Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post-classification comparison are the most common methods used for change detection. In recent years, spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applications. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Research of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature.

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Change detection techniques

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

Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)

Hyperspectral remote sensing technology has advanced significantly in the past two decades. Current sensors onboard airborne and spaceborne platforms cover large areas of the Earth surface with unprecedented spectral, spatial, and temporal resolutions. These characteristics enable a myriad of applications requiring fine identification of materials or estimation of physical parameters. Very often, these applications rely on sophisticated and complex data analysis methods. The sources of difficulties are, namely, the high dimensionality and size of the hyperspectral data, the spectral mixing (linear and nonlinear), and the degradation mechanisms associated to the measurement process such as noise and atmospheric effects. This paper presents a tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing. In all topics, we describe the state-of-the-art, provide illustrative examples, and point to future challenges and research directions.

/pdf/hyperspectral-remote-sensing-data-analysis-and-future-1cxk3zgg98.pdf

Hyperspectral Remote Sensing Data Analysis and Future Challenges

https://rslab.disi.unitn.it/papers/R63-RSE-Plaza.pdf

Recent Advances in Techniques for Hyperspectral Image Processing

https://www.geo.uzh.ch/microsite/rsl-documents/research/SARlab/Publications/PDF/MKM+06.pdf

Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction

https://www.geo.uzh.ch/microsite/rsl-documents/research/SARlab/Publications/PDF/MMK+04.pdf

LIDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management

Four radiometric correction methods for the reduction of slope-aspect effects in a Landsat TM data set are tested in a mountainous test site with regard to their physical soundness and their influence on forest classification, as well as on the visual appearance of the scene. Excellent ground reference information and a fine-resolution DEM allowed precise assessment of the applicability of the methods under investigation. The results of the study presented here demonstrate the weakness of the classical cosine correction method for radiometric correction in rugged terrain. The statistical, Minnaert and C-correction approaches, however, yielded an improvement of the forest classification and an impressive reduction of the visual topography effect.

Radiometric corrections of topographically induced effects on Landsat TM data in an alpine environment

A new field goniometer system (FIGOS) is introduced that allows in situ measurements of hyperspectral bidirectional reflectance data under natural illumination conditions. Hyperspectral bidirectional reflectance distribution function (BRDF) data sets taken with FIGOS nominally cover the spectral range between 300 and 2450 nm in 704 bands. Typical targets are small-growing, dense, and homogeneous vegetation canopies, man-made surfaces, and soils. Field BRDF data of a perennial ryegrass surface reveal a strong spectral variability. In the blue and red chlorophyll absorption bands, BRDF effects are strong. Less-pronounced bidirectional reflectance effects are observed in the green and in most of the near-infrared range there surface reflectance is high. An anisotropy index (ANIX), defined as the ratio between the maximum and minimum bidirectional reflectance over the hemisphere, is introduced as a surrogate measurement for the extent of spectral BRDF effects. The ANIX data of the ryegrass surface show a very high correlation with nadir reflectance due to multiple scattering effects. Since canopy geometry, multiple scattering, and BRDF effects are related, these findings may help to derive canopy architecture parameters, such as leaf area index (LAI) or leaf angle distribution (LAD) from remotely sensed hyperspectral BRDF data. Furthermore, they show that normalized difference vegetation index (NDVI) data are strongly biased by the spectral variability of BRDF effects.

/pdf/a-field-goniometer-system-figos-for-acquisition-of-58swujpgf9.pdf

A field goniometer system (FIGOS) for acquisition of hyperspectral BRDF data

A physically-based model to correct atmospheric and topographically induced illumination effects in optical satellite data is developed and tested. Special emphasis is put on the impact of rugged terrain. Ground reference data for various land use classes enables the assessment of the corrections' influence on land use classifications. The estimation of surface reflectance is achieved in a two-step procedure. First irradiance components and atmospheric parameters are calculated for horizontal surfaces using the atmo-code 6S, E. Vermonte et al. (1994), then the influence of the topography on the parameters is integrated using DEM data.

Klaus I Itten

Papers

Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction

LIDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management

Radiometric corrections of topographically induced effects on Landsat TM data in an alpine environment

A field goniometer system (FIGOS) for acquisition of hyperspectral BRDF data

A physically-based model to correct atmospheric and illumination effects in optical satellite data of rugged terrain