Identification and Classification of Phenomena in Multispectral Satellite Imagery Using a New Image Smoother Method and its Applications in Environmental Remote Sensing
This work addresses the need for efficient and accurate image processing in environmental remote sensing, particularly for applications like monitoring regions such as northern Iran and the Caspian Sea, but it appears incremental as it builds on existing smoothing techniques.
The authors tackled the problem of identifying and classifying phenomena in multispectral satellite imagery by developing a new image smoothing method based on global gradient minimization, which resulted in a fast and highly precise approach that outperformed the usual Laplacian template in distinguishing image elements.
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.