Titlebook: Remote Sensing Digital Image Analysis; John A. Richards Textbook 2022Latest edition The Editor(s) (if applicable) and The Author(s), under

搬運(yùn)工

Geometric Processing and Enhancement: Image Domain Techniques, pixel brightness values. Means are also presented for describing geometric properties such as texture and spatial correlation. Image morphological analysis is covered as a further example of template-based processing, in which image objects can be refined.

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中古

按等級(jí)

Geometric Processing and Enhancement: Image Domain Techniques,etail, all of which are illustrated by examples. Those operations are shown to depend upon processing a neighbourhood of pixels about a central pixel of interest; this is identified as the spatial convolution operation. Most commonly, spatial convolution and thus the operations of smoothing and shar

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Spatial Domain Image Transforms,ms are then covered in continuous and discrete forms, leading to the definition of the Fourier transform of an image, and how it can be evaluated. Convolution, including in two dimensions, is then introduced both as a basis for developing sampling theory and for understanding the theoretical origin

進(jìn)步

Supervised Classification Techniques,od decision rule and minimum distance classification, and progressing to the support vector classifier and neural networks, including the convolutional neural networks and recurrent neural networks used in deep learning. Emphasis is given to the development and properties of each of the algorithms,