scholarly journals A PAN-SHARPENING METHOD BASED ON GUIDED IMAGE FILTERING: A CASE STUDY OVER GF-2 IMAGERY

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1055-1060
Author(s):  
Y. Zheng ◽  
M. Guo ◽  
Q. Dai ◽  
L. Wang
Keyword(s):  
Remote Sensing ◽  
Quantitative Assessment ◽  
Spatial Information ◽  
State Of The Art ◽  
Image Filtering ◽  
Optical Remote Sensing ◽  
Low Resolution ◽  
Spectral Fidelity ◽  
Guided Image Filtering

The GaoFen-2 satellite (GF-2) is a self-developed civil optical remote sensing satellite of China, which is also the first satellite with the resolution of being superior to 1 meter in China. In this paper, we propose a pan-sharpening method based on guided image filtering, apply it to the GF-2 images and compare the performance to state-of-the-art methods. Firstly, a simulated low-resolution panchromatic band is yielded; thereafter, the resampled multispectral image is taken as the guidance image to filter the simulated low resolution panchromatic Pan image, and extracting the spatial information from the original Pan image; finally, the pan-sharpened result is synthesized by injecting the spatial details into each band of the resampled MS image according to proper weights. Three groups of GF-2 images acquired from water body, urban and cropland areas have been selected for assessments. Four evaluation metrics are employed for quantitative assessment. The experimental results show that, for GF-2 imagery acquired over different scenes, the proposed method can not only achieve high spectral fidelity, but also enhance the spatial details

scholarly journals PANSHARPENING ON THE NARROW VNIR AND SWIR SPECTRAL BANDS OF SENTINEL-2

2016 ◽  
Vol XLI-B7 ◽  
pp. 723-730 ◽  
Author(s):  
A. D. Vaiopoulos ◽  
K. Karantzalos
Keyword(s):  
Remote Sensing ◽  
Quantitative Assessment ◽  
Spatial Information ◽  
State Of The Art ◽  
Qualitative Evaluation ◽  
Evaluation Framework ◽  
Image Similarity ◽  
Spectral Bands ◽  
Quantitative Image ◽  
Sentinel 2

In this paper results from the evaluation of several state-of-the-art pansharpening techniques are presented for the VNIR and SWIR bands of Sentinel-2. A procedure for the pansharpening is also proposed which aims at respecting the closest spectral similarities between the higher and lower resolution bands. The evaluation included 21 different fusion algorithms and three evaluation frameworks based both on standard quantitative image similarity indexes and qualitative evaluation from remote sensing experts. The overall analysis of the evaluation results indicated that remote sensing experts disagreed with the outcomes and method ranking from the quantitative assessment. The employed image quality similarity indexes and quantitative evaluation framework based on both high and reduced resolution data from the literature didn’t manage to highlight/evaluate mainly the spatial information that was injected to the lower resolution images. Regarding the SWIR bands none of the methods managed to deliver significantly better results than a standard bicubic interpolation on the original low resolution bands.

scholarly journals PANSHARPENING ON THE NARROW VNIR AND SWIR SPECTRAL BANDS OF SENTINEL-2

2016 ◽  
Vol XLI-B7 ◽  
pp. 723-730
Author(s):  
A. D. Vaiopoulos ◽  
K. Karantzalos
Keyword(s):  
Remote Sensing ◽  
Quantitative Assessment ◽  
Spatial Information ◽  
State Of The Art ◽  
Qualitative Evaluation ◽  
Evaluation Framework ◽  
Image Similarity ◽  
Spectral Bands ◽  
Quantitative Image ◽  
Sentinel 2

In this paper results from the evaluation of several state-of-the-art pansharpening techniques are presented for the VNIR and SWIR bands of Sentinel-2. A procedure for the pansharpening is also proposed which aims at respecting the closest spectral similarities between the higher and lower resolution bands. The evaluation included 21 different fusion algorithms and three evaluation frameworks based both on standard quantitative image similarity indexes and qualitative evaluation from remote sensing experts. The overall analysis of the evaluation results indicated that remote sensing experts disagreed with the outcomes and method ranking from the quantitative assessment. The employed image quality similarity indexes and quantitative evaluation framework based on both high and reduced resolution data from the literature didn’t manage to highlight/evaluate mainly the spatial information that was injected to the lower resolution images. Regarding the SWIR bands none of the methods managed to deliver significantly better results than a standard bicubic interpolation on the original low resolution bands.

scholarly journals A Web-Based GIS and Remote Sensing Framework for Spatial Information System (SIS): A Case Study in Nuwaraeliya, Sri Lanka

