Adaptive GIS Image Compression and Restoration Using Neural Networks

Al-Bastaki, Yousif

doi:10.3390/mca8020191

Cited by 4 publications

(4 citation statements)

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“…Due to the large increase of data and information, including the increased volumes of digitized medical information, hyperspectral, and digital images, the challenge now is how to deal with the storage and transmission requirements. This is why image and data compression is still considered an effective research field [5,6].…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Curve-Fitting Image Compression Using Hyperbolic Function

2019

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Image compression is one of the most interesting fields of image processing that is used to reduce image size. 2D curve-fitting is a method that converts the image data (pixel values) to a set of mathematical equations that are used to represent the image. These equations have a fixed form with a few coefficients estimated from the image which has been divided into several blocks. Since the number of coefficients is lower than the original block pixel size, it can be used as a tool for image compression. In this paper, a new curve-fitting model has been proposed to be derived from the symmetric function (hyperbolic tangent) with only three coefficients. The main disadvantages of previous approaches were the additional errors and degradation of edges of the reconstructed image, as well as the blocking effect. To overcome this deficiency, it is proposed that this symmetric hyperbolic tangent (tanh) function be used instead of the classical 1st-and 2nd-order curve-fitting functions which are asymmetric for reformulating the blocks of the image. Depending on the symmetric property of hyperbolic tangent function, this will reduce the reconstruction error and improve fine details and texture of the reconstructed image. The results of this work have been tested and compared with 1st-order curve-fitting, and standard image compression (JPEG) methods. The main advantages of the proposed approach are: strengthening the edges of the image, removing the blocking effect, improving the Structural SIMilarity (SSIM) index, and increasing the Peak Signal-to-Noise Ratio (PSNR) up to 20 dB. Simulation results show that the proposed method has a significant improvement on the objective and subjective quality of the reconstructed image.

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Section: Introductionmentioning

confidence: 99%

Enhancement of Curve-Fitting Image Compression Using Hyperbolic Function

2019

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“…Integrating satellite imagery with GIS is useful for study in land use change and another parameters (Halim et al, 2006). The higher resolution of the images is large of the data size (Al-Bastaki, 2006). For increasing accuracy data Bonyad (2005) use classified of land cover with PCA (Principal Component Analysis) until 80.63%.…”

Section: Introductionmentioning

confidence: 99%

Terrain Characterization and Soil Erosion Risk Assessment for Watershed Prioritization Using Remote Sensing and GIS (A case study of Nawagaon Maskara Rao Watershed, Saharanpur, India)

Harjadi¹

2009

For. Geo.

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“…Fig. 2 Block diagram of artificial neural network used by Al-Bastaki [23] for compression and restoration of GIS image datasets.…”

Section: Neural Network Fuzzy Logic and Lbs Servicesmentioning

confidence: 99%

“…Al-Bastaki [23] utilized the concepts of neural network and generic algorithms for compressing the GIS images and also explained the algorithms for restoration from compression (Fig. 1).…”

Section: Neural Network Fuzzy Logic and Lbs Servicesmentioning

confidence: 99%

Convergence of LBS and AI

Kumar¹,

Qadeer²,

Singh³

2012

IJMLC

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Abstract-Location Based Service (LBS) refers to class of applications that provides services based on the current or a known location. The location information can be obtained through the mobile communication network or the Global Navigation Satellite Systems (GNSS) [8]. But in all the cases after extracting information we have to make an exhaustive processing over this extracted geographic information. And this exhaustive processing requires a significant amount of time if it is performed by traditional techniques.Artificial Intelligence, on the other hand, has been proven to be useful in the interpretation of spatial resource information. On of the artificial intelligence technique to support spatial processing is artificial neural networks (ANNs). The recent developments in ANN technology have made it more ofan applied mathematical technique that has some similarities to the human brain.The integration of LBS with AIoffers a potential mechanismto lower the analysis-time of spatial information. One major advantage is that this integration allows the interpretive result from a small area to be transferred to a larger, geographically similar area.The objective of this paper is to discuss about the relativity of LBS services and Artificial Intelligence technologies and to uncover the benefits of converging LBS and AI.

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Adaptive GIS Image Compression and Restoration Using Neural Networks

Cited by 4 publications

References 0 publications

Enhancement of Curve-Fitting Image Compression Using Hyperbolic Function

Enhancement of Curve-Fitting Image Compression Using Hyperbolic Function

Terrain Characterization and Soil Erosion Risk Assessment for Watershed Prioritization Using Remote Sensing and GIS (A case study of Nawagaon Maskara Rao Watershed, Saharanpur, India)

Convergence of LBS and AI

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