Pixel and sub-pixel accuracy in geometrical correction of AVHRR imagery

Self Cite

246

112

We address various questions that arise from the fact that a pixel Ð or the related instantaneous ® eld-of-view ( IFOV ) on the groundÐ is often larger than we would like it to be. The problems arise as a penalty imposed by technology for the fact that a spacecraft gives an overview of a very large area. We study the question of why the pixel size is important when one studies satellite imagery and also some questions related to the factors that contribute to the recorded signal in a remotely-sensed data set. This includes a discussion of the instantaneous ® eld-of-view, both from the simple geometrical point of view and from a more physical point of view. It also involves a study of the point spread function and some discussion of the problems associated with the determination of the point spread function of a given scanner. The questions of the calibration of the detectors in an instrument and of the intercalibration of nominally identical members of a series of instruments are also considered.The integration of remotely-sensed data into a GIS almost inevitably involves resampling and this leads to further complications in understanding the origin of the signal (digital number, DN) associated with a given pixel. The e ects of di erent methods of interpolation are considered as well as the consequences of resampling in relation to the classi® cation of an image. Several other topics are also considered and these include (a) the achievement of geometrical recti® cation with an error substantially smaller than (e.g., only 20 per cent of ) the length of the edge of the instantaneous ® eld-of-view, (b) data compression or upscaling, (c) mixed pixels, and (d ) the study of sub-IFOV size objects.The general conclusion is that it is important to realise that what contributes to producing the digital number on a computer tape or in a disk ® le of an image is not a simple thing. There is no simple answer to the question`what exactly gives rise to the signal detected and recorded in a pixel in a remotely-sensed image?' The main point to be made is to try to ensure that it is realised that there is a problem and to give some indication of the nature of that problem.

Section: Sub-pixel Accuracymentioning

confidence: 99%

Section: Sub-pixel Accuracymentioning

confidence: 99%

Review article Synergy in remote sensing-what's in a pixel?

Cracknell¹

1998

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246

112

“…Geometric corrections will not be discussed here as they have already been worked out for both coarse (e.g. Cracknell and Paithoonwattanakij 1989, Emery et al 1989, Roberston et al 1992, Nishihama et al 1997 and ne (Friedmann 1981 ) resolution satellite data.…”

Section: Pre-processingmentioning

confidence: 99%

Land cover mapping of large areas from satellites: Status and research priorities

Cihlar¹

2000

538

220

Abstract. Although land cover mapping is one of the earliest applications of remote sensing technology, routine mapping over large areas has only relatively recently come under consideration. This change has resulted from new information requirements as well as from new developments in remote sensing science and technology. In the near future, new data types will become available that will enable marked progress to be made in land cover mapping over large areas at a range of spatial resolutions. This paper is concerned with mapping strategies based on 'coarse' and ' ne' resolution satellite data as well as their combinations. The status of land cover mapping is discussed in relation to requirements, data sources and analysis methodologies-including pixel or scene compositing, radiometric corrections, classi cation and accuracy assessment. The overview sets the stage for identifying research priorities in data pre-processingand classi cation in relation to forthcoming improvements in data sources as well as new requirements for land cover information. Introduction and objectiveLand cover, i.e. the composition and characteristics of land surface elements, is key environmental information. It is important for many scienti c, resource management and policy purposes and for a range of human activities. It is an important determinant of land use and thus of value of land to the society. Land cover varies at a range of spatial scales from local to global, and at temporal frequencies of days to millennia. As the need for environmental planning and management became important, an accompanying call for land cover information emerged in parallel.Land cover mapping is a product of the development of remote sensing, initially through aerial photography (Colwell 1960 ). This is because 'viewing' large areas repeatedly is necessary for acquiring information about land cover. For the same reason, land cover mapping has been perhaps the most widely studied problem employing satellite data, beginning with Landsat 1. However, most of the studies using ' ne' resolution data (i.e. 20-100 m) were methodological in nature, exploring various information extraction techniques and applying these over limited areas. Applications over large areas were hampered by the lack of suitable technology, an absence of a user community with a strong need for such information, a lack of appropriate analysis methodologies, and the cost of data. Thus, at the global level, land cover data sets compiled from ground surveys or various national sources (Mathews 1983, Olson et al. 1983 were, for a number of years, the major source of information.

“…In the past, two methods have been used to attempt to correct the various types of geometric distortions in satellite image data: (a) the nature and magnitude of inaccuracy sources were estimated and correction fomulae were applied (Brush 1985, WollT 1985, Brunei and Marsouin 1987, Straka et al 1993); (b) the different coordinates between Ground Control Points (GCPs) in an image and those on a map were used to derive mathematical relationships to correct the image geometry (Benny 1983, Davison 1986, Leeuw et al 1988, Novae 1992. Algorithms which combine these two methods have also been proposed (Cracknell andPaithoonwattanakij 1989, O'Brien andTurner 1992).…”

Section: Introductionmentioning

confidence: 97%

Fine co-location of pre-corrected ATSR images

Shin

Pollard

1995

A fast and automatic algorithm is presented in this paper for co-locating nadir and forward views of the Along Track Scanning Radiometer (ATSR) images by using a binary cross-correlation matching technique. The proposed algorithm does not require any auxiliary information, a priori knowledge, or any human operation. Singlechannel thermal infrared (IR) data (I I I'm) are used for this algorithm, giving faster processing and avoiding the imperfect coregistration problem observed with multiple channels. Coastlines in images are detected by a region segmentation and an automatic thresholding technique. The matching procedure is carried out with binary coastline images (nadir and forward), and it gives comparable accuracy and faster processing than a patch based matching technique. This technique reduces geometric errors between two views to ±O·7 pixel.