A Hybrid Method Based on Wavelet Transform and Optimized IHS to Fusion of Remote Sensing Images Through Salience Analysis

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Journal: ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY Year:2024 | Volume:12 | Issue:1 Page(s): 61-81

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Title

A Hybrid Method Based on Wavelet Transform and Optimized IHS to Fusion of Remote Sensing Images Through Salience Analysis

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Keywords

Saliency

Fusion of sattelite images

Wavelet transform

Intensity Hue Saturation

Genetic Algorithm

Abstract

Remote sensing satellites provide various data in different parts of the electromagnetic spectrum with spectral, temporal and spatial resolution. In order to make full use of the data obtained from different sources, various numerical and analytical techniques of image integration have been developed. Among the existing image integration methods, due to their high efficiency, speed and spatial accuracy, IHS (Intensity Hue Saturation) and Wavelet transformation are the most widely used algorithms. But generally, these methods are applied to the entire image all together, and basically whatever its characteristics and contents are, they consider the entire image as a unique object. While from a satellite image of different areas we can get different data and contents. In this research, a new process for integrating images using image analysis based on its surface salience is presented. In this way, the image is divided into two prominent and non-prominent sections, and the integration scenario will be different in these two areas. In the prominent areas, which include residential areas, roads, etc., we used the IHS method which was improved by the genetic optimization method, and in the non-prominent areas (forests, pastures, and agricultural fields) we used the Wavelet transformation to analyze and extract the features with high frequency. In this research, in order to implement and evaluate the presented method, samples of images related to worldview 2 gauges have been used. The visual results and the spectral and spatial quantitive ones show the improvement of the integration results compared to the conventional and integrated methods (the output of the assessed metrics CC, ERGAS, RASE and RMSE showed better results compared to the other methods). In addition, the processing speed in this method is much higher than the new techniques which are based on the deep learning networks.

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