A survey of classical methods and new trends in pansharpening of multispectral images <s> Pre-processing <s> From the Publisher: ::: The book provides the non-specialist with an introduction to quantitative evaluation of satellite and aircraft derived from remotely retrieved data. Each chapter covers the pros and cons of digital remotely sensed data, without detailed mathematical treatment of computer based algorithms, but in a manner conductive to an understanding of their capabilities and limitations. Problems conclude each chapter. <s> BIB001 </s> A survey of classical methods and new trends in pansharpening of multispectral images <s> Pre-processing <s> 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. <s> BIB002 </s> A survey of classical methods and new trends in pansharpening of multispectral images <s> Pre-processing <s> Our framework is the synthesis of multispectral images (MS) at higher spatial resolution, which should be as close as possible to those that would have been acquired by the corresponding sensors if they had this high resolution. This synthesis is performed with the help of a high spatial but low spectral resolution image: the panchromatic (Pan) image. The fusion of the Pan and MS images is classically referred as pan-sharpening. A fused product reaches good quality only if the characteristics and differences between input images are taken into account. Dissimilarities existing between these two data sets originate from two causes-different times and different spectral bands of acquisition. Remote sensing physics should be carefully considered while designing the fusion process. Because of the complexity of physics and the large number of unknowns, authors are led to make assumptions to drive their development. Weaknesses and strengths of each reported method are raised and confronted to these physical constraints. The conclusion of this critical survey of literature is that the choice in the assumptions for the development of a method is crucial, with the risk to drastically weaken fusion performance. It is also shown that the Amelioration de la Resolution Spatiale par Injection de Structures concept prevents from introducing spectral distortion into fused products and offers a reliable framework for further developments. <s> BIB003
Remote sensors acquire raw data that need to be processed in order to convert it to images. The grid of pixels that constitutes a digital image is determined by a combination of scanning in the cross-track direction (orthogonal to the motion of the sensor platform) and by the platform motion along the in-track direction. A pixel is created whenever the sensor system electronically samples the continuous data stream provided by the scanning BIB002 . The image data recorded by sensors and aircrafts can contain errors in geometry and measured brightness value of the pixels (which are referred to as radiometric errors) BIB001 . The relative motion of the platform, the non-idealities in the sensors themselves and the curvature of the Earth can lead to geometric errors of varying degrees of severity. The radiometric errors can result from the instrumentation used to record the data, the wavelength dependence of solar radiation and the effect of the atmosphere. For many applications using these images, it is necessary to make corrections in geometry and brightness before the data are used. By using correction techniques BIB002 BIB001 , an image can be registered to a map coordinate system and therefore has its pixels addressable in terms of map coordinates rather than pixel and line numbers, a process often referred to as geocoding. The Earth Observing System Data and Information System (EOSDIS) receives "raw" data from all spacecrafts and processes it to remove telemetry errors, eliminate communication artifacts and create Level 0 Standard Data Products that represent raw science data as measured by the instruments. Other levels of remote sensing data processing were defined in  by the NASA Earth Science program. In Level 1A, the reconstructed, unprocessed instrument data at full resolution, time-referenced and annotated with ancillary information (including radiometric and geometric calibration coefficients and georeferencing parameters) are computed and appended, but not applied to Level 0 data (i. e., Level 0 can be fully recovered from Level 1A). Some instruments have Level 1B data products, where the data resulting from Level 1A are processed to sensor units. At Level 2, the geographical variables are derived (e.g., Ocean wave height, soil moisture, ice concentration) at the same resolution and location as Level 1 data. Level 3 maps the variables on uniform space-time grids usually with some completeness and consistency, and finally, Level 4 gives the results from the analysis of the previous levels data. For many applications, Level 1 data are the most fundamental data records with significant scientific utility, and it is the foundation upon which all subsequent data sets are produced. For pansharpening, where the accuracy of the input data is crucial, at least radiometric and geometric corrections need to be performed on the satellite data. Radiometric correction rectifies defective columns and missing lines and reduces the non-uniformity of the sensor response among detectors. The geometrical correction deals with systematic effects such as panoramic effect, earth curvature and rotation. Note, however, that even with geometrically registered PAN and MS images, differences might appear between images as described in BIB003 . These differences include object disappearance or appearance and contrast inversion due to different spectral bands or different times of acquisition. Besides, both sensors do not aim exactly at the same direction, and acquisition times are not identical which have an impact on the imaging of fast-moving objects. Once the image data have already been processed in one of the standard levels previously described, and in order to apply pansharpening techniques, the images are pre-processed to accommodate the pansharpening algorithm requirements. This pre-processing may include registration, resampling and histogram matching of the MS and PAN images. Let us now study these processes in detail.