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遙感與圖像處理 版權信息
- ISBN：9787118094466
- 條形碼：9787118094466 ; 978-7-118-09446-6
- 裝幀：一般膠版紙
- 冊數：暫無
- 重量：暫無
- 所屬分類：>
遙感與圖像處理 本書特色
周紹光、楊英寶、陳仁喜、徐佳、蘇紅軍編著的《遙感與圖像處理(普通高等教育十二五測繪科學與技術系列教材)》以英語的形式闡述了遙感的基礎知識和遙感圖像處理的典型方法。主要內容包括：遙感的定義，典型遙感系統的組成；常用的遙感傳感器及平臺，遙感影像的分辨率，多光譜掃描和熱成像原理，圖像幾何失真的原因及校正方法；微波遙感的基礎知識介紹；遙感影像處理、分析和解譯的經典方法及部分先進的技術，利用erdas和matlab進行影像處理和分析的具體步驟；遙感在土地利用、土地覆蓋及城市熱環境等方面的應用。 除了作為課程教材之外，本書還可以作為研究人員學習專業英語的參考書。
遙感與圖像處理 內容簡介
本書內容主要包括：遙感的定義、典型遙感系統的組成；常用的遙感傳感器及平臺，遙感影像的分辨率，多光譜掃描和熱成像原理，圖像幾何失真的原因及校正方法；微波遙感的基礎知識介紹；遙感影像處理、分析和解譯的經典方法及部分先進的技術，利用ERDAS和MATLAB進行影像處理和分析的具體步驟；遙感在土地利用、土地覆蓋及城市熱環境等方面的應用。
遙感與圖像處理 目錄
chapter 1 introuduction to fundamentals
1.1 definition of remote sensing
1.2 electromagnetic radiation
1.3 the electromagnetic spectrum
1.4 interactions with the atmosphere
1.5 radiation-target interactions
1.6 passive vs. active sensing
1.7 characteristics of images
exercises
chapter 2 satellites and sensors
2.1 on the ground, in the air, in space
2.2 satellite characteristics: orbits and swaths
2.3 spatial resolution, pixel size, and scale
2.4 spectral resolution
2.5 radiometric resolution
2.6 temporal resolution
2.7 cameras and aerial photography
2.8 muhispeetral scanning
2.9 thermal imaging
2.10 geometric distortion in imagery
2.11 weather satellites/sensors
2.12 land observation satellites/sensors
2.13 marine observation satellites/sensors
2.14 other sensors
2.15 data reception, transmission, and processing
exercises
chapter 3 microwave remote sensing
3.1 introduction
3.2 radar basics
3.3 viewing geometry and spatial resolution
3.4 radar image distortions
3.5 target interaction and image appearance
3.6 radar image properties
3.7 advanced radar applications
3.8 radar polarimetry
3.9 airborne versus spaceborne radars
3.10 airborne and spaceborne radar systems
3.10.1 airborne radar systems
3.10.2 spaceborne radar systems
chapter4 image interpretaion&analysis
4.1 introduction
4.2 elements of visual interpretation
4.3 pre-processing
4.3.1 radiometric corrections
4.3.2 correction of geometric distortion
4.4 image subsetting and mosaicking
4.4.1 image subsetting
4.4.2 image mosaicking
4.5 image enhancement
4.5.1 image histogram
4.5.2 density slicing
4.5.3 linear enhancement
4.5.4 piecewise linear enhancement
4.5.5 look-up table
4.5.6 nonlinear stretching
4.6 spatial filtering
4.6.1 neighborhood and connectivity
4.6.2 kernels and convolution
4.6.3 image smoothing
4.6.4 median filtering
4.6.5 edge-detection templates
4.7 multiple-image manipulation
4.7.1 band ratioing
4.7.2 vegetation index
4.8 image transformation
4.8.1 pca
4.8.2 tasseled cap transformation
4.8.3 his transformation
4.9 image filtering in frequency domain
4.10 fundamentals of classification
4.10.1 spectral class versus information class
4.10.2 distance in the spectral domain
4.11unsupervised classification
4.11.1 moving cluster analysis
4.11.2 iterative self-organizing data analysis
4.11.3 agglomerative hierarchical clustering
4.11.4 histogram-based clustering
4.12supervised classification
4.12.1 procedure
4.12.2 per-pixel image classifiers
4.13 unsupervised and supervised classification
4.14 other methods for classification
4.14.1 mean shift clustering
4.14.2 fuzzy image classification
4.14.3 neural network
4.14.4 decision tree
4.15 data integration and analysis
exercises
chapter 5 applications
5.1 introduction
5.2 land use & land cover ( rural / urban)
5.2.1 basic concepts
5.2.2 change detection steps
5.2.3 common satellite and sensor in lulc research
5.2.4 case study
5.3 urban thermal environment
5.3.1 introduction
5.3.2 case study-yangtze river delta
exercises
