A Quality of Images Fusion for Remote Sensing Applications

Yuhendra, Yuhendra; Sumantyo, Josaphat Tetuko Sri

doi:10.12928/telkomnika.v14i1.2681

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…The fused image is generated by applying inverse stationary wavelet transform on the fused coefficients map. Many wavelet transform based image fusion method has been presented in the literature which find wavelet transform applicable into many areas like medical diagnosis, image analysis and resolution enhancement [9]- [15]. Finally, Clustering technique is applied on the output of image fusion which classifies the changed and unchanged areas into different clusters.…”

Section: Introductionmentioning

confidence: 99%

Change Detection from Remotely Sensed Images Based on Stationary Wavelet Transform

Sharma¹,

Gulati²

2017

IJECE

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The major issue of concern in change detection process is the accuracy of the algorithm to recover changed and unchanged pixels. The fusion rules presented in the existing methods could not integrate the features accurately which results in more number of false alarms and speckle noise in the output image. This paper proposes an algorithm which fuses two multi-temporal images through proposed set of fusion rules in stationary wavelet transform. In the first step, the source images obtained from log ratio and mean ratio operators are decomposed into three high frequency sub-bands and one low frequency sub-band by stationary wavelet transform. Then, proposed fusion rules for low and high frequency sub-bands are applied on the coefficient maps to get the fused wavelet coefficients map. The fused image is recovered by applying the inverse stationary wavelet transform (ISWT) on the fused coefficient map. Finally, the changed and unchanged areas are classified using Fuzzy c means clustering. The performance of the algorithm is calculated in terms of percentage correct classification (PCC), overall error (OE) and Kappa coefficient (K c ). The qualitative and quantitative results prove that the proposed method offers least error, highest accuracy and Kappa value as compare to its preexistences.

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Section: Introductionmentioning

confidence: 99%

Change Detection from Remotely Sensed Images Based on Stationary Wavelet Transform

Sharma¹,

Gulati²

2017

IJECE

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“…Those include: (i) quality color distortion in the fused images [22]; (ii) skills and knowledge of the persons employed in doing the fusion [31]; (iii) quality results are rarely obtained [18]; and (iv) building the relationships of individual MS bands with the PAN image [32]. Note that most of the platforms acquire PAN images in the spectrum ranging between 0.50-0.90 µm (that spans visible and NIR bands).…”

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confidence: 99%

Pan-Sharpening of Landsat-8 Images and Its Application in Calculating Vegetation Greenness and Canopy Water Contents

Rahaman

Hassan

Ahmed

2017

IJGI

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Pan-sharpening is the process of fusing higher spatial resolution panchromatic (PAN) with lower spatial resolution multispectral (MS) imagery to create higher spatial resolution MS images. Here, our overall objective was to pan-sharpen Landsat-8 images and calculate vegetation greenness (i.e., normalized difference vegetation index (NDVI)), canopy structure (i.e., enhanced vegetation index (EVI)), and canopy water content (i.e., normalized difference water index (NDWI))-related variables. Our proposed methods consisted of: (i)

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“…Satellite remote sensing (RS) offers a wide variety of image data with different characteristics like temporal, spatial, radiometric and spectral resolution. These features are useful because they match the requirements for monitoring environmental conditions and natural resources [3,4]. Nowadays, large amounts of data are provided by satellite sensors like Landsat, IKONOS, SPOT-5, WordView-2, QuickBird, GeoEye, and Orbview.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Landsat 8 TIRS data capability for the preliminary study of geothermal energy resources in West Sumatra

Yuhendra¹,

Sumantyo²

2020

TELKOMNIKA

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West Sumatra is one of has big geothermal energy resources potential. Remote sensing technology can have a role in geothermal exploration activity to measure the distribution of land surface temperatures (LST) and predict the geothermal potential area. Main study to obtain the assessment of Landsat 8 TIRS (Landsat`s Thermal Infrared Sensor) data capability for geothermal energy resources estimation. Mono-window algorithms were used to generate the LST maps. Data set was combined with a digital elevation model (DEM) to identify the potential geothermal energy based on the variation in surface temperature. The result that were derived from LST map of West Sumatra shows that ranged from -8.6 C 0 to 32.59 C 0 and the different temperatures are represented by a graduated pink to brown shading. A calculated result clearly identifies the hot areas in the dataset, which are brown in colour images. Lima Puluh Kota, Tanah Datar, Solok, and South Solok areas showed the high-temperature value (Brown) in the range of 28.1 C 0 to 32.59 C 0 color in images which means that they possess high potential for generating thermal energy. In contrast, the temperatures were lower (Pink) in the north-eastern areas and the range distribution was from -8.5 C 0 to 5 C 0 .

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A Quality of Images Fusion for Remote Sensing Applications

Cited by 3 publications

References 15 publications

Change Detection from Remotely Sensed Images Based on Stationary Wavelet Transform

Change Detection from Remotely Sensed Images Based on Stationary Wavelet Transform

Pan-Sharpening of Landsat-8 Images and Its Application in Calculating Vegetation Greenness and Canopy Water Contents

Assessment of Landsat 8 TIRS data capability for the preliminary study of geothermal energy resources in West Sumatra

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