A review of the geologic history of the Himalayan-Tibetan orogen suggests that at least 1400 km of north-south shortening has been absorbed by the orogen since the onset of the Indo-Asian collision at about 70 Ma. Significant crustal shortening, which leads to eventual construction of the Cenozoic Tibetan plateau, began more or less synchronously in the Eocene (50–40 Ma) in the Tethyan Himalaya in the south, and in the Kunlun Shan and the Qilian Shan some 1000–1400 km in the north. The Paleozoic and Mesozoic tectonic histories in the Himalayan-Tibetan orogen exerted a strong control over the Cenozoic strain history and strain distribution. The presence of widespread Triassic flysch complex in the Songpan-Ganzi-Hoh Xil and the Qiangtang terranes can be spatially correlated with Cenozoic volcanism and thrusting in central Tibet. The marked difference in seismic properties of the crust and the upper mantle between southern and central Tibet is a manifestation of both Mesozoic and Cenozoic tectonics. The form...

Geologic Evolution of the Himalayan-Tibetan Orogen

Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.

/pdf/a-survey-of-image-classification-methods-and-techniques-for-5bc5ltr9dq.pdf

A survey of image classification methods and techniques for improving classification performance

https://researchweb.iiit.ac.in/~sai.deepak/lectures/14.%20Thermal%20remote%20sensing%20of%20urban%20climates%202003%20Voogt%20and%20Oke.pdf