chapter 6 erdas user' s guide
6.1 introduction to erdas
6.2 getting started
6.3 viewer
6.4 image enhance
6.5 image rectification
6.6 unsupervised classification
6.7 supervised classification
1 matrix indexing
2 function imadjust
3 logarithmic and contrast-stretching transformation
4 generating and plotting image histog
1.1 definition of remote sensing
1.2 electromagnetic radiation
1.3 the electromagnetic spectrum
1.4 interactions with the atmosphere
1.5 radiation-target interactions
1.6 passive vs. active sensing
1.7 characteristics of images
exercises
chapter 2 satellites and sensors
2.1 on the ground, in the air, in space
2.2 satellite characteristics: orbits and swaths
2.3 spatial resolution, pixel size, and scale
2.4 spectral resolution
2.5 radiometric resolution
2.6 temporal resolution
2.7 cameras and aerial photography
2.8 muhispeetral scanning
2.9 thermal imaging
2.10 geometric distortion in imagery
2.11 weather satellites/sensors
2.12 land observation satellites/sensors
2.13 marine observation satellites/sensors
2.14 other sensors
2.15 data reception, transmission, and processing
exercises
chapter 3 microwave remote sensing
3.1 introduction
3.2 radar basics
3.3 viewing geometry and spatial resolution
3.4 radar image distortions
3.5 target interaction and image appearance
3.6 radar image properties
3.7 advanced radar applications
3.8 radar polarimetry
3.9 airborne versus spaceborne radars
3.10 airborne and spaceborne radar systems
3.10.1 airborne radar systems
3.10.2 spaceborne radar systems
chapter4 image interpretaion&analysis
4.1 introduction
4.2 elements of visual interpretation
4.3 pre-processing
4.3.1 radiometric corrections
4.3.2 correction of geometric distortion
4.4 image subsetting and mosaicking
4.4.1 image subsetting
4.4.2 image mosaicking
4.5 image enhancement
4.5.1 image histogram
4.5.2 density slicing
4.5.3 linear enhancement
4.5.4 piecewise linear enhancement
4.5.5 look-up table
4.5.6 nonlinear stretching
4.6 spatial filtering
4.6.1 neighborhood and connectivity
4.6.2 kernels and convolution
4.6.3 image smoothing
4.6.4 median filtering
4.6.5 edge-detection templates
4.7 multiple-image manipulation
4.7.1 band ratioing
4.7.2 vegetation index
4.8 image transformation
4.8.1 pca
4.8.2 tasseled cap transformation
4.8.3 his transformation
4.9 image filtering in frequency domain
4.10 fundamentals of classification
4.10.1 spectral class versus information class
4.10.2 distance in the spectral domain
4.11unsupervised classification
4.11.1 moving cluster analysis
4.11.2 iterative self-organizing data analysis
4.11.3 agglomerative hierarchical clustering
4.11.4 histogram-based clustering
4.12supervised classification
4.12.1 procedure
4.12.2 per-pixel image classifiers
4.13 unsupervised and supervised classification
4.14 other methods for classification
4.14.1 mean shift clustering
4.14.2 fuzzy image classification
4.14.3 neural network
4.14.4 decision tree
4.15 data integration and analysis
exercises
chapter 5 applications
5.1 introduction
5.2 land use & land cover ( rural / urban)
5.2.1 basic concepts
5.2.2 change detection steps
5.2.3 common satellite and sensor in lulc research
5.2.4 case study
5.3 urban thermal environment
5.3.1 introduction
5.3.2 case study-yangtze river delta
exercises
chapter 6 erdas user' s guide
6.1 introduction to erdas
6.2 getting started
6.3 viewer
6.4 image enhance
6.5 image rectification
6.6 unsupervised classification
6.7 supervised classification
1 matrix indexing
2 function imadjust
3 logarithmic and contrast-stretching transformation
4 generating and plotting image histog
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