soil salinity has been a large problem in arid and semi arid regions. preparation of such maps is useful for natural resource managers. old methods of preparing such maps require a lot of time and cost. multi-spectral remotely sensed dates due to the broad vision and repeating of these imageries is suitable for provide saline soil maps. this investigation is conducted to provide saline soil map...

In this paper an attempt has been made to develop a decision tree classification algorithm for remotely sensed satellite data using the separability matrix of the spectral distributions of probable classes in respective bands. The spectral distance between any two classes is calculated from the difference between the minimum spectral value of a class and maximum spectral value of its preceding ...

In this paper, we propose a technique to construct a sub-optimal semi-naive Bayesian network when given a bound on the maximum number of variables that can be combined into a node. We theoretically show that our approach has a less computation cost when compared with the traditional semi-naive Bayesian network. At the same time, we can obtain a resulting sub-optimal structure according to the m...

multi-spectral remotely sensed data is useful information source for the detection of surface changes and change detection is a major application of the remotely sensed data. this study is conducted to investigate capability of sensor liss-iii of irs-p6resource satellite data for providing land cover map, in najm abad of savojbolagh region with 20000 ha area. the images of 26th june, 2006 were ...

This paper presents classification of various land cover types from the raw satellite image using supervised classifiers and performances of the classifiers are analyzed. Geo coded and Geo-referenced remote sensed images from Survey of India, Government of India Topographical maps are used. Prior to classification, Training process to assemble a set of statistics describing spectral response pa...

Maximum likelihood and neural classifiers are two typical techniques in image classification. This paper investigates how to adapt these approaches to hyperspectral imaging for the classification of five kinds of Chinese tea samples, using visible light hyperspectral spectroscopy rather than near-infrared. After removal of unnecessary parts from each imaged tea sample using a morphological crop...