Detection of Multitemporal Changes with Artificial Neural Network-Based Change Detection Algorithm Using Hyperspectral Dataset

Author:

Dahiya Neelam1,Singh Sartajvir2ORCID,Gupta Sheifali1,Rajab Adel3,Hamdi Mohammed3ORCID,Elmagzoub M.4,Sulaiman Adel3ORCID,Shaikh Asadullah5ORCID

Affiliation:

1. Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140 401, India

2. Chitkara University School of Engineering and Technology, Chitkara University, Himachal Pradesh 174 103, India

3. Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia

4. Department of Network and Communication Engineering, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia

5. Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia

Abstract

Monitoring the Earth’s surface and objects is important for many applications, such as managing natural resources, crop yield predictions, and natural hazard analysis. Remote sensing is one of the most efficient and cost-effective solutions for analyzing land-use and land-cover (LULC) changes over the Earth’s surface through advanced computer algorithms, such as classification and change detection. In the past literature, various developments were made to change detection algorithms to detect LULC multitemporal changes using optical or microwave imagery. The optical-based hyperspectral highlights the critical information, but sometimes it is difficult to analyze the dataset due to the presence of atmospheric distortion, radiometric errors, and misregistration. In this work, an artificial neural network-based post-classification comparison (ANPC) as change detection has been utilized to detect the muti-temporal LULC changes over a part of Uttar Pradesh, India, using the Hyperion EO-1 dataset. The experimental outcomes confirmed the effectiveness of ANPC (92.6%) as compared to the existing models, such as a spectral angle mapper (SAM) based post-classification comparison (SAMPC) (89.7%) and k-nearest neighbor (KNN) based post-classification comparison (KNNPC) (91.2%). The study will be beneficial in extracting critical information about the Earth’s surface, analysis of crop diseases, crop diversity, agriculture, weather forecasting, and forest monitoring.

Funder

the Deputy for Research and Innovation, Ministry of Education, Kingdom of Saudi Arabia

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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3. A shallow 2D-CNN network for crack detection in concrete structures;International Journal of Structural Integrity;2024-04-12

4. Development of Multispectral Post-Classification Change Detection Technique;2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS);2024-02-24

5. Comparing Time Series Nearest Neighbors and Artificial Neural Networks for Hyper Spectral Image Analysis;2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC);2024-01-29

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