Quantifying Information Content in Multispectral Remote-Sensing Images Based on Image Transforms and Geostatistical Modelling

Abstract

Quantifying information content in remote-sensing images is fundamental for information-theoretic characterization of remote sensing information processes, with the images being usually information sources. Information-theoretic methods, being complementary to conventional statistical methods, enable images and their derivatives to be described and analyzed in terms of information as defined in information theory rather than data per se. However, accurately quantifying images’ information content is nontrivial, as information redundancy due to spectral and spatial dependence needs to be properly handled. There has been little systematic research on this, hampering wide applications of information theory. This paper seeks to fill this important research niche by proposing a strategy for quantifying information content in multispectral images based on information theory, geostatistics, and image transformations, by which interband spectral dependence, intraband spatial dependence, and additive noise inherent to multispectral images are effectively dealt with. Specifically, to handle spectral dependence, independent component analysis (ICA) is performed to transform a multispectral image into one with statistically independent image bands (not spectral bands of the original image). The ICA-transformed image is further normal-transformed to facilitate computation of information content based on entropy formulas for Gaussian distributions. Normal transform facilitates straightforward incorporation of spatial dependence in entropy computation for the aforementioned double-transformed image bands with inter-pixel spatial correlation modeled via variograms. Experiments were undertaken using Landsat ETM+ and TM image subsets featuring different dominant land cover types (i.e., built-up, agricultural, and hilly). The experimental results confirm that the proposed methods provide more objective estimates of information content than otherwise when spectral dependence, spatial dependence, or non-normality is not accommodated properly. The differences in information content between image subsets obtained with ETM+ and TM were found to be about 3.6 bits/pixel, indicating the former’s greater information content. The proposed methods can be adapted for information-theoretic analyses of remote sensing information processes.

A Multi-Feature Framework for Quantifying Information Content of Optical Remote Sensing Imagery

“…Among the wide range of applications of Shannon entropy, some key factors that may lead to changes in image information have been analyzed, such as noise [44], bandto-band relationship [45], neighborhood pixel relationship [45,46], and image scale [47], these factors are quantified to construct probabilistic statistical indicators. In addition, integrals [48], Markov chains [49], geostatistics [2], and multi-feature correlation [50,51] are used to model the correlation of image pixels and the correlation of bands to reduce redundant information content in remote sensing images. However, Shannon entropy relies entirely on the statistics of pixel grayscale and fails to capture the spatial structure of imagery.…”

“…Rapid and automatic screening of images that meet the user's needs from massive image data is a key technology for the improvement of image data utilization and to fully exploit the value of images' big data. The information content of images can be considered to be one of the critical reference indicators for image value assessment [1,2]. However, when considering the information content as an evaluation index for image screening, it is necessary to ensure the objectivity of the information content and the richness of the image information content covered.…”

“…In fact, establishing an information theory-based model for analyzing remote sensing information content is considered to be fundamental in quantitative remote sensing [2,4]. Geographic information science, including remote sensing science, as a branch of information science, has recently been the subject of calls for attention [5,6].…”