An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

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.

/pdf/change-detection-techniques-4g34rgajio.pdf

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

A major benefit of multitemporal, remotely sensed images is their applicability to change detection over time. Because of concerns about global and environmental change, these data are becoming increasingly more important. However, to maximize the usefulness of the data from a multitemporal point of view, an easy-to-use, cost-effective, and accurate radiometric calibration and correction procedure is needed. The atmosphere effects the radiance received at the satellite by scattering, absorbing, and refracting light; corrections for these effects, as well as for sensor gains and offsets, solar irradiance, and solar zenith angles, must be included in radiometric correction procedures that are used to convert satellite-recorded digital counts to ground reflectances. To generate acceptable radiometric correction results, a model is required that typically uses in-situ atmospheric measurements and radiative transfer code (RTC) to correct for atmospheric effects. The main disadvantage of this type of correction procedure is that it requires in-situ field measurements during each satellite overflight. This is unacceptable for many applications and is often impossible, as when using historical data or when working in very remote locations. The optimum radiometric correction procedure is one based solely on the digital image and requiring no in-situ field measurements during the satellite overflight. The darkobject subtraction (DOS) method, a strictly image-based technique, is an attempt to achieve this ideal procedure. However, the accuracy is not acceptable for many applications, mostly because it corrects only for the additive scattering effect and not for the multiplicative transmittance effect. This paper presents an entirely image-based procedure that expands on the ~10s model by including a simple multiplicative correction for the effect of atmospheric transmittance. Two straightforward methods to derive the multiplicative transmittance-correction coefficient are presented. The COSITZ) or COST method uses the cosine of the solar zenith angle, which, to a first order, is a good approximation of the atmospheric transmittance for the dates und sites used in this study. The default TAUS method uses the average of the transmittance values computed by using in-situ atmospheric field measurements made during seven different satellite overflights. Published and unpublished data made available for this study by Moran et al. (1992) are used, and my model results are compared with their results. The corrections generated by the entirely image-based COST model are as accurate as those generated by the models that used in-situ atmospheric field measurements and RTC software.

/pdf/image-based-atmospheric-corrections-revisited-and-improved-1jx25ea6e7.pdf

Image-Based Atmospheric Corrections - Revisited and Improved

https://quantalab.ias.csic.es/pdf/paper_rse_haboudane_lai.pdf

Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture

Vegetation dynamics from NDVI time series analysis using the wavelet transform

A generalized soil-adjusted vegetation index

Most of the atmospheric correction methods proposed in the literature are not easily applicable in reaJ cases. The most sophisticated models frequently require inputs which are not commonly available, whilst traditional simple dark object subtraction techniques do not generally give real reflectance values. In the present work an atmospheric correction method applicable to Landsat-TM data is described, which requires only inputs that are commonly available and the presence in the imaged scenes of some dark surfaces in TM bands 1 (blue) and 3 (red). The method consists of an inversion algorithm based on a simplified radiative transfer model in which the characteristics of atmospheric aerosols are estimated by the use of the path radiance in two TM bands rather than a priori assumed. On the basis of this information, which is crucial for determining the atmospheric properties, the retrieval of real reflectances from TM images is possible. The method can be applied to all TM scenes in which some dar...

An atmospheric correction method for the automatic retrieval of surface reflectances from TM images

Spectral mixture modelling has developed in recent years as a suitable remote sensing tool for analysing the biophysical and compositional character of ground surfaces. In this paper the potentiality of the linear spectral mixture model to extract vegetation related parameters from 0·4-2·5 μm reflectance data has been tested. High spectral resolution reflectance measurements of soil-plant mixtures with different soil colour and plant densities were carried out in a laboratory experiment. The constrained least-squares and the factor analysis unmixing procedures were applied to generate endmember fractions of the components present in the mixtures and to test the validity of the model. It is concluded that the derived fraction of the vegetation endmember is less sensitive to soil background than the NDV[. The accuracy attainable by this modelling approach can be considered sufficient for many practical purposes, being operational in the monitoring of vegetation from satellite data.

María Amparo Gilabert

Papers

Vegetation dynamics from NDVI time series analysis using the wavelet transform

A generalized soil-adjusted vegetation index

An atmospheric correction method for the automatic retrieval of surface reflectances from TM images

Linear spectral mixture modelling to estimate vegetation amount from optical spectral data

Analyses of spectral-biophysical relationships for a corn canopy