Thermal remote sensing of urban climates

The Nature of Remote Sensing: Introduction. Remote Sensing. Information Extraction from Remote-Sensing Images. Spectral Factors in Remote Sensing. Spectral Signatures. Remote-Sensing Systems. Optical Sensors. Temporal Characteristics. Image Display Systems. Data Systems. Summary. Exercises. References. Optical Radiation Models: Introduction. Visible to Short Wave Infrared Region. Solar Radiation. Radiation Components. Surface-Reflected. Unscattered Component. Surface-Reflected. Atmosphere-Scattered Component. Path-Scattered Component. Total At-Sensor. Solar Radiance. Image Examples in the Solar Region. Terrain Shading. Shadowing. Atmospheric Correction. Midwave to Thermal Infrared Region. Thermal Radiation. Radiation Components. Surface-Emitted Component. Surface-Reflected. Atmosphere-Emitted Component. Path-Emitted Component. Total At-Sensor. Emitted Radiance. Total Solar and Thermal Upwelling Radiance. Image Examples in the Thermal Region. Summary. Exercises. References. Sensor Models: Introduction. Overall Sensor Model. Resolution. The Instrument Response. Spatial Resolution. Spectral Resolution. Spectral Response. Spatial Response. Optical PSFopt. Image Motion PSFIM. Detector PSFdet. Electronics PSFel. Net PSFnet. Comparison of Sensor PSFs. PSF Summary for TM. Imaging System Simulation. Amplification. Sampling and Quantization. Simplified Sensor Model. Geometric Distortion. Orbit Models. Platform Attitude Models. Scanner Models. Earth Model. Line and Whiskbroom ScanGeometry. Pushbroom Scan Geometry. Topographic Distortion. Summary. Exercises. References. Data Models: Introduction. A Word on Notation. Univariate Image Statistics. Histogram. Normal Distribution. Cumulative Histogram. Statistical Parameters. Multivariate Image Statistics. Reduction to Univariate Statistics. Noise Models. Statistical Measures of Image Quality. Contrast. Modulation. Signal-to-Noise Ratio (SNR). Noise Equivalent Signal. Spatial Statistics. Visualization of Spatial Covariance. Covariance with Semivariogram. Separability and Anisotropy. Power Spectral Density. Co-occurrence Matrix. Fractal Geometry. Topographic and Sensor Effects. Topography and Spectral Statistics. Sensor Characteristics and Spectral Stastistics. Sensor Characteristics and Spectral Scattergrams. Summary. Exercises. References. Spectral Transforms: Introduction. Feature Space. Multispectral Ratios. Vegetation Indexes. Image Examples. Principal Components. Standardized Principal Components (SPC) Transform. Maximum Noise Fraction (MNF) Transform. Tasseled Cap Tranformation. Contrast Enhancement. Transformations Based on Global Statistics. Linear Transformations. Nonlinear Transformations. Normalization Stretch. Reference Stretch. Thresholding. Adaptive Transformation. Color Image Contrast Enhancement. Min-max Stretch. Normalization Stretch. Decorrelation Stretch. Color Spacer Transformations. Summary. Exercises. References. Spatial Transforms: Introduction. An Image Model for Spatial Filtering. Convolution Filters. Low Pass and High Pass Filters. High Boost Filters. Directional Filters. The Border Region. Characterization of Filtered Images. The Box Filter Algorithm. Cascaded Linear Filters. Statistical Filters. Gradient Filters. Fourier Synthesis. Discrete Fourier Transforms in 2-D. The Fourier Components. Filtering with the Fourier Transform. Transfer Functions. The Power Spectrum. Scale Space Transforms. Image Resolution Pyramids. Zero-Crossing Filters. Laplacian-of-Gaussian (LoG) Filters. Difference-of-Gaussians (DoG) Filters.Wavelet Transforms. Summary. Exercises. References. Correction and Calibration: Introduction. Noise Correction. Global Noise. Sigma Filter. Nagao-Matsuyama Filter. Local Noise. Periodic Noise. Distriping 359. Global,Linear Detector Matching. Nonlinear Detector Matching. Statistical Modification to Linear and Nonlinear Detector. Matching. Spatial Filtering Approaches. Radiometric Calibration. Sensor Calibration. Atmospheric Correction. Solar and Topographic Correction. Image Examples. Calibration and Normalization of Hyperspectral Imagery. AVIRIS Examples. Distortion Correction. Polynomial Distortion Models. Ground Control Points (GCPs). Coordinate Transformation. Map Projections. Resampling. Summary. Exercises References. Registration and Image Fusion: Introduction. What is Registration? Automated GCP Location. Area Correlation. Other Spatial Features. Orthrectification. Low-Resolution DEM. High-Resolution DEM. Hierarchical Warp Stereo. Multi-Image Fusion. Spatial Domain Fusion. High Frequency Modulation. Spectral Domain Fusion. Fusion Image Examples. Summary. Exercises. References. Thematic Classification: Introduction. The Importance of Image Scale. The Notion of Similarity. Hard Versus Soft Classification. Training the Classifier. Supervised Training. Unsupervised Training. K-Means Clustering Algorithm. Clustering Examples. Hybrid Supervised/Unsupervised Training. Non-Parametric Classification Algorithms. Level-Slice. Nearest-Mean. Artificial Neural Networks (ANNs). Back-Propagation Algorithm. Nonparametric Classification Examples. Parametric Classification Algorithms. Estimation of Model-Parameters. Discriminant Functions. The Normal Distribution Model. Relation to the Nearest-Mean Classifier. Supervised Classification Examples and Comparison to Nonparametric Classifiers. Segmentation. Region Growing. Region Labeling. Sub-Pixel Classification. The Linear Mixing Model. Unmixing Model. Hyperspectral Image Analysis. Visualization of the Image Cube. Feature Extraction. Image Residuals. Pre-Classification Processing and Feature Extraction. Classification Algorithms. Exercises. Error Analysis. Multitemporal Images. Summary. References. Index.

Remote sensing, models, and methods for image processing

With the availability of multisensor, multitemporal, multiresolution and multifrequency image data from operational Earth observation satellites the fusion of digital image data has become a valuable tool in remote sensing image evaluation. Digital image fusion is a relatively new research field at the leading edge of available technology. It forms a rapidly developing area of research in remote sensing. This review paper describes and explains mainly pixel based image fusion of Earth observation satellite data as a contribution to multisensor integration oriented data processing.

