Image Processing Techniques for Analysis of Satellite Images for Historical Maps Classification—An Overview

Asokan, Anju; Anitha, J.; Ciobanu, Monica; Andrei, Gabor; Naaji, Antoanela; Hemanth, D. Jude

doi:10.3390/app10124207

Cited by 63 publications

(22 citation statements)

References 84 publications

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“…The high F1‐measure of the proposed method indicates that the boundary region is matched and segmented very well. The accuracy measure is the ratio of the classified pixels to the overall pixels in an image 88,89 . Comparatively, the proposed method achieves higher scores for all the performance metrices than the other dehazing algorithms.…”

Section: Resultsmentioning

confidence: 98%

Multiscale decomposition and fusion‐based color contrast restoration for various water‐colored environments

Sivamani

Nedumaran

2021

Color Research & Application

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Underwater image processing is a vast area of research to identify blurred objects in the ocean or sea environments. The light absorbed and scattered by the underwater medium create artifacts in the acquired images due to low‐light effects, poor visibility, darkness, illumination variation, and color contrast degradation. In this research work, a novel optical image preprocessing framework was proposed to restore the color contrast of images obtained from various water‐colored environments. Domain transform‐based multiscale image decomposition method was used to extract the base, residual, and detail layers. Then, the smoothed image was reconstructed from the base layer by applying the adaptive iterative backward‐projection algorithm. To obtain dehazed images, a multiscale fusion‐based algorithm was developed, which fused the detail layers and reconstructed the images. The outcome of the proposed framework revealed that the contrast degradation and low‐light‐related artifacts present in the acquired images from various water‐colored environments were found to be considerably reduced. The experimental results of the proposed method exhibit scalable improvements over the other methods in terms of visual perception and estimated performance metrics (underwater image quality measure = 6.1186; peak signal‐to‐noise ratio = 26.68; feature points matching = 96 points; semantic image segmentation accuracy = 0.8465 and processing time of 0.11 seconds on a single image). The novelty of this method relies on the fusion of well‐established techniques, which outperformed the advanced techniques like deep learning method in terms of simplicity, speed, performance, datasets, image resolution, and applications.

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Section: Resultsmentioning

confidence: 98%

Multiscale decomposition and fusion‐based color contrast restoration for various water‐colored environments

Sivamani

Nedumaran

2021

Color Research & Application

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“…Asokan et al [21] presented an overview of image processing techniques for analysis of satellite images for historical maps classification. An exhaustive analysis on the merits and demerits of many satellite image processing methods was done in this study.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Satellite Image Compression Using Hybrid (DWT, DCT and SVD) Algorithm

Anasuodei¹,

Eleonu²

2021

AJCST

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Storing images consumes a lot of storage space due to the large number of bits used to represent them. These bits are comprised of pixels that make up the image. These heavy images are also very difficult to be transmitted over channels due to their great size. Compression involves the reduction of the amount of bits used in representing an image and consequently reducing the size of that image without losing any detail from the image. There are so many image compression techniques used to achieve this feat, but they have drawbacks such as lack of a model that can compress a satellite image, lack of adaptive reversible techniques for compression and inability to compress complex images such as satellite images. This work, proposed an hybrid Discrete Wavelet Transform, Discrete Cosine Transform and Singular Value Decomposition (DCT-DWT-SVD)based techniques for satellite image compression. The algorithms were combined to breakdown the images into blocks/matrices and assign certain values to them depending on the concentration of colour bits around the region. The areas with higher bits are reduced and compression is achieved. A hybrid methodology of Agile and Waterfall model was used in this approach. The model was implemented using MATLAB and satellite images gotten from a public repository. The Compression ratio was 0.9990 and 0.9941 for the two images compressed which shows high and efficient compression. The Mean Square Error (MSE) was 2.51 which is low. This study will be beneficial to remote sensor companies, Graphic designers and the research community.

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“…To analyse the environment of a geographical area many image processing methods have been developed. Here Anand et al, 2020 [8] proposes a method for detection of water level, crops, buildings in an area using the Support Vector Machine classifier algorithm in Matlab platform. The various steps involved in the detection process are explained here.…”

Section: Literature Surveymentioning

confidence: 99%

Remote Sensing Satellite Image Classification Using Neural Network

Karthik¹,

Sangeetha²,

Karthik³

2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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Continuous monitoring of the geographical area of the Earth has become a necessity. Either to develop the infrastructure or to protect the resources of a region the right analysis of the geographical distribution has to be detected. Hence processing of satellite image of the region is the best solution. This paper proposes an approach for refining the detection and classification of various geographical regions like land, water, forest etc., in an area. Here the Convolutional Neural Network (CNN) classifier of deep learning technique is applied for processing the satellite image of the region under study using Matlab platform.

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Image Processing Techniques for Analysis of Satellite Images for Historical Maps Classification—An Overview

Cited by 63 publications

References 84 publications

Multiscale decomposition and fusion‐based color contrast restoration for various water‐colored environments

Multiscale decomposition and fusion‐based color contrast restoration for various water‐colored environments

An Enhanced Satellite Image Compression Using Hybrid (DWT, DCT and SVD) Algorithm

Remote Sensing Satellite Image Classification Using Neural Network

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