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MindMap Gallery Remote sensing digital image processing

Remote sensing digital image processing

This is a mind map about remote sensing digital image processing. The main contents include: Chapter 11: Remote sensing digital image classification, Chapter 8: Image enhancement, Chapter 12: Remote sensing cartographic expression, Chapter 10: Feature extraction and selection, Chapter 9: Targets of Interest and Object Extraction, Chapter 7: Image Denoising, Chapter 6: Geometric Correction, Chapter 5: Radiation Correction, Chapter 4: Transform Domain Processing Method, Chapter 3: Space Domain processing methods, Chapter 2: Digital image storage and processing, Chapter 1: Digital image basics.

Edited at 2024-10-30 09:36:18

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Remote sensing digital image processing

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Remote sensing digital image processing

Chapter 1: Digital Image Basics

Overview

digital image

simulated image

sampling

Quantify

Digital image acquisition

spatial resolution

pixel size

Number of line pairs

instantaneous field of view

radiometric resolution

Grayscale

n bit 2 to the nth power gray level

Spectral resolution

time resolution

digital image features

spatial distribution characteristics

spatial location

shape

size

numerical statistical characteristics

gray value

Grayscale

Histogram purpose

Image acquisition quality evaluation

Choice of boundary threshold

Noise type judgment

Digital image output

Output resolution

Corresponding spatial resolution

Grayscale resolution

Corresponding radiometric resolution

Quality characteristics

color space model

Corresponding spectrum, time resolution

informative features

Digital image types

black and white

Grayscale

false color

color

Chapter 2: Digital Image Storage and Processing

Storage of information on computers

Large end

Basic information stored in image files

header file

decoding order

The number of rows and columns of the image

Image data type

The number of bands in the image

image offset

Multi-band data storage method

BSQ

BIP

BIL

Common image file storage formats

open storage format

Header files and data files are stored separately

Common: ENVI-format hdr

closed storage format

TIFF

GeoTIff

HDF-EOS

IMG

BMP

JPEG

PSD

CDR

Chapter 3: Processing Methods of Spatial Domain

Numerical operations

Single band operation

Point arithmetic

linear point arithmetic

Piecewise linear point operations

Nonlinear point operations

Neighborhood operations

Convolution operation

neighborhood statistics

Diversity

density

mode

few

Sum

mean

standard deviation

maximum value

minimum value

rank

multi-band operation

algebraic operations

Section operation

Set operations

spatial operations

Image cropping

image mosaic

Band operation

Band extraction

band overlay

Logical operations

Reverse

AND operation

OR operation

XOR operation

mathematical morphological operations

binary morphology

corrosion

Expansion

Open operation

closed operation

grayscale morphology

corrosion

Expansion

Open operation

Closed calculation

Chapter 4: Transform domain processing method

Principal component analysis

Basic principles

Principal component exchange in remote sensing image processing

application

image compression

Image denoising

image enhancement

image fusion

Feature extraction

minimal noise separation exchange

basic principles

Principal component exchange in remote sensing image processing

application

image compression

image enhancement

image fusion

Feature extraction

tassel exchange

Basic principles

Tassel cap exchange in remote sensing image processing

application

image compression

image enhancement

image fusion

Feature extraction

independent component analysis

Basic principles

Independent component analysis in remote sensing image processing

application

Image denoising

Feature extraction

Fourier transform

Basic principles

One-dimensional discrete Fourier transform

Fourier transform in remote sensing image processing

application

Image denoising

image enhancement

Feature extraction

Internal: low frequency, gently changing parts; External: high frequency, edge noise and other parts with steep changes.

Limitations of Fourier exchange and window Fourier transform

Chapter 5: Radiation Correction

Overview

Radiation distortion

radiometric correction

atmospheric radiation

radiance value

radiometric calibration

sensor calibration

Relative radiometric calibration

Absolute radiometric calibration

sun altitude angle

sun azimuth

Observation zenith angle

Observation azimuth

transparency

radiance

Lambert merchant

Lambertian

pass rate

Calibration parameter acquisition

Laboratory calibration

On-board calibration

Site calibration

atmospheric correction

absolute atmospheric correction

Based on physical model

Based on radiative transfer model

LOWTRAR model

MOOTRAR model

ATCOR model

es model

Based on a simplified radiative transfer transfer model

dark pixel method

Based on statistical models

empirical linear method

Relative atmospheric correction

Based on statistical models

internal average relative reflectance method

flat field method

Logarithmic residual method

constant goal method

Rectangular circle matching method

statistical model

physical model

terrain correction

terrain correction method

Band-based method

DEM-based methods

Statistical-empirical models

Teilet-return to proofreading

b correction

normalized model

Two stage correction

Lambertian reflection model

cosine correction

C correction

SCS correction

SCS C Correction

Dymond-shepherd correction

Non-Lambertian reflection model

Minnaert correction

Ekstrand correction

Minnaert-SCS correction

Hypersphere-based method

Hyperspherical direction cosine transformation correction

cosine correction method

c correction method

Sun altitude angle correction

Chapter 6: Geometric Correction

Overview

geometric distortion

Geometric precision and fine correction

Principles of geometric correction

Coordinate sampling

ground control point

Resampling

nearest neighbor method

bilinear interpolation

cubic convolution method

Geometric correction steps

Establish a unified coordinate system and map projection for the distorted image and the reference image

Select ground control points, and according to the GCP selection principle, search for pairs of ground control points with the same features on the distorted image and the reference image.

