Identification and Classification of Phenomena in Multispectral Satellite Imagery Using a New Image Smoother Method and its Applications in Environmental Remote Sensing

TL;DR

This paper introduces a novel, fast, and precise image smoothing method based on global gradient minimization for multispectral satellite imagery, enhancing the identification of environmental phenomena.

Contribution

The paper presents a new image smoothing technique using a 5x5 template based on gradient minimization, improving the discrimination of phenomena in satellite images.

Findings

The method is faster and more precise than traditional Laplacian-based smoothing.

It effectively distinguishes various environmental phenomena in multispectral images.

Application to northern Iran demonstrates its practical utility.

Abstract

In this paper a new method of image smoothing for satellite imagery and its applications in environmental remote sensing are presented. This method is based on the global gradient minimization over the whole image. With respect to the image discrete identity, the continuous minimization problem is discretized. Using the finite difference numerical method of differentiation, a simple yet efficient 5*5-pixel template is derived. Convolution of the derived template with the image in different bands results in the discrimination of various image elements. This method is extremely fast, besides being highly precise. A case study is presented for the northern Iran, covering parts of the Caspian Sea. Comparison of the method with the usual Laplacian template reveals that it is more capable of distinguishing phenomena in the image.