Оценка результатов повышения разрешения мультиспектральных спутниковых изображений методом слияния

When processing digital images obtained by remote sensing of the Earth, various methods are used to increase their resolution. However, in this case, some distortions of a different nature may appear on the images. For example, luminance distortion (color, contrast, sharpness) and geometric (object boundary deformations). Developers of automated image processing systems face the task of choosing from dozens of methods the one that introduces the least visually noticeable distortions, i.e. creates images of the best quality.In this article, the following problem was solved: to determine the functions for assessing the quality of images formed as a result of multispectral satellite image pansharpening. The pansharped image cannot be compared with the template one, since it does not exist. To assess quality of such images, we proposed to use the so-called no-reference evaluation measures.The article briefly describes methods for synthesizing a new high-resolution color image from four images of Earth remote sensing. Functions for calculating quantitative estimates of the quality of the resulting images are discussed. Results of some space image pansharpening by different methods are presented. Graphs of these assessments of image quality are constructed. To evaluate panchromatic fusion results, the following non-reference quality scores are recommended: FISH, LOCC, LOEN, NATU, SHAR, and WAVS. The clearest boundaries and natural colors of objects were demonstrated by the P+XS pansharpening algorithm based on a linear combination of spectral channels.При обработке цифровых изображений, полученных при дистанционном зондировании Земли, используются различные способы повышения их разрешения. Однако при этом на изображениях могут появиться искажения разного характера. Например, яркостные искажения (цвета, контраста, резкости) и геометрические (границ объектов). Перед разработчиками автоматизированных систем обработки изображений возникает задача из десятков методов выбрать тот, который вносит наименьшие визуально заметные искажения, т.е. создает изображения наилучшего качества.В данной статье решалась следующая задача: определить функции оценки качества изображений, формируемых в результате слияния мультиспектральных снимков с панхроматическим изображением, зарегистрированных одним спутником. Подобные преобразования называют – паншарпенинг. Полученный результат слияния невозможно сравнить с эталоном, поскольку его не существует. Для оценки качества таких изображений предлагается использовать так называемые безэталонные оценочные меры.  В статье кратко описаны методы синтеза нового цветного изображения высокого разрешения из четырех снимков дистанционного зондировании Земли. Обсуждаются особенности количественной оценки качества получаемых изображений. Приведены результаты преобразования космических изображений различными методами увеличения разрешения. Построены графики количественных оценок качества изображений. Для оценки результатов панхроматического слияния рекомендуется использовать следующие безэталонные оценки качества: FISH, LOCC, LOEN, NATU, SHAR и WAVS. При повышении разрешения мультиспектральных спутниковых изображений методом слияния с панхроматическим изображением лучшие результаты (четкие границы и естественные цвета) показал метод, в основе которого используется линейная комбинация спектральных каналов.

An Overview of Multimodal Remote Sensing Data Fusion: From Image to Feature, from Shallow to Deep

With the ever-growing availability of different remote sens-ing (RS) products from both satellite and airborne platforms,simultaneous processing and interpretation of multimodal RSdata have shown increasing significance in the RS field. Dif-ferent resolutions, contexts, and sensors of multimodal RSdata enable the identification and recognition of the materialslying on the earth’s surface at a more accurate level by de-scribing the same object from different points of the view. Asa result, the topic on multimodal RS data fusion has graduallyemerged as a hotspot research direction in recent years.This paper aims at presenting an overview of multimodalRS data fusion in several mainstream applications, which canbe roughly categorized by 1) image pansharpening, 2) hyper-spectral and multispectral image fusion, 3) multimodal fea-ture learning, and (4) crossmodal feature learning. For eachtopic, we will briefly describe what is the to-be-addressed re-search problem related to multimodal RS data fusion and givethe representative and state-of-the-art models from shallow todeep perspectives

Particle Swarm Optimization-Based Multispectral Image Fusion for Minimizing Spectral Loss

A novel multispectral image fusion technique is proposed which minimizes the spectral loss of fused product using a proper objective function. It is found that the Relative Average Square Error (RASE) is a good choice to be considered as the objective function. A linear combination of multispectral bands is calculated in which the weights are optimized using particle swarm optimization algorithm. Several experimental studies have been conducted on three public domain datasets to show the effectiveness of the proposed approach in comparison with state-of-the-art methods. The objective and visual assessments of the proposed method support the claims provided in this paper

Particle Swarm Optimization-Based Multispectral Image Fusion for Minimizing Spectral Loss

A novel multispectral image fusion technique is proposed which minimizes the spectral loss of fused product using a proper objective function. It is found that the Relative Average Square Error (RASE) is a good choice to be considered as the objective function. A linear combination of multispectral bands is calculated in which the weights are optimized using particle swarm optimization algorithm. Several experimental studies have been conducted on three public domain datasets to show the effectiveness of the proposed approach in comparison with state-of-the-art methods. The objective and visual assessments of the proposed method support the claims provided in this paper

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches