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Remote sensing training manual +arcgis |
Remote sensing training manual +arcgis
These tools allow you to have geographical context when working with your video data. The video player includes standard video controls such as play, fast forward, rewind, step forward, step backward, jump to the beginning, or jump to the end of the video. You can zoom in to and roam a video while it is in play mode or in pause mode. Additional tools include capturing, annotating, and saving video bookmarks; capturing single video frames as images, and exporting video clips.
You can collect ground features of interest that are visible in the video player by marking them with a feature, which is also displayed on the map. Conversely, you can mark ground features in the map view, and the features are also displayed in the video player. You can save these points as a feature class and use them later for further analysis.
Video player with video footprint and point features displayed on the map. Creating video bookmarks is an important function for recording phenomena and features of interest when analyzing a video. You can collect video bookmarks in the different modes of playing a video, such as play, pause, fast-forward, and rewind. You can describe bookmarks in the Bookmark pane that opens when you collect a video bookmark.
You can only use videos that contain essential metadata in FMV. Professional-grade airborne video data collection systems generally collect the required metadata and encode it into the video file in real time. This data is readily input directly into the FMV application, either in live-streaming mode or from an archived file. Consumer-grade video collection systems often produce separate video data and metadata files that must be combined into a single FMV-compliant video file.
This process is performed in the software and is referred to as multiplexing. FMV provides the Video Multiplexer tool, which encodes proper metadata at the proper location of the video file to produce a single FMV-compliant video file. Each video and metadata file uses time stamps to synchronize the encoding of the proper metadata in the proper location in the video file.
The metadata is generated from appropriate sensors, such as GPS for x,y,z position, altimeter, and inertial measurement unit IMU , or other data sources for camera orientation.
The metadata file must be in the comma-separated values CSV format. The FMV metadata is used to compute the flight path of the video sensor, the video image frame center, and the footprint on the ground of the video image frames. All the MISB parameters that are provided will be encoded into the final FMV-compliant video, including more than 80 parameters, or a subset. One set of FMV parameters includes the map coordinates of the four corners of the video image frame projected to the ground.
If these four corner map coordinates are provided, they will be used. Otherwise, the tool will compute the video footprint from a subset of required parameters. When the metadata is complete and accurate, the tool will calculate the video frame corners, and thus the size, shape, and position of the video frame outline, which may then be displayed on a map. These 12 parameters comprise the minimum metadata required to compute the transform between video and map, to display the video footprint on the map, and to enable other functionality such as digitizing and marking on the video and the map.
A key element of this process is understanding the different parameters that come with the tool:. Understanding these parameters will allow you to make smart adjustments and get the most accurate output possible. Note that no deep learning approach will give you percent accurate results, but adjusting your model parameters and iterating through the process can optimize the accuracy of your model.
Below we will discuss the importance of each parameter a nd how to adjust the inputs based on your imagery and environment. The first parameter is the padding of the model. Padding is the border area from which the model will discard detections, as they tend to be of truncated buildings that span multiple tiles during inferencing. We stride over the padded region, so buildings that are discarded because they lie at the edge in one pass of the model inferencing, are detected in the second pass of the inferencing when they lie at the center of the tiles due to this striding.
This means that with the padding parameter being adjusted, the model will adjust the stride of each tile as it runs the inferencing workflow. For example, if we introduce a padding of 32 px pixels on a model that is inferencing px tiles, the model will stride the tile by 32 px inside the 4 edges of the tile.
If the centroid of the detected feature is within the padded tile, it will pass as a building in this example. I f you are new to deep learning , feel free to leave the default value of padding. In the graphic below, we are demonstrating how a padding of 64 px is treated while inferencing. For example, if the default is 32 px , try running the tool with paddings of 24 px and 16 px and compare the results.
Check the images below to see the output of a model run with a padding of 32 px in green vs 8 px in purple. Batch size is a term used in machine learning and refers to the number of image tiles the GPU can process at once while inferencing. The imagery is chopped up into tiles during inferencing, and the number of tiles the GPU can inference in one batch is called the batch size.
If you run into out-of-memory errors with the tool, you need to reduce the batch size. The batch size your computer can handle will depend on the GPU available in your machine. To determine the optimal batch size, you m ay need to run the tool a few times on a small geographical extent while monitoring your GPU metrics. Click the Classification Wizard button on the Imagery tab to open and dock the wizard.
