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While satellites are a proven resource for detecting and tracking volcanic ash and dust clouds, existing algorithms for automatically detecting volcanic ash and dust either exhibit poor overall skill or can only be applied to a limited number of sensors and/or geographic regions. As such, existing techniques are not optimized for use in real-time applications like volcanic eruption alerting and data assimilation. In an effort to significantly improve upon existing capabilities, the Spectrally Enhanced Cloud Objects (SECO) algorithm was developed. The SECO algorithm utilizes a combination of radiative transfer theory, a statistical model, and image processing techniques to identify volcanic ash and dust clouds in satellite imagery with a very low false alarm rate. This fully automated technique is globally applicable (day and night) and can be adapted to a wide range of low earth orbit and geostationary satellite sensors or even combinations of satellite sensors. The SECO algorithm consists of four primary components: conversion of satellite measurements into robust spectral metrics, application of a Bayesian method to estimate the probability that a given satellite pixel contains volcanic ash and/or dust, construction of cloud objects, and the selection of cloud objects deemed to have the physical attributes consistent with volcanic ash and/or dust clouds. The first two components of the SECO algorithm are described in this paper, while the final two components are described in a companion paper.