Cloud Removal from Satellite Images Using Auto Associative Neural Network and Stationary Wevlet Transform

Sahoo, Tapasmini; Patnaik, Suprava

doi:10.1109/icetet.2008.99

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…The two temporally based methods mentioned above aim to establish a clear functional relationship between cloud-contaminated images and cloud-free images in the time domain. In contrast, temporal learning methods, such as compressed sensing [26], neural networks [27], machine learning [28], etc., intend to learn the relationship by themselves. One of the most typical temporal learning methods is dictionary learning, which aims to find a representation of data by learning a set of basis vectors and atoms from the given data [29].…”

Section: Related Workmentioning

confidence: 99%

A Flexible Spatiotemporal Thick Cloud Removal Method with Low Requirements for Reference Images

Zhang,

Ji,

et al. 2023

Remote Sensing

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Thick cloud and shadows have a significant impact on the availability of optical remote sensing data. Although various methods have been proposed to address this issue, they still have some limitations. First, most approaches rely on a single clear reference image as complementary information, which becomes challenging when the target image has large missing areas. Secondly, the existing methods that can utilize multiple reference images require the complementary data to have high temporal correlation, which is not suitable for situations where the difference between the reference image and the target image is large. To overcome these limitations, a flexible spatiotemporal deep learning framework based on generative adversarial networks is proposed for thick cloud removal, which allows for the use of three arbitrary temporal images as references. The framework incorporates a three-step encoder that can leverage the uncontaminated information from the target image to assimilate the reference images, enhancing the model’s ability to handle reference images with diverse temporal differences. A series of simulated and real experiments on Landsat 8 and Sentinel 2 data is performed to demonstrate the effectiveness of the proposed method. The proposed method is especially applicable to small/large-scale regions with reference images that are significantly different from the target image.

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Section: Related Workmentioning

confidence: 99%

A Flexible Spatiotemporal Thick Cloud Removal Method with Low Requirements for Reference Images

Zhang,

Ji,

et al. 2023

Remote Sensing

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“…Tapasmini Sahoo [4] used an image fusion technique to remove clouds from satellite images. The proposed method involves an auto associative neural network based PCAT (principal component transform) and SWT (stationary wavelet transform) to remove clouds recursively which integrates complementary information to form a composite image from multi-temporal images.…”

Section: Introductionmentioning

confidence: 99%

Cloud Processing for NRSC Imagery to Acquire Reservoir Details

Balan¹,

James²,

Kapadia³

et al. 2015

IJERT

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A significant obstacle of extracting information using satellite imagery is the presence of clouds. Reservoir boundaries cannot be extracted as the images are covered with clouds. The problem can be mostly resolved by mosaicking the cloud areas with the cloud free areas in other temporal images. In this project, a complete approach, including image enhancement, cloud detection and cloud areas mosaicking, is proposed to generate cloud free images from multi-temporal satellite images.

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“…One possible cause of data gaps is cloud cover. Cloud cover is recognized as a source of significant loss of information and data quality by many scientific studies [3][4][5][6][7]. The existence of clouds hinders the extraction of meaningful information because they are a considerable source of uncertainty with regard to the application of any algorithm aiming at the retrieval of land surface parameters.…”

Section: Introductionmentioning

confidence: 99%

Removal of Optically Thick Clouds from Multi-Spectral Satellite Images Using Multi-Frequency SAR Data

et al. 2013

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This study presents a method for the reconstruction of pixels contaminated by optical thick clouds in multi-spectral Landsat images using multi-frequency SAR data. A number of reconstruction techniques have already been proposed in the scientific literature. However, all of the existing techniques have certain limitations. In order to overcome these limitations, we expose the Closest Spectral Fit (CSF) method proposed by Meng et al. to a new, synergistic approach using optical and SAR data. Therefore, the term Closest Feature Vector (CFV) is introduced. The technique facilitates an elegant way to avoid radiometric distortions in the course of image reconstruction. Furthermore the cloud cover removal is independent from underlying land cover types and assumptions on seasonality, etc. The methodology is applied to mono-temporal, multi-frequency SAR data from TerraSAR-X (X-Band), ERS (C-Band) and ALOS Palsar (L-Band). This represents a way of thinking about Radar data not as foreign, but as additional data source in multi-spectral remote sensing. For the assessment of the image restoration performance, an experimental framework is established and a statistical evaluation protocol is designed. The results show the potential of a synergistic usage of multi-spectral and SAR data to overcome the loss of data due to cloud cover.

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Cloud Removal from Satellite Images Using Auto Associative Neural Network and Stationary Wevlet Transform

Cited by 5 publications

References 6 publications

A Flexible Spatiotemporal Thick Cloud Removal Method with Low Requirements for Reference Images

A Flexible Spatiotemporal Thick Cloud Removal Method with Low Requirements for Reference Images

Cloud Processing for NRSC Imagery to Acquire Reservoir Details

Removal of Optically Thick Clouds from Multi-Spectral Satellite Images Using Multi-Frequency SAR Data

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