Maximum Likelihood (ML) modeling of multiclass data for classi cation often su ers from the following problems: a) data insu ciency implying overtrained or unreliable models b) large storage requirement c) large computational requirement and/or d) ML is not discriminating between classes. Sharing parameters across classes (or constraining the parameters) clearly tends to alleviate the rst three...

Land use is being considered as an element in determining land change studies, environmental planning and natural resource applications. The Earth’s surface Study by remote sensing has many benefits such as, continuous acquisition of data, broad regional coverage, cost effective data, map accurate data, and large archives of historical data. To study land use / cover, remote sensing as an effic...

This paper focuses on the classification of the MPSK modulations using compressive measurements in additional Gaussian white noise (AWGN). Under the compressive sensing (CS) frame, the compressive maximum-likelihood (CML) classifier provided in this paper tries to recognize the MPSK signals using far fewer samplings than traditional maximum-likelihood (TML) classifier needs. This paper presents...

This paper deals with modulation classification, First, a state of the art is given which is separated into two classes: the pattern recognition approach and the Maximum Likelihood (ML) approach. Then we propose a new classifier called the General Maximum Likelihood Classilier (GMLC) based on an approximation of the likelihood function. We derive equations of this classifier in the case of line...

A simplified maximum likelihood classification technique for handling remotely sensed image data is proposed which reduces, significantly, the processing time associated with traditional maximum likelihood classification when applied to imaging spectrometer data, and copes with the training of geographically small classes. Several wavelength subgroups are formed from the complete set of spectra...

<p><em>Classification technique on remote sensing images is an effort taken to identify the class of each pixel based spectral characteristics various channels. Traditional classifications such as Maximum Likelihood are statistical parameters standard deviation and mean, which have a probability model in class. While object-based classification method, one Decision Trees, rules for ...