Dynamic Gradient Sparsity Based Image Registration and Fusion Technique for Satellite Images

ShambuGowda, Anil Naragonahalli; Nanjundaiah, Chandrappa Dasanapura

doi:10.1007/978-981-19-2281-7_38

Cited by 1 publication

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This section studies various existing fusion and classification techniques for improving the quality of satellite images for effective provisioning of remote sensing object classification applications. In study [13] showed outlier [14] during the registration process significantly impacts fusion performance; recently, deep learning-based techniques [15] have been used to effectively remove the outliers and enhance fusion accuracy [16]. Using spatial data from HSI characteristics, an HSI-based crop classification model was developed in [17].…”

Section: Literature Surveymentioning

confidence: 99%

Hyperspectral object classification using hybrid spectral-spatial fusion and noise tolerant soft-margin technique

Mani,

Raguttapalli Chowdareddy

2024

IJECE

View full text Add to dashboard Cite

Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.

show abstract