The first page is the Configure page, where you set up your classification project. The parameters set here determine the steps and functionality available in the subsequent wizard pages. There are two options for the method you will use to classify your imagery. The outcome of the classification is determined without training samples.
Pixels or segments are statistically assigned to a class based on the ISO Cluster classifier. Pixels are grouped into classes based on spectral and spatial characteristics.
You provide the number of classes to compute, and the classes are identified and merged once the classification is complete. The outcome of the classification depends on the training samples provided. Training samples are representative sites for all the classes you want to classify in your image. These sites are stored as a point or polygon feature class with corresponding class names for each feature, and they are created or selected based on user knowledge of the source data and expected results.
All other pixels in the image are classified using the characteristics of the training samples. This is the default option. There are two options for the type of classification to use for both supervised and unsupervised classification. Classification is performed on a per-pixel basis, where the spectral characteristics of the individual pixel determines the class to which it is assigned. Characteristics of neighboring pixels are not considered in the pixel-based approach.
This is considered the more traditional classification method, and can result in a speckled effect in the classified image. Classification is performed on localized neighborhoods of pixels, grouped together with a process called segmentation. Segmentation takes into account both color and shape characteristics when grouping pixels into objects.
The objects resulting from segmentation more closely resemble real-world features and produce cleaner classification results.
A classification schema determines the number and types of classes to use for supervised classification. Schemas can be hierarchical, meaning there can be classes with subclasses. For example, you can specify a class of Forest with subclasses for Evergreen and Deciduous forests. A schema is saved in an Esri classification schema file. You can choose from the following options to specify the classification schema:. This is the workspace or directory that stores all of the outputs created in the Classification Wizard , including training data, segmented images, custom schemas, accuracy assessment information, and classification results.
All intermediate files created using the Classification Wizard are stored in the user temp directory. This is only an option if you selected Object based classification for Classification Type. If you have already created a segmented image, you can reference the existing dataset. Otherwise, you will create a segmented image as a step on the next page. If the segmented raster has not been created previously, it will be created before training the classifier.
This is a computer-intensive operation, and it may take a significant amount of time to create the segmented raster dataset. For large datasets, it is highly recommended that you create the segmented raster beforehand and specify it as an input when you configure your classification project. This is only an option if you selected Supervised for Classification Method.
You can create training samples using the Training Samples Manager pane in the Classification Tools drop-down list, or you can provide an existing training samples file. This can be either a shapefile or a feature class that contains your training samples, and it must correspond with the classification schema. The following field names are required in the training sample file:.
If you want to assess the accuracy of classified results, you need to provide a reference dataset. Reference data consists of features with a known location and class value, and it can be collected using a field survey, an existing class map or raster land base, or with higher-resolution imagery.
The results of your image classification will be compared with your reference data for accuracy assessment. The classes in your reference dataset need to match your classification schema. Reference data can be in one of the following formats: A raster dataset that is a classified image. A polygon feature class or a shapefile. The format of the feature class attribute table needs to match the training samples.
To ensure this, you can create the reference dataset using the Training Samples Manager tools. A point feature class or a shapefile. The format needs to match the output of the Create Accuracy Assessment tool.
If you are using an existing file and want to convert it to the appropriate format, use the Create Accuracy Assessment Points geoprocessing tool. Open source QGIS software does not limit which tools can be used. You could use the free trial of ET GeoWizards. QGIS is working on its geoprocessing framework which is already impressive. But in the end, you really are licensed to geoprocess in ArcGIS.
We all know you can engineer specialized analyses with plugins. Nearly of them. But what you may not have known is that ArcGIS has plugins too. There are paid and free solutions for almost any spatial problem you can think of. Esri has nailed every corner of the market including gardening. You have to understand how scalable and unique Esri is to solve your geospatial problem. ArcGIS raster-based tools are rock-solid.
The Spatial Analysis tools also offer specialized tools for groundwater, hydrology, and solar radiation. Other options are to filter reclass or extraction toolsets or simplify data generalization toolset. In QGIS, the raster calculator tool performs map algebra with a little less math and trigonometry functions.
QGIS has multiple ways to perform interpolation. GRASS r. QGIS wins for more filtering options. We could continue. But choose not to bore you. Have you ever tried to assemble furniture without the instruction manual? Practically impossible, right? When you run tools in the ArcGIS Geostatistics Toolbox, the instructions and output explanations are so clear that a child could understand the results. You know if your data is auto-correlated or not.