Select calibration model

Choosing an appropriate resampling method

Accuracy analysis of geometric correction

Geometric correction type

Image-to-image geometric correction

Image to map geometric correction

Geometric correction with geolocation information

orthorectification

method

Strict physical model

collinear equation model

Affine transformation model

general empirical model

polynomial model

direct linear model

rational function model

neural network model

Image automatic matching

Image matching elements

Image matching performance

Image matching method

Grayscale-based matching method

feature-based matching methods

Projective transformation

Chapter 7: Image Denoising

Overview

external noise

internal noise

Common noise types and their identification

Random noise types and their identification

Gaussian noise

Rayleigh noise

gamma noise

Exponentially distributed noise

uniformly distributed noise

impulse noise

Random noise type identification

Periodic noise and its identification

Spatial domain noise removal

mean filter

median filter

edge preserving smooth filtering

mathematical morphological noise removal

Transform domain denoising

Fourier transform

ideal filter

ideal low pass filter

ideal band stop filter

ideal notch filter

Butterworth filter

Gaussian filter

Wavelet transformation

High frequency coefficients are set to zero

Wavelet threshold method

Other transformations

Chapter 8: Image Enhancement

Spatial domain image enhancement

point budget

Grayscale transformation

linear transformation

Piecewise linear transformation

inverse operation

Power transformation

Logarithmic and antilog transformations

Adjustment of Round Round ·

Square circle matching

Square circle equalization

rectangular circle

Cumulative rectangular circle

Grayscale probability square circle

cumulative probability square circle

Neighborhood operations

unsharp mask

differential operator

First order differential operator

One-way differential operator

ROBERTS cross differential operator

SOBE differential operator

PREWITT differential operator

second order differential operator

laplacian differential operator

WALLIS differential operator

Grayscale morphology gradient operation

Dilation-corrosion gradient, that is, the arithmetic difference between the dilation image and the corrosion image

corrosive gradient

dilational gradient

Transform domain image enhancement

Fourier transform

high frequency enhancement

high pass filter

ideal high pass filter

Bartworth Qualified filter

Gaussian high pass filter

bandpass filter

Homogenic filter

Basic steps

original image

Logarithmic transformation

Fourier transform

filter

Inverse Fourier Transform

antilog transformation

Enhance images

Wavelet transform

color space transformation

Other transformations

Pseudo color processing

image fusion

Algebraic operations in space domain

Brovey transformation method

PBIM fusion algorithm

SFIM fusion algorithm

spatial domain substitution method

HSI fusion algorithm

Principal component transform fusion method

Wavelet transform fusion method

Chapter 9: Targets of Interest and Object Extraction

Image segmentation

threshold method

Basics of threshold method processing

global threshold

uniformity measure

maximum class spacing method

maximum class variance method

maximum entropy method

local adaptive threshold

boundary segmentation method

edge detection

Edge detection based on differential operators

Comprehensive edge detection

LoG operator

Canny operator

Edge detection based on morphological gradient

edge connection

region extraction method

region growing method

Region Split and Merge Method

Morphological watershed segmentation

Binary image processing

Basic concepts

Four nearest neighbors and eight nearest neighbors

Four-connection and eight-connection

interior points and boundary points

Hole filling and debris elimination

Object extraction

label

Boundary extraction

Chapter 10: Feature Extraction and Selection

Spectral feature extraction

spatial feature extraction

texture features

shape characteristics

basic shape characteristics

perimeter

area

Firmness

Shape factor

Minimum external matrix description

Minimum circumscribed ellipse description

Roundness

aspect ratio

Placement angle

Other parameter descriptions

spatial relationship characteristics

Feature options

Feature selection process

subset generation

subset evaluation

Evaluation aborted

Result verification

Attribute evaluation criteria

Relevance criteria

independence criterion

distance measure

Relevance measure

Information measure

consistency measure

Comprehensive measure

Feature selection based on prior knowledge

Feature combination

Chapter 11: Remote Sensing Digital Image Classification

Background knowledge

Classification knowledge

type

Leveraging prior knowledge

First supervised classification

After unsupervised classification

Is it based on statistical characteristics of data?

Yes Statistical Decision

No decision tree classification

The pixel division probability is 100%

Yes Hard classification

No soft classification

Whether the classification object is a pixel

Yes Then pixel classification

No object-oriented classification

process

Understanding the purpose of classification and background of the study area

Data extraction

Pre -processing

Classification category determination and interpretation flag establishment

Training sample selection and evaluation

Feature extraction and selection·

Choice of classification method

Image classification

Classification post-processing

Accuracy evaluation

Classification method

Supervised classification

Selection of training samples

Source of training samples

Number of training samples required

Distribution of training samples

Evaluation of training samples

icon method

mean chart method

Rectangular circle method

Feature space multidimensional graph method

statistical method

Convert dispersion

JEFFRIES-MATUSITA distance method

Choice of classification method

parallel box algorithm

distance judgment method

maximum likelihood method

Common classification algorithms for hyperspectral remote sensing data

Spectral angle mapping

Spectral information dispersion

binary encoding

neural network algorithm

Support vector machine classification algorithm

Characteristics of supervised classification

unsupervised classification

Category setting for unsupervised classification

Common classifiers

K-means algorithm

ISODATA algorithm

Characteristics of supervised classification

Decision tree classification

object-oriented

Object extraction

Object classification

Classification post-processing

major/minor analysis

Clustering

Filter processing