Self-supervision assisted multimodal remote sensing image classification with coupled self-looping convolution networks
S Pande, B Banerjee - Neural Networks, 2023 - Elsevier
Recently, remote sensing community has seen a surge in the use of multimodal data for
different tasks such as land cover classification, change detection and many more. However …
different tasks such as land cover classification, change detection and many more. However …
Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification—A Review
According to existing signatures for various kinds of land cover coming from different
spectral bands, ie, optical, thermal infrared and PolSAR, it is possible to infer about the land …
spectral bands, ie, optical, thermal infrared and PolSAR, it is possible to infer about the land …
Double weight-based SAR and infrared sensor fusion for automatic ground target recognition with deep learning
S Kim, WJ Song, SH Kim - Remote Sensing, 2018 - mdpi.com
This paper presents a novel double weight-based synthetic aperture radar (SAR) and
infrared (IR) sensor fusion method (DW-SIF) for automatic ground target recognition (ATR) …
infrared (IR) sensor fusion method (DW-SIF) for automatic ground target recognition (ATR) …
High resolution multisensor fusion of SAR, optical and LiDAR data based on crisp vs. fuzzy and feature vs. decision ensemble systems
B Bigdeli, P Pahlavani - International journal of applied earth observation …, 2016 - Elsevier
Abstract Synthetic Aperture Radar (SAR) data are of high interest for different applications in
remote sensing specially land cover classification. SAR imaging is independent of solar …
remote sensing specially land cover classification. SAR imaging is independent of solar …
Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques
S Pande - arXiv preprint arXiv:2403.01546, 2024 - arxiv.org
Hyperspectral imaging provides precise classification for land use and cover due to its
exceptional spectral resolution. However, the challenges of high dimensionality and limited …
exceptional spectral resolution. However, the challenges of high dimensionality and limited …
Comparison of pre-event VHR optical data and post-event PolSAR data to investigate damage caused by the 2011 Japan tsunami in built-up areas
M Jung, J Yeom, Y Kim - Remote Sensing, 2018 - mdpi.com
Combining pre-disaster optical and post-disaster synthetic aperture radar (SAR) satellite
data is essential for the timely damage investigation because the availability of data in a …
data is essential for the timely damage investigation because the availability of data in a …
Fusion of color images based on fuzzy transform and spatial frequency
D Gambhir, M Manchanda - International Journal of Computational …, 2018 - World Scientific
Multiple images of a scene, captured using different imaging devices and containing
complementary information, are required to be combined into a single fused image. The …
complementary information, are required to be combined into a single fused image. The …
Fusion of multi-frequency polarimetric SAR and LISS-3 optical data for classification of various land covers
A comparative assessment of optical and microwave data is essential to find out the
complementarity of data for classification of various land features. In this paper, the …
complementarity of data for classification of various land features. In this paper, the …
Feature Selection Based on Combination of Minimal Redundancy-Maximal Relevance and Genetic Algorithm for Alassification of Fused Optical and SAR Images
The use of multi-source data, especially the fusion of optical and radar images, is a
promising way for improving the level of interpretability of the remote sensing data which …
promising way for improving the level of interpretability of the remote sensing data which …