In QGIS, you need a good understanding of the tool beforehand. The exploratory regression tools in ArcGIS are well-made because the outputs allow users to connect statistics with their data.
This saves time for analysts. The columns are your statistics types average, minimum, variance, etc. Rows are categorical fields such as place names or watersheds. Add a value field and push calculate. Voila, your pivot table is generated. When you can make difficult concepts straight-forward, you become a winner in my book. And ArcGIS is best at teaching geostatistics.
Silently, satellites collect data of our planet with active and passive remote sensing. Satellites like Sentinel 2 and Landsat 8 are the exciting ones making data more ubiquitous to GIS analysts.
Some of these are like hand tools , like a chisel. Others are like power tools , like an electric drill. When ArcGIS 10 added the image analysis toolbar, it instantly provided remote sensing analysts with the necessary tools to create samples and perform unsupervised and supervised classification.
Pansharpen, perform NDVI , orthorectify and interactively change the brightness, contrast and transparency. It still gets the job done. A Guide to Earth Observation. In ArcGIS, flick on the network analyst switch. Add your data to a network data set. Building a clean topological road dataset is the challenge. When you run redundant tasks as a scheduled model, you can sit at home in your bathrobe all day long and still get work done.
You string together sets of tools in Model Builder to automate processes. Drop tools in your Model Builder Diagram and connect them. Automate everything. You will also be using a bunch of other modules as needed for different projects. It can be difficult to figure out what to use and where it all is. But QGIS is a viable option to create cartographic masterpieces.
It acts almost like another application. ArcGIS layout view is how to set up map templates and export map products. The ArcGIS layout view is practical. It has tools to pinpoint your labels, set up mapbooks, and link data frames with easy extent rectangles. ArcGIS is loaded with stunning symbology on startup. We like the symbology by discipline transportation, real estate, soils, weather, etc. The existing symbology in ArcMap is beautiful, useful and plentiful.
QGIS misses the beat on pre-existing choices. Life would be easier in QGIS if it came equipped with symbology like railways and hatched polygons. Keep in mind: you can download and load them to your symbology palette.
It has more blending options than a symbology bakery: lighten, screen, dodge, addition, darken, multiply, burn, overlay, soft light, hard light, and difference. You can create simple gradients with two or multiple colors.
Add the different types-linear, radial, conical. You no longer have to write an RGB code again. QGIS has some really advanced symbology. ArcGIS is practical and puts symbols in the hands of the cartographer. Both are winners in my books.
Gain full control of exactly how and where you want to label features. Set label location and scale dependency. Curved and parallel labeling is easy in ArcGIS. The drawing toolbar is how to control annotation groups in ArcGIS. Make a separate toolbar for annotations. But with a little practice, you can control which annotation group labels belong to. It is your complete arsenal for automated map production. The index layer is used to create each page. The Cartographer Toolbox is how to create strip maps.
If your map spans multiple projections, use the Calculate UTM zone tool. For each geometry in the coverage layer, a new output will be generated.
Fields associated with this geometry can be used within text labels. A page will be generated for each feature. ArcGlobe and ArcScene are stand-alone programs using the 3D analyst extension. These applications give you a chance like no other to enter a world in 3D.
ArcScene is for small study area scenes. Extrude objects with amazing vertical exaggeration. Z-factor is your friend. ArcGlobe is for data that spans the whole globe.
Make your data come to life. Perform wicked fly-throughs. QGIS lacks decent 3D support. The Qgis2threejs plugin can catapult you in three dimensions.
The Qgis2threejs plugin exports terrain data, map canvas image and vector data to your web browser. Web maps are on the uptrend.
The news industry, governments, and businesses are using web maps because they tell a story. Web mapping is easy in ArcGIS. A cool trend are ArcGIS story maps because everyone has a story to tell. With ArcGIS, you can harness the power of maps to tell yours. Watch polar ice caps melt over time. Display global time-aware weather patterns. ArcGIS makes it an easy process to go from static to dynamic with its animation toolbar. When you have a time-enabled field, scroll the time slider left-to-right.
Watch your data change over time. A little preparation is necessary but nothing too painful. Export as an AVI and impress your boss. Using time controls, you animate vector features based on time attributes.
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