The algorithm for retrieving surface albedo via multispectral remote sensing data of high spatial resolution

The salt dome in Jizan, southwestern Saudi Arabia, has caused several problems related to underground dissolution, particularly in the old part of the city. Examples of these problems include surface collapse, building failure, fracturing, tilting, and road cracking. Analysis of the salt dome using X-ray diffraction (XRD) revealed the dominance of gypsum, anhydrite, and halite. This study evaluates the damage assessment using multitemporal high spatial resolution data of the GeoEye-1, and QuickBird-2 sensors. Change detection technique, textural analysis, and visual interpretation were applied to these data. Analysis of the data recorded before and after a particular damage event revealed that three neighborhoods located above the Jizan salt dome—Al-Ashaima, Shamiya, and Aljabal—were affected to the greatest extent. The entire residential neighborhood of Al-Ashaima was evacuated, and the buildings located in it were demolished. Several buildings in the Shamiya and Aljabal neighborhoods were also demolished. Therefore, high spatial remote sensing data are effective in assessing building damage and for anticipating future damage, thus benefiting decision making for the affected cities.

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SINGLE-IMAGE SUPER RESOLUTION FOR MULTISPECTRAL REMOTE SENSING DATA USING CONVOLUTIONAL NEURAL NETWORKS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b3-883-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 883-890 ◽

Cited By ~ 33

Author(s):

L. Liebel ◽

M. Körner

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Spatial Resolution ◽

Super Resolution ◽

Remote Sensing Data ◽

Single Image ◽

Sensing Data ◽

Multispectral Remote Sensing ◽

Image Super Resolution ◽

Single Image Super Resolution

In optical remote sensing, spatial resolution of images is crucial for numerous applications. Space-borne systems are most likely to be affected by a lack of spatial resolution, due to their natural disadvantage of a large distance between the sensor and the sensed object. Thus, methods for single-image super resolution are desirable to exceed the limits of the sensor. Apart from assisting visual inspection of datasets, post-processing operations—e.g., segmentation or feature extraction—can benefit from detailed and distinguishable structures. In this paper, we show that recently introduced state-of-the-art approaches for single-image super resolution of conventional photographs, making use of deep learning techniques, such as convolutional neural networks (CNN), can successfully be applied to remote sensing data. With a huge amount of training data available, end-to-end learning is reasonably easy to apply and can achieve results unattainable using conventional handcrafted algorithms. We trained our CNN on a specifically designed, domain-specific dataset, in order to take into account the special characteristics of multispectral remote sensing data. This dataset consists of publicly available SENTINEL-2 images featuring 13 spectral bands, a ground resolution of up to 10m, and a high radiometric resolution and thus satisfying our requirements in terms of quality and quantity. In experiments, we obtained results superior compared to competing approaches trained on generic image sets, which failed to reasonably scale satellite images with a high radiometric resolution, as well as conventional interpolation methods.

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Pattern recognition of forest canopy using the airborne hyperspectral data and multi-bands high spatial resolution satellite sensor worldview-2 data. A results comparison and accuracy estimation

Исследования Земли из космоса ◽

10.31857/s0205-96142019689-102 ◽

2019 ◽

pp. 89-102

Author(s):

V. V. Kozoderov ◽

V. D. Egorov

Keyword(s):

Remote Sensing ◽

Pattern Recognition ◽

Spatial Resolution ◽

Forest Canopy ◽

High Spatial Resolution ◽

Remote Sensing Data ◽

Hyperspectral Data ◽

Automatic Data ◽

Sensing Data ◽

Satellite Sensor

Pattern recognition of forest surface from remote sensing data: using the airborne hyperspectral data and using multi-bands high spatial resolution satellite sensor WorldView‑2 data are investigated. The early proposed method and standard QDA method for calculations were used. A comparison of calculations results were conducted. A recognition calculation accuracy range for airborne and satellite remote sensing data for three forest surface fragments for different created data bases for recognition system has been assessed. Some opportunities of automatic data preparing of created system were displayed. Some special features of pattern recognition of forest surfaces from hyperspectral airborne data and from multi-bands high spatial resolution satellite data were discussed.

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Convolutional Neural Networks for High Spatial Resolution Remote Sensing Image Classification

10.21203/rs.3.rs-953414/v1 ◽

2021 ◽

Author(s):

Rajagopal T K P ◽

Sakthi G ◽

Prakash J

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Image Classification ◽

Convolutional Neural Networks ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Remote Sensing Data ◽

Remote Sensing Image ◽

Sensing Data

Abstract Hyperspectral remote sensing based image classification is found to be a very widely used method employed for scene analysis that is from a remote sensing data which is of a high spatial resolution. Classification is a critical task in the processing of remote sensing. On the basis of the fact that there are different materials with reflections in a particular spectral band, all the traditional pixel-wise classifiers both identify and also classify all materials on the basis of their spectral curves (or pixels). Owing to the dimensionality of the remote sensing data of high spatial resolution along with a limited number of labelled samples, a remote sensing image of a high spatial resolution tends to suffer from something known as the Hughes phenomenon which can pose a serious problem. In order to overcome such a small-sample problem, there are several methods of learning like the Support Vector Machine (SVM) along with the other methods that are kernel based and these were introduced recently for a remote sensing classification of the image and this has shown a good performance. For the purpose of this work, an SVM along with Radial Basis Function (RBF) method was proposed. But, a feature learning approach for the classification of the hyperspectral image is based on the Convolutional Neural Networks (CNNs). The results of the experiment that were based on various image datasets that were hyperspectral which implies that the method proposed will be able to achieve a better performance of classification compared to other traditional methods like the SVM and the RBF kernel and also all conventional methods based on deep learning (CNN).

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