UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Volume 11 | Issue 10 | October 2024

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Volume 11 Issue 10
October-2024
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIRGN06021


Registration ID:
549128

Page Number

180-187

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Title

MULTICLASS CLASSIFICATION OF REMOTE SENSING USING ALEX NET

Abstract

With over 160 publications, this article offers a thorough analysis of deep learning techniques for remote sensing image scene classification. It addresses the primary drawbacks of these techniques, which include generative adversarial networks, autoencoder-based techniques, and convolutional neural networks. Along with introducing benchmarks for remote sensing image scene categorization, the study provides an overview of over two dozen methods' performance on three datasets. It also talks about interesting directions for future study.Deforestation in the Amazon rainforest leads to reduced biodiversity, habitat loss, and climate change. A novel remote sensing image classification framework is proposed to manage deforestation effectively. The framework uses an attention module to separate features from CNN and LSTM networks, and a loss function to calculate co-occurrence matrix and assign weights to labels. Experimental results show improved multi-label image classification performancePatch-based multi-scale completed local binary pattern (MS-CLBP) features are used in the suggested remote sensing picture scene classification method, and local patch descriptors are extracted using a Fisher vector (FV). These attributes are encoded into a discriminative representation by the method using Fisher vector encoding. Several FVs are generated using different settings, and classification is handled using a kernel-based extreme learning machine (KELM). The approach performs better on two benchmark datasets.

Key Words

remote sensing image scene classification; completed local binary patterns; multi-scale analysis; fisher vector; extreme learning machine

Cite This Article

"MULTICLASS CLASSIFICATION OF REMOTE SENSING USING ALEX NET", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.180-187, October-2024, Available :http://www.jetir.org/papers/JETIRGN06021.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"MULTICLASS CLASSIFICATION OF REMOTE SENSING USING ALEX NET", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. pp180-187, October-2024, Available at : http://www.jetir.org/papers/JETIRGN06021.pdf

Publication Details

Published Paper ID: JETIRGN06021
Registration ID: 549128
Published In: Volume 11 | Issue 10 | Year October-2024
DOI (Digital Object Identifier):
Page No: 180-187
Country: -, -, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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