Synthetic aperture radar interferometry is an imaging technique for measuring the topography of a surface, its changes over time, and other changes in the detailed characteristic of the surface. By exploiting the phase of the coherent radar signal, interferometry has transformed radar remote sensing from a largely interpretive science to a quantitative tool, with applications in cartography, geodesy, land cover characterization, and natural hazards. This paper reviews the techniques of interferometry, systems and limitations, and applications in a rapidly growing area of science and engineering.

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Synthetic aperture radar interferometry

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

Geophysical applications of radar interferometry to measure changes in the Earth's surface have exploded in the early 1990s. This new geodetic technique calculates the interference pattern caused by the difference in phase between two images acquired by a spaceborne synthetic aperture radar at two distinct times. The resulting interferogram is a contour map of the change in distance between the ground and the radar instrument. These maps provide an unsurpassed spatial sampling density (∼100 pixels km−2), a competitive precision (∼1 cm), and a useful observation cadence (1 pass month−1). They record movements in the crust, perturbations in the atmosphere, dielectric modifications in the soil, and relief in the topography. They are also sensitive to technical effects, such as relative variations in the radar's trajectory or variations in its frequency standard. We describe how all these phenomena contribute to an interferogram. Then a practical summary explains the techniques for calculating and manipulating interferograms from various radar instruments, including the four satellites currently in orbit: ERS-1, ERS-2, JERS-1, and RADARSAT. The next chapter suggests some guidelines for interpreting an interferogram as a geophysical measurement: respecting the limits of the technique, assessing its uncertainty, recognizing artifacts, and discriminating different types of signal. We then review the geophysical applications published to date, most of which study deformation related to earthquakes, volcanoes, and glaciers using ERS-1 data. We also show examples of monitoring natural hazards and environmental alterations related to landslides, subsidence, and agriculture. In addition, we consider subtler geophysical signals such as postseismic relaxation, tidal loading of coastal areas, and interseismic strain accumulation. We conclude with our perspectives on the future of radar interferometry. The objective of the review is for the reader to develop the physical understanding necessary to calculate an interferogram and the geophysical intuition necessary to interpret it.

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Radar interferometry and its application to changes in the Earth's surface

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

The development of the Internet in recent years has made it possible and useful to access many different information systems anywhere in the world to obtain information. While there is much research on the integration of heterogeneous information systems, most commercial systems stop short of the actual integration of available data. Data fusion is the process of fusing multiple records representing the same real-world object into a single, consistent, and clean representation.This article places data fusion into the greater context of data integration, precisely defines the goals of data fusion, namely, complete, concise, and consistent data, and highlights the challenges of data fusion, namely, uncertain and conflicting data values. We give an overview and classification of different ways of fusing data and present several techniques based on standard and advanced operators of the relational algebra and SQL. Finally, the article features a comprehensive survey of data integration systems from academia and industry, showing if and how data fusion is performed in each.

Data fusion

Data from interferometric synthetic aperture radar (INSAR) can provide three-dimensional information by using the phase as an additional information source derived from the complex radar data. In this paper, the issues, techniques and applications of SAR interferometry are reviewed. After a brief historical review, the geometric implementations of SAR interferometry are described. The general processing techniques are summarized and an introduction to various phase unwrapping techniques is given. Besides the differential use of SAR interferometry, the review focuses on parameters such as baseline, decorrelation and motion compensation which have a limiting influence on the quality of the data. After evaluating the various existing and potential applications using SAR interferometric techniques such as topographic mapping, digital elevation modelling, slope measurement, change detection, classification, ocean currents, polar research, seismic events and volcanic hazards, the paper concludes with s...

SAR interferometry : issues, techniques, applications

Image fusion : issues, techniques and applications

The two objectives of this study are to compare the performances of different data fusion techniques for the enhancement of urban features and subsequently to improve urban land cover types classification using a refined Bayesian classification. For the data fusion, wavelet-based fusion, Brovey transform, Elhers fusion and principal component analysis are used and the results are compared. The refined Bayesian classification uses spatial thresholds defined from local knowledge and different features obtained through a feature derivation process. The result of the refined classification is compared with the results of a standard method and it demonstrates a higher accuracy. Overall, the research indicates that multi-source information can significantly improves the interpretation and classification of land cover types and the refined Bayesian classification is a powerful tool to increase the classification accuracy.

https://www.tandfonline.com/doi/pdf/10.1080/19479830903562041

Fusing high-resolution SAR and optical imagery for improved urban land cover study and classification

The Digital Earth concept as originally proposed by former US Vice president Al Gore is now well established and widely adopted internationally. Similarly, many researchers world-wide are studying the causes, effects and impacts of Global Change. The authors commence by describing a five-step approach to the development of Digital Earth technologies. This is followed by a detailed account of Digital Earth research and developments in China. The authors then present the research results of Global Change studies carried out in China, based on the Digital Earth approach. These research results are based on a classification of global change regions. This covers the following global change situations: Forest and grassland fires in Northern China, temperate region desertification and dust storms, underground coal fires, deforestation and carbon sequestration, protection and utilisation of wetlands, Avian Influenza and the spread of diseases, Tibet Plateau uplift and sub-tropical monsoon climate region,...

https://www.tandfonline.com/doi/pdf/10.1080/17538940701782510

J.L. van Genderen

Papers

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

SAR interferometry : issues, techniques, applications

Image fusion : issues, techniques and applications

Fusing high-resolution SAR and optical imagery for improved urban land cover study and classification

Digital Earth in support of global change research