Engineering topics

Salu, Yehuda

Publications and source records attributed to Salu, Yehuda.

3 records

NASA NTRS1995

Sub-pixel localization of highways in AVIRIS images

Roads and highways show up clearly in many bands of AVIRIS images. A typical lane in the U.S. is 12 feet wide, and the total width of a four lane highway, including 18 feet of paved shoulders, is 19.8 m. Such a highway will cover only a portion of any 20x20 m AVIRIS pixel that it traverses. The other portion of these pixels wil be usually covered by vegetation. An interesting problem is to precisely determine the location of a highway within the AVIRIS pixels that it traverses. This information may be used for alignment and spatial calibration of AVIRIS images. Also, since the reflection properties of highway surfaces do not change with time, and they can be determined once and for all, such information can be of help in calculating and filtering out the atmospheric noise that contaminates AVIRIS measurements. The purpose of this report is to describe a method for sub-pixel localization of highways.

Salu, Yehuda↗

NASA NTRS1993

Classification of multispectral image data by the Binary Diamond neural network and by nonparametric, pixel-by-pixel methods

The classification of multispectral image data obtained from satellites has become an important tool for generating ground cover maps. This study deals with the application of nonparametric pixel-by-pixel classification methods in the classification of pixels, based on their multispectral data. A new neural network, the Binary Diamond, is introduced, and its performance is compared with a nearest neighbor algorithm and a back-propagation network. The Binary Diamond is a multilayer, feed-forward neural network, which learns from examples in unsupervised, 'one-shot' mode. It recruits its neurons according to the actual training set, as it learns. The comparisons of the algorithms were done by using a realistic data base, consisting of approximately 90,000 Landsat 4 Thematic Mapper pixels. The Binary Diamond and the nearest neighbor performances were close, with some advantages to the Binary Diamond. The performance of the back-propagation network lagged behind. An efficient nearest neighbor algorithm, the binned nearest neighbor, is described. Ways for improving the performances, such as merging categories, and analyzing nonboundary pixels, are addressed and evaluated.

Salu, Yehuda↗

NASA NTRS1991

Non-parametric analysis of LANDSAT maps using neural nets and parallel computers

Nearest neighbor approaches and a new neural network, the Binary Diamond, are used for the classification of images of ground pixels obtained by LANDSAT satellite. The performances are evaluated by comparing classifications of a scene in the vicinity of Washington DC. The problem of optimal selection of categories is addressed as a step in the classification process.

Salu, Yehuda↗