RNN-based multispectral satellite image processing for remote sensing applications. Issue 5 (25th October 2021)

• Record Type: Journal Article
• Authors: Marri, Venkata Dasu; P., Veera Narayana Reddy; S., Chandra Mohan Reddy
• Is Part Of: International journal of pervasive computing and communications. Volume 17:Issue 5(2021)
• Page Start: 583
• Page End: 595
• Publication Date: 2021-10-25
• Subjects: Optimization techniques -- Deep learning -- Recurrent neural network -- Image classification -- Image feature extraction -- Multispectral images; Ubiquitous computing -- Periodicals; Mobile computing -- Periodicals; Computer network protocols -- Periodicals; Computer network architectures -- Periodicals; Application software -- Development -- Periodicals; 004.6
• DOI: 10.1108/IJPCC-07-2021-0153
• Languages: English
• ISSNs: 1742-7371

Abstract

Abstract : Purpose: Image classification is a fundamental form of digital image processing in which pixels are labeled into one of the object classes present in the image. Multispectral image classification is a challenging task due to complexities associated with the images captured by satellites. Accurate image classification is highly essential in remote sensing applications. However, existing machine learning and deep learning–based classification methods could not provide desired accuracy. The purpose of this paper is to classify the objects in the satellite image with greater accuracy. Design/methodology/approach: This paper proposes a deep learning-based automated method for classifying multispectral images. The central issue of this work is that data sets collected from public databases are first divided into a number of patches and their features are extracted. The features extracted from patches are then concatenated before a classification method is used to classify the objects in the image. Findings: The performance of proposed modified velocity-based colliding bodies optimization method is compared with existing methods in terms of type-1 measures such as sensitivity, specificity, accuracy, net present value, F1 Score and Matthews correlation coefficient and type 2 measures such as false discovery rate and false positive rate. The statistical results obtained from the proposed method show better performance than existing methods. Originality/value: In this work, multispectral image classification accuracy is improved with an optimization algorithm called modified velocity-based colliding bodies optimization.