ISSN:0143-1161    

DOI:10.1080/01431169608949082

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

ISSN:0143-1161    

DOI:10.1080/01431169608949083

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

摘要:

Some authors have determined patch perimeters in raster image processing and geographic information systems by summing the number of pixels immediately surrounding the patches. We demonstrate that this method is inaccurate. When applied to satellite sensor imagery, errors ranged from overestimation of perimeter length by 34-5 per cent to underestimation by 41·7 per cent. Therefore. we developed a new method that is both accurate and relatively simple. This method determines perimeters using a standard 3 by 3 pixel moving window. The method can be used with most raster systems and has applications in landscape ecology for calculating such variables as the fractal dimension and ecotone dimensions

ISSN:0143-1161    

DOI:10.1080/01431169608949084

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

摘要:

There are many sources of both conservative and optimistic bias in classification accuracy assessment. In this Letter, we discuss three sources of optimistic bias: use of training data for accuracy assessment, restriction of reference data sampling to homogeneous areas, and sampling of reference data not independent of training data. The magnitude and direction of bias in classification accuracy estimates depends on the methods used for classification and reference data sampling. However, based on our review of 1994 papers published in three remote sensing journals, we conclude that many studies currently do not report their methods in sufficient detail to enable readers to assess the potential for bias in classification accuracy estimates

ISSN:0143-1161    

DOI:10.1080/01431169608949085

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

摘要:

Airborne synthetic aperture radar (SAR) data acquired over Alaska are used to investigate the ability of SAR to distinguish between land cover classes of differing methane exchange rates. Land cover within the study area is divided into four classes: forest, bog, water, and fen, with fen having the highest methane emission. Accurate classification is achieved using both statistical and neural network techniques applied to fully polarimetric L- and C-band data. Similar classification accuracies are also obtained using non-polarimetric subsets of the data, analogous to data that would be available by combining SAR observations from ERS-1/2, JERS-I (Fuyo-1), and RADARSAT. Accurate classification of fens, however, is possible only when the non-polarimetric subset includes L-band data

ISSN:0143-1161    

DOI:10.1080/01431169608949086

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor