ENVI EX is the latest addition in the ENVI for image processing and analysis software products. ENVI is the choice of scientists and professional image for extracting accurate scientific information from the image. Now ENVI EX provides, scientifically accurate process proves that ENVI is known in the workflow step-by-step revolutionary quick and easy guide you through advanced image processing tasks, regardless of the level of experience using it.
ENVI EX is a pretty amazing device with dynamic display that allows to quickly see the results of remote sensing or image manipulation, vector, and an explanation. Tampilanya provides quick access to the device displays the common tools such as contrast, brightness, sharpen, and color transparency in the image. Can work with multiple layers of data at one time and in a single window, using the Layer Manager and Data Manager to track multiple datasets, and "punch through " layer to see and work with one layer or another layer in the same window. In addition, ENVI EX can do reproject and resample the image and vector on-the-fly. ENVI EX works seamlessly with layers and features of ESRI and allows you to create professional map presentation.
For classification. You can optionally implement a subset of the space or spectral, and / or masks for the input. Input classification requires a minimum of two bands. This type is compatible Iput ENVI, TIFF, NITF, JPEG 2000, JPEG, image Erdas, ESRI raster and raster geodatabase.
For classification. You can optionally implement a subset of the space or spectral, and / or masks for the input. Input classification requires a minimum of two bands. This type is compatible Iput ENVI, TIFF, NITF, JPEG 2000, JPEG, image Erdas, ESRI raster and raster geodatabase.
Classification guided and not guided can be done quickly, easily and smoothly. Supervised Classificationbecomes more easy with the feature extraction / image segmentation of the ENVI EX . Feature extraction method first developed in the graphic design software to "copy" feature / object seen in the picture, and is currently ENVI and Idrisi also have applied this method to extract the information contained in the satellite image. Even ENVI EX has been combined (feature extraction) with K-nearest neighborhood method to perform supervised classification directly. Feature extraction is very helpful in the work of LULC classification using satellite images, because this method can identify the LULC classes not only pixel-based, but also take into account other components in the interpretation, such as shape and texture of the features / objects that appear in the image. Kedetailan level of precision and can be adjusted as needed. Other data such as elevation, slope, or NDVI can be added as supporting data in the process of feature extraction