EVALUATION OF FILTERS FOR ENVISAT ASAR SPECKLE SUPPRESSION IN PASTURE AREA

21 citations as recorded by crossref.

1. Sampling Kantorovich Operators for Speckle Noise Reduction and Gap Filling with Some Applications to Remote Sensing D. Costarelli & M. Natale https://doi.org/10.1007/s10851-026-01323-8
2. Assessing the use of cross-orbit Sentinel-1 images in land cover classification S. Sayedain et al. https://doi.org/10.1080/01431161.2020.1763512
3. Image processing and analysis of radar and lidar data: new discoveries in Verona southern lowland (Italy) L. Burigana & L. Magnini https://doi.org/10.1080/20548923.2018.1426273
4. Field-Level Crop Classification Using an Optimal Dataset of Multi-temporal Sentinel-1 and Polarimetric RADARSAT-2 SAR Data with Machine Learning Algorithms . Neetu et al. https://doi.org/10.1007/s12524-021-01436-0
5. Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia N. Clerici et al. https://doi.org/10.1080/17445647.2017.1372316
6. High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models G. Forkuor et al. https://doi.org/10.1371/journal.pone.0170478
7. Speckle filtering impact on land use/land cover classification area using the combination of Sentinel-1A and Sentinel-2B (a case study of Kirkuk city, Iraq) S. Hasan et al. https://doi.org/10.1007/s12517-021-06494-9
8. Acreage estimation of kharif rice crop using Sentinel-1 temporal SAR data N. Subbarao et al. https://doi.org/10.1007/s41324-020-00374-2
9. SAME: a self-supervised sonar image despeckling framework via multi-scale mixture-of-experts and semantic guidance C. Guo et al. https://doi.org/10.1007/s44443-026-00789-1
10. Ensemble Learning Framework with GLCM Texture Extraction for Early Detection of Lung Cancer on CT Images S. Althubiti et al. https://doi.org/10.1155/2022/2733965
11. Comparative Assessment of Machine Learning Methods for Urban Vegetation Mapping Using Multitemporal Sentinel-1 Imagery M. Gašparović & D. Dobrinić https://doi.org/10.3390/rs12121952
12. De-Speckling of SAR Images with Fuzzy Filters along with Altered Preserved Edge Values M. Mynoddin et al. https://doi.org/10.4236/jcc.2022.103002
13. Application of remote sensing methods for statistical estimation of organic matter in soils V. Belenok et al. https://doi.org/10.15446/esrj.v27n3.100324
14. Speckle Noise Reduction in Ultrasound Images for Improving the Metrological Evaluation of Biomedical Applications: An Overview C. Duarte-Salazar et al. https://doi.org/10.1109/ACCESS.2020.2967178
15. Integration of C-band SAR and high-resolution optical images for delineating palaeo-channels in Nagaur and Barmer districts, western Rajasthan, India R. Biswas et al. https://doi.org/10.1007/s10661-022-10203-8
16. Discrimination of pearl millet in the rainfed agroecosystem using multitemporal sentinel-1 SAR data J. Mani et al. https://doi.org/10.1007/s43538-023-00218-1
17. Evaluation of tsunami inundation using synthetic aperture radar (SAR) data and numerical modeling M. Iyyappan et al. https://doi.org/10.1007/s11069-018-3257-4
18. Extending SAR Image Despckling methods for ViSAR Denoising Z. Abedi & M. Yazdian-Dehkordi https://doi.org/10.52547/jsdp.19.1.137
19. Lung Cancer Detection Using Image Segmentation by means of Various Evolutionary Algorithms K. Senthil Kumar et al. https://doi.org/10.1155/2019/4909846
20. Two‐step morphology‐based denoising and non‐local means smoothing improves micro‐computed tomography digital rock images U. Gupta et al. https://doi.org/10.1111/1365-2478.13429
21. FSDFuse: Physics-Inspired Frequency Decoupling and Data-Adaptive Spatial Decoupling for SAR–Visible Image Fusion Y. Yan et al. https://doi.org/10.1016/j.inffus.2026.104829