Satellite Image Analysis: A Review

Babbar, Jatin; Rathee, Neeru

doi:10.1109/icecct.2019.8869481

Cited by 9 publications

(4 citation statements)

References 35 publications

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“…A state of the art of satellite image processing falls outside the scope of this paper; reviews covering not only edge detection but also segmentation and classification can be found in [17,18].…”

Section: Previous Work On Fractional Image Processingmentioning

confidence: 99%

Fractional Order Processing of Satellite Images

2021

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Nowadays, satellite images are used in many applications, and their automatic processing is vital. Conventional integer grey-scale edge detection algorithms are often used for this. This study shows that the use of color-based, fractional order edge detection may enhance the results obtained using conventional techniques in satellite images. It also shows that it is possible to find a fixed set of parameters, allowing automatic detection while maintaining high performance.

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Section: Previous Work On Fractional Image Processingmentioning

confidence: 99%

Fractional Order Processing of Satellite Images

2021

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“…While the latter one, the squeezed image must contain a precise model of the authentic image. Their applications include biological data, e-documents, camera visualization, satellite images [16][17][18], and medical archives, in which the distorted information can lead to fault analytics.…”

Section: Image Compression Algorithmsmentioning

confidence: 99%

Evaluation of graphic effects embedded image compression

Jittawiriyanukoon¹,

Srisarkun²

2020

IJECE

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A fundamental factor of digital image compression is the conversion processes. The intention of this process is to understand the shape of an image and to modify the digital image to a grayscale configuration where the encoding of the compression technique is operational. This article focuses on an investigation of compression algorithms for images with artistic effects. A key component in image compression is how to effectively preserve the original quality of images. Image compression is to condense by lessening the redundant data of images in order that they are transformed cost-effectively. The common techniques include discrete cosine transform (DCT), fast Fourier transform (FFT), and shifted FFT (SFFT). Experimental results point out compression ratio between original RGB images and grayscale images, as well as comparison. The superior algorithm improving a shape comprehension for images with grahic effect is SFFT technique.

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“…Digital image acquisition and capturing tools have been quite popular in many domains. Utilizations of images in different fields are in trend, be it using in clinical science for diseases detection (1) , for diagnosis in radiology (2) , using remote sensing (3) , use case scenario in communication (4) , document scrutiny by retrieving watermarks (5) are just few examples. The huge image repositories generated through this exploration and the constraints posed by conventional text-based database systems, underscoring the need to explore robust solutions for image databases and adopt efficient strategies to facilitate image retrieval.…”

Section: Introductionmentioning

confidence: 99%

Delving into the Depths of Image Retrieval Systems in the Light of Deep Learning: A Review

Khan,

Bhat

2023

IJST

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Objective:The objective of this study is to conduct a comprehensive review of existing research and literature in the field of Content-Based Image Retrieval (CBIR). This review highlights the key challenges associated with the extraction and representation of visual semantics of images. This paper discusses the measure used computing similarity and ranking of retrieved images by CBIR system. The review discusses limitation of traditional approaches and also highlights the challenges with the current deep learning methods in semantic feature representation, defining the similarity metrics and indexing. This paper also highlights scalability and generalization challenges in implementing real environment. Methods: A thorough literature review was conducted on wellestablished databases, including Scopus, Web of Science, IEEE Xplore, ACM, and Science Direct, employing appropriate keywords. Mention the period of coverage. Pertinent search terms encompassed local feature representation, global feature representation, low-level features, high level features, semantic gap, image embeddings, handcrafted features, deep learning, image descriptors, similarity, and image indexing, with the aim of exploring content-based image retrieval systems. Comparative analysis was performed on the chosen articles, taking into account factors such as algorithms, methodologies, datasets, and evaluation metrics. The results discussed using comparative analysis, ensuring a comprehensive overview of recent literature on content-based image retrieval, offering valuable insights and highlighting emerging trends in the field. Findings : The research uncovers the novelty in the realm of contentbased image retrieval (CBIR) by highlighting the challenge of high-level visual semantics when comparing images, as perceived by humans. It emphasizes that feature extraction methods and choices significantly influence CBIR system performance, stressing the importance of selecting suitable features and similarity measures based on image dataset characteristics and application requirements. The study underscores the persistent obstacle of the semantic gap between low-level visual features and high-level semantic concepts, encouraging exploration of diverse approaches like deep learning, relevance feedback, and ontology-based methods to bridge this gap. Particularly, deep learnhttps://www.indjst.org/

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Satellite Image Analysis: A Review

Cited by 9 publications

References 35 publications

Fractional Order Processing of Satellite Images

Fractional Order Processing of Satellite Images

Evaluation of graphic effects embedded image compression

Delving into the Depths of Image Retrieval Systems in the Light of Deep Learning: A Review

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