Multisensor image fusion in remote sensing: Concepts, methods and applications

Late Quaternary slip rates of the 11 or so recognized active range-front faults of the Sierra Nevada from Owens Lake northwestward to Carson Valley show enough variation with time and location that a proper understanding of slip behavior of these faults may require slip histories at many places for each Late Quaternary traces of these normal faults vary in length from 13 to 45 km Most faults trend more northerly than the [approximately]MW trend of the range front The faults are separated by 20 km of apparently unfaulted terrain; many have echelon overlap None of the faults has a significant component of strike slip, including those of Owens Valley The largest late Quaternary slip rates (> 2 mm/yr) occur on the Hilton Creek fault at Long Valley and 20 km to the north on the Mono Lake fault Slip rates > 1 mm/yr occur on at least one fault north of Mono Lake and in Round Valley, south of Long Valley Farther south (Owens Valley) range-front faults have slip rates 15 km from the north end of the fault At McGee Creek, slip rate since 10--15 ka is 13--25 mm/yr; since 13--20 ka, 14--26 mm/yr; since 25--40 ka, 14--42 mm/yr, and since 65--140 ka, 11--35 mm/yr The apparently uniform rate through time at McGee Creek (and also at Hilton Lakes and Tobacco Flat, but for fewer periods; the south end site is for only one period) is interesting, but not yet convincing, mainly because of uncertain dates« less

Variations in Late Quaternary behavior along and among range-front faults of the Sierra Nevada, California

Compensation of thermal infrared radiometric measurements for atmospheric absorption and emission is one of the main factors limiting the accurate estimation of land surface temperatures and emissivities today.

In-Scene Atmospheric Characterization and Compensation in Hyperspectral Thermal Infrared Images

EAGLE2006 – an intensive field campaign for
the advances in land surface hydrometeorological processes
– was carried out in the Netherlands from 8th to 18th June
2006, involving 16 institutions with in total 67 people from
16 different countries. In addition to the acquisition of multiangle
and multi-sensor satellite data, several airborne instruments
– an optical imaging sensor, an imaging microwave radiometer,
and a flux airplane – were deployed and extensive ground measurements were conducted over one grassland
site at Cabauw and two forest sites at Loobos and Speulderbos
in the central part of the Netherlands. The generated data
set is both unique and urgently needed for the development
and validation of models and inversion algorithms for quantitative
land surface parameter estimation and land surface
hydrometeorological process studies. EAGLE2006 was led
by the Department of Water Resources of the International
Institute for Geo-Information Science and Earth Observation
(ITC) and originated from the combination of a number
of initiatives supported by different funding agencies.
The objectives of the EAGLE2006 campaign were closely related to the objectives of other European Space Agency
(ESA) campaign activities (SPARC2004, SEN2FLEX2005
and especially AGRISAR2006). However, one important objective
of the EAGLE2006 campaign is to build up a data
base for the investigation and validation of the retrieval of
bio-geophysical parameters, obtained at different radar frequencies
(X-, C- and L-Band) and at hyperspectral optical
and thermal bands acquired simultaneously over contrasting
vegetated fields (forest and grassland). As such, all activities
were related to algorithm development for future satellite
missions such as the Sentinels and for validation of retrievals
of land surface parameters with optical and thermal
and microwave sensors onboard current and future satellite
missions. This contribution describes the campaign objectives
and provides an overview of the airborne and field campaign
dataset. This dataset is available for scientific investigations
and can be accessed on the ESA Principal Investigator
Portal http://eopi.esa.int.

/pdf/eagle-2006-multi-purpose-multi-angle-and-multi-sensor-in-ruzhfv750f.pdf

EAGLE 2006 – Multi-purpose, Multi-angle and Multi-sensor In-situ, Airborne and Space-borne Campaigns over Grassland and Forest

The role of active sand seas in the formation of desert loess

An orthographic photomap of the south pole of Mars from Marina 7

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