are displayed in Fig Figure 3.73 The interface of filtering geospatial features by shape Figure 3.74 The result page of filtering features by shape 40

44 Figure 3.75 The filtered features with the target shape as rectangle (red: retained features; light yellow: raw features) Combining a RGB Image to an One-layer Image At many scenarios, we need to integrate the three bands in a RGB image into one single layer. To do that in igfds: (1) Open (2) Type the URL of a RGB image into the field shown in Fig Figure 3.76 The interface of combining three bands in a RGB image into a single band image (3) Click transform to get the single band image. The transformation is reversible because each composition of r, g, b values relates to a unique value in the single band image and vice versa. Figure 3.77 The result page of comibing the RGB image into a single band image 41

45 3.4.7 Eliminating small polygons in an One-layer Image To remove the polygons whose areas are smaller than some value from a single band image: (1) Open (2) Input the URL of a single band image to the field shown in Fig Figure 3.78 The interface of eliminating small polygons in a single band image (3) Click transform to get an image without polygons whose area are smaller than some value (e.g. 20 pixels). The link of the new generated image is provided in the result page as shown in Fig Figure 3.79 The result page of eliminating small polygons in a single band image Viewing Vector and Raster files to an Online OpenLayers Map Users usually want to view and check vector and raster files before they make further processes. To view vector and raster files (projected) in igfds: (1) Open (2) Copy the URL of a vector file (e.g. feature.class.kml) to the first field shown in Fig (3) Click add to map to overlay the vector file onto an OpenLayers map and zoom to the vector extent. Two examples for adding vector file and raster file separately are shown in Fig and

46 Figure 3.80 The interface for viewing vector or raster files Figure 3.81 A KML file is overlaid on top of the OpenLayers map 43

47 Figure 3.82 A Geotiff image is overlaid on top of the OpenLayers map 44