Remote Sensing Image Fusion Using Ica and Optimized Wavelet  Transform

Hnatushenko, Volodymyr; Vasyliev, Volodymyr V.

doi:10.5194/isprs-archives-xli-b7-653-2016

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…When analyzing the equations for RASE (Equation (2)) or nQ% (Equation 3), it can be deduced that they should also achieve values close to 0. The RASE indicator is an average relative error for any number of channels and is independent of the number of channels and the ratio of spatial resolutions of the integrated images [33][34][35].…”

Section: Methods Of Spectral Quality Assessment Of Fused Imagesmentioning

confidence: 99%

A Method of Panchromatic Image Modification for Satellite Imagery Data Fusion

Grochala

Kędzierski

2017

Remote Sensing

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The standard ratio of spatial resolution between bands for high resolution satellites is 1:4, which is typical when combining images obtained from the same sensor. However, the cost of simultaneously purchasing a set of panchromatic and multispectral images is still relatively high. There is therefore a need to develop methods of data fusion of very high resolution panchromatic imagery with low-cost multispectral data (e.g., Landsat). Combining high resolution images with low resolution images broadens the scope of use of satellite data, however, it is also accompanied by the problem of a large ratio between spatial resolutions, which results in large spectral distortions in the merged images. The authors propose a modification of the panchromatic image in such a way that it includes the spectral and spatial information from both the panchromatic and multispectral images to improve the quality of spectral data integration. This fusion is done based on a weighted average. The weight is determined using a coefficient, which determines the ratio of the amount of information contained in the corresponding pixels of the integrated images. The effectiveness of the author's algorithm had been tested for six of the most popular fusion methods. The proposed methodology is ideal mainly for statistical and numerical methods, especially Principal Component Analysis and Gram-Schmidt. The author's algorithm makes it possible to lower the root mean square error by up to 20% for the Principal Component Analysis. The spectral quality was also increased, especially for the spectral bands extending beyond the panchromatic image, where the correlation rose by 18% for the Gram-Schmidt orthogonalization.

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Section: Methods Of Spectral Quality Assessment Of Fused Imagesmentioning

confidence: 99%

A Method of Panchromatic Image Modification for Satellite Imagery Data Fusion

Grochala

Kędzierski

2017

Remote Sensing

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“…Os diferentes sistemas sensores têm em seus acervos de bandas, a disponibilidade de imagens pancromáticas e multiespectrais, e a fusão de imagens é um recurso auxiliar para melhorar a resolução espacial dos índices físicos (Ejaily et al, 2013;Bhaskarrajan, 2014;Dong et al, 2014;Hnatushenko & Vasyliev, 2016).…”

Section: Introductionunclassified

Utilização de Índices Físicos a partir de Imagens OLI – TIRS para o Mapeamento de Uso e Cobertura da Terra no entorno do Aeroporto Internacional do Recife/Guararapes – Gilberto Freire

Oliveira¹

2019

Rev. Bras. Geog. Fis.

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Atualmente o uso de índices físicos NDVI (Normalized Difference Vegetacion Index), NDBI (Normalized Difference Built-up Index) e NDWI (Normalized Difference Water Index) vêm sendo muito utilizados como suporte para o mapeamento e monitoramento de uso e ocupação da terra. A área de estudo abrange o Aeroporto Internacional do Recife/Guararapes – Gilberto Freyre e o seu entorno, uma região na qual estão inseridos os municípios de Jaboatão dos Guararapes e Recife, ambos no Estado de Pernambuco. Utilizando imagens do satélite LANDSAT-8, sensor OLI de 18-06-2016, orbita-ponto 214-066, aplicou-se a técnica de fusão RGB-IHS para se obter uma melhor resolução espacial, logo após foram calculados os índices físicos, com o objetivo de avaliar o uso e ocupação do solo da área em questão. Como resultado final, obteve-se um mapa de uso e cobertura da terra, contendo quatro classes (solo exposto, água, vegetação e área construída), na escala de 1:50.000, no sistema de referência geodésico WGS84. Physical indexes from OLI - TIRS images as tools for land use and coverage mapping around the airport International Recife / Guararapes - Gilberto Freire A B S T R A C TCurrently the use of NDVI (Normalized Difference Vegetation Index), NDBI (Normalized Difference Built-up Index) and NDWI (Normalized Difference Water Index) have been widely used as support for mapping and monitoring land use and occupation. The study area covers the Recife / Guararapes - Gilberto Freyre International Airport and its surroundings, a region in which the municipalities of Jaboatão dos Guararapes and Recife are located, both in the State of Pernambuco. Using images from the LANDSAT-8 satellite, OLI sensor of 06-06-2016, orbit-point 214-066, the RGB-IHS fusion technique was applied to obtain a better spatial resolution, after the physical indexes were calculated, with the objective of evaluating the land use and occupation of the area in question. As a final result, a land use and land cover map was obtained, containing four classes (exposed soil, water, vegetation and built area), in the 1: 50.000 scale, in the WGS84 geodetic reference system.Keywords: physical indexes, remote sensing, urban area, use and land cover.

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“…Intensity-hue-saturation (IHS) transformbased fusion (Tu et al 2004) and principal component analysis (PCA)-based fusion have also been applied in the literature (Gonz alez-Audicana et al 2004). Consequently, many studies have been developed for the optimization of various satellite imageries (Rottensteiner et al 2005;Demir et al 2009;Hnatushenko and Vasyliev 2016). In a recent paper, Pradhan et al (2016) applied the wavelet transform technique to fuse LiDAR data and QuickBird image for automatic landslide detection.…”

Section: Introductionmentioning

confidence: 99%

Automatic landslide detection using Dempster–Shafer theory from LiDAR-derived data and orthophotos

Mezaal

Pradhan

Shafri

et al. 2017

Geomatics, Natural Hazards and Risk

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A good landslide inventory map is a prerequisite for landslide hazard and risk analysis. In tropical countries, such as Malaysia, preparation of the landslide inventory is a challenging task because of the rapid growth of vegetation. Thus, it is crucial to use rapid and accurate technique and effective parameters. For this purpose, Dempster Shafer theory (DST) was applied in fusing high resolution LiDAR derived data products and Greenness index derived from orthophoto imagery. Two sites were selected, for the implementation and evaluation of the DST model; site "A" for DST implementation and site "B" for the comparison. For model implementation, vegetation index, slope and height were used as effective parameters for identifying automatic landslide detection. Two type of DST based fusions were evaluated; (greenness and height) and (greenness and slope). Furthermore, validation techniques were used to validate the accuracy are confusion matrix and area under the curve. The overall accuracy of the first and second evaluated fusions were (73.4% and 84.33%), and area under the curve were (0.76 and 0.81) respectively. Additionally, the result was compared with Random Forest (RF) based detection approach. The results showed that DST does not require a priori knowledge.

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Remote Sensing Image Fusion Using Ica and Optimized Wavelet Transform

Cited by 6 publications

References 27 publications

A Method of Panchromatic Image Modification for Satellite Imagery Data Fusion

A Method of Panchromatic Image Modification for Satellite Imagery Data Fusion

Utilização de Índices Físicos a partir de Imagens OLI – TIRS para o Mapeamento de Uso e Cobertura da Terra no entorno do Aeroporto Internacional do Recife/Guararapes – Gilberto Freire

Automatic landslide detection using Dempster–Shafer theory from LiDAR-derived data and orthophotos

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