The aim of this project was to determine the accuracy of using simple digital image processing techniques to map riverine water bodies with Landsat 5 TM data. This paper quantifies the classification accuracy of single band density slicing of Landsat 5 TM data to delineate water bodies on riverine floodplains. The results of these analyses are then compared to a 6-band maximum likelihood classi...

Image registration is one of the basic image processing operations in remote sensing. With the increase in the number of images collected every day from different sensors, automated registration of multisensor/multispectral images has become a very important issue. A wide range of registration techniques has been developed for many different types of applications and data. Given the diversity o...

Urban built-up area information is required by various applications. However, urban built-up area extraction using moderate resolution satellite data, such as Landsat series data, is still a challenging task due to significant intra-urban heterogeneity and spectral confusion with other land cover types. In this paper, a new method that combines spectral information and multivariate texture is p...

In this paper, a new multispectral image wavelet representation is introduced, based on multiscale fundamental forms. This representation describes gradient information of multispectral images in a multiresolution framework. The representation is in particular extremely suited for the fusion and merging of multispectral images. For fusion as well as for merging, a strategy is described. Experim...

The main goal of this study was to quantify within and between field variability in mapping agricultural crop types, their biophysical characteristics, and yield for precision farming applications using near-real-time and historical (archival) Landsat TM images. Data for 6 crops (wheat, barley, chickpea, lentil, vetch, and cumin) were gathered from a representative benchmark study area in the s...

Based on canonical correlation analysis the iteratively re-weighted multivariate alteration detection (MAD) method is used to successfully perform unsupervised change detection in bi-temporal Landsat ETM+ images covering an area with villages, woods, agricultural fields and open pit mines in North RhineWestphalia, Germany. A link to an example with ASTER data to detect change with the same meth...

ABSTRACf The National Agricultural Statistics Service used LANDSAT multispectral scanner (MSS) data to aid regression estimates of acreage for major crops during 1980-1987. Future LANDSAT satellites will not have the MSS scanner, but will have a thematic mapper (TM) scanner. TM data is more expensive to purchase and process for large areas because of its higher spatial and spectral resolution. ...