2010 ◽  
Vol 19 (4) ◽  
pp. 106-116 ◽  
Author(s):  
P. K. S. C. Jayasinghe ◽  
H. A. Adornado ◽  
Masao Yoshida ◽  
D. A. L. Leelamanie
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Sri Lanka ◽  
Spatial Information ◽  
Web Based ◽  
Gis And Remote Sensing

Spatiotemporal monitoring of surface soil moisture using optical remote sensing data: a case study in a semi-arid area

2018 ◽  
Vol 65 (3) ◽  
pp. 481-499 ◽  
Author(s):  
Rida Khellouk ◽  
Ahmed Barakat ◽  
Abdelghani Boudhar ◽  
Rachid Hadria ◽  
Hayat Lionboui ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Arid Area ◽  
Optical Remote Sensing ◽  
Surface Soil Moisture ◽  
Sensing Data ◽  
Semi Arid

scholarly journals Mining impacts on forest cover change in a tropical forest using remote sensing and spatial information from 2001–2019: A case study of Odisha (India)

2022 ◽  
Vol 302 ◽  
pp. 114067
Author(s):  
Manoranjan Mishra ◽  
Celso Augusto Guimarães Santos ◽  
Thiago Victor Medeiros do Nascimento ◽  
Manoj Kumar Dash ◽  
Richarde Marques da Silva ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Tropical Forest ◽  
Forest Cover ◽  
Spatial Information ◽  
Forest Cover Change ◽  
Mining Impacts

scholarly journals An Oil Well Dataset Derived from Satellite-Based Remote Sensing

2021 ◽  
Vol 13 (6) ◽  
pp. 1132
Author(s):  
Zhibao Wang ◽  
Lu Bai ◽  
Guangfu Song ◽  
Jie Zhang ◽  
Jinhua Tao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Object Detection ◽  
State Of The Art ◽  
Google Earth ◽  
Oil Wells ◽  
Oil Well ◽  
Optical Remote Sensing ◽  
Learning Models ◽  
Remote Sensing Images

Estimation of the number and geo-location of oil wells is important for policy holders considering their impact on energy resource planning. With the recent development in optical remote sensing, it is possible to identify oil wells from satellite images. Moreover, the recent advancement in deep learning frameworks for object detection in remote sensing makes it possible to automatically detect oil wells from remote sensing images. In this paper, we collected a dataset named Northeast Petroleum University–Oil Well Object Detection Version 1.0 (NEPU–OWOD V1.0) based on high-resolution remote sensing images from Google Earth Imagery. Our database includes 1192 oil wells in 432 images from Daqing City, which has the largest oilfield in China. In this study, we compared nine different state-of-the-art deep learning models based on algorithms for object detection from optical remote sensing images. Experimental results show that the state-of-the-art deep learning models achieve high precision on our collected dataset, which demonstrate the great potential for oil well detection in remote sensing.

scholarly journals Deep Learning Classification of Cheatgrass Invasion in the Western United States Using Biophysical and Remote Sensing Data

2021 ◽  
Vol 13 (7) ◽  
pp. 1246
Author(s):  
Kyle B. Larson ◽  
Aaron R. Tuor
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Biological Invasions ◽  
Spatial Information ◽  
Wildlife Habitat ◽  
Ground Sample ◽  
Sensing Applications ◽  
Combining Data ◽  
Remote Sensing Applications

Cheatgrass (Bromus tectorum) invasion is driving an emerging cycle of increased fire frequency and irreversible loss of wildlife habitat in the western US. Yet, detailed spatial information about its occurrence is still lacking for much of its presumably invaded range. Deep learning (DL) has demonstrated success for remote sensing applications but is less tested on more challenging tasks like identifying biological invasions using sub-pixel phenomena. We compare two DL architectures and the more conventional Random Forest and Logistic Regression methods to improve upon a previous effort to map cheatgrass occurrence at >2% canopy cover. High-dimensional sets of biophysical, MODIS, and Landsat-7 ETM+ predictor variables are also compared to evaluate different multi-modal data strategies. All model configurations improved results relative to the case study and accuracy generally improved by combining data from both sensors with biophysical data. Cheatgrass occurrence is mapped at 30 m ground sample distance (GSD) with an estimated 78.1% accuracy, compared to 250-m GSD and 71% map accuracy in the case study. Furthermore, DL is shown to be competitive with well-established machine learning methods in a limited data regime, suggesting it can be an effective tool for mapping biological invasions and more broadly for multi-modal remote sensing applications.

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