Extended Tools In Remote Sensing_book 5

Multispectral images are satellite images that are made up of three or more bands of data. These bands capture different wavelengths of light, allowing for the analysis of various features on the Earth's surface. By combining multiple bands, multispectral images can provide valuable information about vegetation health, land use, and other environmental factors. This makes them a useful tool for applications such as agriculture, forestry, and urban planning.

Histogram stretch is the correct answer because it refers to the process of reorganizing the histogram range in an image. This technique is used to enhance the contrast and improve the visibility of different features in the image. By stretching the histogram, the full range of pixel values is utilized, which helps to emphasize or de-emphasize specific land features in the image.

A "clipped" portion of an original image data layer is called a subset image. This means that a specific portion or region of the original image has been selected or clipped out for further analysis or use. It can be used to focus on a particular area of interest or to reduce the size of the image for easier handling and processing.

The process of examining infrared satellite images to determine regions of vegetation health and stress is called the Normalized Difference Vegetation Index (NDVI). NDVI is a numerical indicator that uses the difference between the visible and near-infrared light reflected by vegetation to assess its health and vitality. By measuring the NDVI values of different areas, it is possible to identify regions with healthy vegetation and areas that may be experiencing stress or degradation.

Sharpening is the correct answer because it refers to the process of enhancing the clarity and detail of an image by applying a filter. This filter increases the contrast between adjacent pixels, making the edges and details of the image more pronounced and defined. Sharpening can be used to improve the overall sharpness and quality of an image, making it appear clearer and more focused.

Clicking a single pixel rather than dragging a box when using the Seed Tool typically causes a smaller polygon to be drawn because the number of pixels used to build the pixel value range would be smaller. When clicking a single pixel, only that pixel's value is considered for the polygon, whereas dragging a box includes multiple pixels and their values to determine the polygon. This results in a smaller range of pixel values being used, leading to a smaller polygon being drawn.

Supervised classification is a type of analysis that allows you to find all areas of an image that have the same pixel characteristics. This technique involves training a model using labeled samples of different classes and then applying that model to classify the remaining pixels in the image. It is a useful tool for identifying and mapping specific features or land cover types in remote sensing imagery.

Change detection is the process of examining two images or feature layers of the same area over a period of time to identify and analyze the differences or changes that have occurred. It is used in various fields such as remote sensing, environmental monitoring, and urban planning to track and understand changes in land use, vegetation cover, and other features. By comparing the images or layers, change detection allows for the identification of areas that have undergone significant changes, providing valuable information for decision-making and analysis.

Smoothing refers to the process of applying a filter to an image in order to reduce the amount of speckle. This technique helps to remove noise and blur the image slightly, resulting in a smoother appearance. It is commonly used in image processing to improve the quality and clarity of images by reducing unwanted artifacts.

When performing orthorectification, geocorrection properties need to be entered with regard to the camera used for data collection because cameras or sensors have a certain amount of error or discrepancy related to them. This error or discrepancy can affect the accuracy of the orthorectification process. By taking into account the specific properties and characteristics of the camera or sensor used, such as its focal length, distortion, and sensor size, the orthorectification process can correct for these errors and discrepancies, resulting in more accurate geocorrected imagery.

Mosaicking refers to the process of combining multiple georeferenced image data layers into a single data layer. This can be done by aligning and blending the individual layers to create a seamless and continuous image. Mosaicking is commonly used in remote sensing and GIS applications to create composite images that provide a more complete and accurate representation of a specific area or region.

The process of reversing the pixel values of an image to create a negative of the original image is called an "invert stretch". This means that the darkest pixels in the original image become the lightest in the inverted image, and vice versa. This technique is commonly used in image processing to enhance certain features or create visual effects.

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A larger seed radius in Seed Tool Properties typically causes a larger polygon to be drawn because it specifies the number of pixels that can be used to establish the range of pixel values that will be used to draw the polygon. This means that a larger seed radius will include more pixels in the range, resulting in a larger area being covered by the polygon.

A resolution merge can greatly enhance an image by using the real color of a low-resolution image to enhance a monochromatic high-resolution image. This means that the low-resolution image's color information is used to add detail and improve the overall quality of the high-resolution image, resulting in a more visually appealing and accurate representation.

When only a photograph negative is available, using an invert stretch on an image would be needed. This is because a photograph negative has reversed colors, with light areas appearing dark and vice versa. By applying an invert stretch, the colors can be corrected and the image can be restored to its original appearance.

Unsupervised categorization analysis would be needed to identify land cover types in a county. This is because land cover types can vary greatly and may not be easily identifiable or distinguishable without any prior knowledge or labeled data. By using unsupervised categorization analysis, the system can automatically group similar land cover types together based on patterns and similarities in the data, helping to identify and classify different types of land cover in the county.

Change detection analysis is used to identify changes in a specific area over time. In this case, the best example of when change detection analysis would be needed is to identify the loss of wetlands in a county. By comparing satellite images or other data from different time periods, it is possible to detect and quantify any changes in the extent of wetlands in the county. This analysis can help in monitoring and managing the conservation of wetland ecosystems.

Supervised categorization analysis would be needed to identify the locations of all wetlands in a county because this task requires the classification of specific areas as wetlands. This type of analysis involves training a model on labeled data, where the model learns to recognize the characteristics and patterns that define wetlands. Once trained, the model can then be used to predict and identify the locations of wetlands in the county based on the input data.

Orthorectification differs from rectification because it uses elevation data to account for the natural variation in the Earth's surface. Rectification, on the other hand, is performed on image data and does not take into consideration the elevation data.

When orthorectifying an image with a geographic extent that extends beyond one quarter quadrangle of area, you may need to mosaic the digital elevation models (DEMs) to use for the analysis because the geographic extent of the elevation data must be at least that of the study area. Mosaicking the DEMs allows for a larger coverage area, ensuring that the elevation data covers the entire study area. If the elevation data does not extend beyond the study area, there may be gaps or missing information in the orthorectified image, which can affect the accuracy and reliability of the analysis.

Feature extraction analysis would be needed to identify the extent of an oil spill in the ocean. This is because feature extraction involves identifying and extracting relevant features or characteristics from a dataset. In the case of an oil spill, features such as the size, shape, and spread of the spill would need to be extracted and analyzed to determine its extent. This analysis would help in understanding the severity of the spill, its potential impact on the environment, and in planning appropriate response and cleanup measures.

The seed tool is used for feature extraction in image analysis. This tool allows users to manually select specific areas or points of interest in an image, which can then be used as reference points for further analysis or classification. By selecting seeds, the tool helps to identify and extract features that are important for the analysis of the image.

The process of dividing pixel values in an image into a specified number of classes is called change detection. Change detection involves comparing two or more images taken at different times and identifying the differences or changes that have occurred. By dividing the pixel values into classes, it becomes easier to analyze and interpret the changes in the image. This process is commonly used in remote sensing and image analysis to monitor and study various phenomena such as land cover changes, urban development, and environmental changes.