Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery

http://www.ens-lyon.fr/DI/wp-content/uploads/2009/07/LopesBetrouni.pdf

Fractal and multifractal analysis: a review.

In geostatistics, spatial autocorrelation is utilized to estimate optimally local values from data sampled elsewhere. The powerful synergy between geostatistics and remote sensing went unrealized until the 1980s. Today geostatistics are used to explore and describe spatial variation in remotely sensed and ground data; to design optimum sampling schemes for image data and ground data; and to increase the accuracy with which remotely sensed data can be used to classify land cover or estimate continuous variables. This article introduces these applications and uses two examples to highlight characteristics that are common to them all. The article concludes with a discussion of conditional simulation as a novel geostatistical technique for use in remote sensing.

Geostatistics and remote sensing

A comparison of spatial autocorrelation indices and landscape metrics in measuring urban landscape fragmentation

Mandelbrot's fractal geometry has sparked considerable interest in the remote sensing community since the publication of his highly influential book in 1977. Fractal models have been used in several image processing and pattern recognition applications such as texture analysis and classification. Applications of fractal geometry in remote sensing rely heavily on estimation of the fractal dimension. The fractal dimension (D) is a central construct developed in fractal geometry to describe the geometric complexity of natural phenomena as well as other complex forms. This paper provides a survey of several commonly used methods for estimating the fractal dimension and their applications to remote sensing problems. Methodological issues related to the use of these methods are summarized. Results from empirical studies applying fractal techniques are collected and discussed. Factors affecting the estimation of fractal dimension are outlined. Important issues for future research are also identified and discussed.

https://terpconnect.umd.edu/~sliang/papers/Fractal.review.IJRS2006.pdf

Fractal analysis of remotely sensed images: A review of methods and applications

A method is presented to assess fractal dimensions from remotely sensed images. The method is a three-dimensional version of the walking dividers method which has been applied to two digital images of southern France to distinguish various types of Mediterranean landscape units. The first image is a Landsat Thematic Mapper image, while the second image was acquired by the airborne Geophysical Environmental Research Imaging Spectrometer. The method has been tested on some artificial images to demonstrate procedures and results. The method can distinguish range-lands, maquis and closed garrigue and to a lesser extent agricultural regions on the TM image. Fractal dimensions for open garrigue and badlands are similar. However, the reflection properties of the land-cover units do not behave like real fractals at the scale considered, and different methods to compute the fractal dimension do not yield the same results. Results of the airborne image are disappointing, probably due to somewhat poor image quality. Finally, some advantages and disadvantages of the method are discussed.

S. M. De Jong

Papers

A fractal approach to the classification of Mediterranean vegetation types in remotely sensed images