6 Image Processing and Virtual Mosaic

This chapter describes advanced image processing capabilities, including GCP georeferencing, reprojection, rectification, orthorectification, warping, image scaling, stretching, filtering, masking, segmentation, NDVI computation, Tasseled Cap Transformation, image appending, bands merging, and large-scale advanced image mosaicking.

This chapter also describes the concept and application of virtual mosaic within the context of a large-scale image database and on-the-fly spatial queries over it.

The operations in this chapter are most commonly used to process geospatial images, particularly raw satellite imagery and airborne photographs. However, those operations, just like the GeoRaster raster algebra, apply to all raster data types.

This chapter contains the following major sections.

• Advanced Georeferencing
  In addition to spatial referencing capability, advanced georeferencing capabilities are available.
• Image Reprojection
  Image reprojection is the process of transforming an image from one SRS (spatial reference system, or coordinate system) to another.
• Image Rectification
  Most raster data originating from remote sensors above the ground is usually subject to distortion caused by the terrain, the view angles of the instrument, and the irregular shape of the Earth. Image rectification as explained in this section is the process of transforming the images to reduce some of that distortion.
• Image Orthorectification
  Orthorectification is a rectification transformation process where information about the elevation, the terrain, and the shape of the Earth is used to improve the quality of the output rectified image. Oracle GeoRaster supports single image orthorectification with average height value or DEM.
• Image Warping
  Image warping transforms an input GeoRaster object to an output GeoRaster object using the spatial reference information from a specified SDO_GEOR_SRS object.
• Image Affine Transformation and Scaling
  Affine transformation is the process of using geometric transformations of translation, scaling, rotation, shearing, and reflection on an image to produce another image.
• Image Stretching, Normalization, Equalization, Histogram Matching, and Dodging
  The color and contrast of images can be enhanced to improve their visual quality. The SDO_GEOR_IP package (“IP” for image processing) provides a set of subprograms for image enhancement, including performing image stretching, image normalization, image equalization, histogram matching, and image dodging.
• Image Filtering
  Image filtering is the process of applying a convolution filter on an image to achieve a specific purpose. For example, applying a low-pass filter on an image can smooth and reduce noise in an image, while applying a high-pass filter on an image can enhance the details of the image or even detect the edges inside the image.
• Image Segmentation
  Segmentation is a simple type of classification algorithm, and can be useful in classifying certain types of images into larger ground feature categories, such as land, cloud, water, or snow.
• Image Pyramiding: Parallel Generation and Partial Update
  Image pyramiding is one of the most commonly used processes in building large-scale image databases.
• Bitmap Pyramiding
  Bitmap pyramiding can produce high-quality pyramids in certain cases where traditional pyramiding is not adequate.
• Vegetation Index Computation
  In remote sensing, the Normalized Difference Vegetation Index (NDVI) is a widely used vegetation index, enabling users to quickly identify vegetated areas and monitor the growth and "condition" of plants.
• Tasseled Cap Transformation
  Tasseled Cap Transformation (TCT) is a useful tool for analyzing physical ground features using remotely sensed imagery.
• Image Masking
  To perform image masking, an application can query the GeoRaster database for bitmap masks, retrieve the desired bitmap mask or masks, and apply the masking operation on the target GeoRaster object for the purpose of displaying the object or performing some other processing.
• Band Merging
  For image classification, time series analysis, and raster GIS modeling, multiple bands or layers of different GeoRaster objects may need to be merged into a single GeoRaster object.
• Image Appending
  You can append one image to another image when the two images have the same number of bands.
• Large-Scale Image Mosaicking
  A large geospatial area typically consists of many smaller aerial photographs or satellite images. Large-scale image mosaicking can stitch these small geospatial images into one large image to get a better view of the whole spatial area.
• Virtual Mosaic
  A virtual mosaic treats a set of GeoRaster images as one large virtually mosaicked image.
• Image Serving
  Serving of image and raster data to clients or applications is supported through many features of the GeoRaster PL/SQL and Java APIs.