scholarly journals 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

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.

Impact of Observational Environment Change on Air Temperature Based on High-Spatial-Resolution Satellite Remote Sensing Data

2020 ◽  
Vol 40 (10) ◽  
pp. 1028001
Author(s):  
陈世涵 Chen Shihan ◽  
李玲 Li Ling ◽  
蒋弘凡 Jiang Hongfan ◽  
居伟杰 Ju Weijie ◽  
张曼玉 Zhang Manyu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Air Temperature ◽  
Satellite Remote Sensing ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Environment Change ◽  
Sensing Data

scholarly journals Damage Assessment of a Salt Dome in Jizan, Southwestern Saudi Arabia, Using High Spatial Resolution Remote Sensing Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Habes Ghrefat ◽  
Ahmed Hakami ◽  
Elkhedr Ibrahim ◽  
Saad Mogren ◽  
Saleh Qaysi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Spatial Resolution ◽  
Damage Assessment ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Salt Dome ◽  
Visual Interpretation ◽  
Sensing Data ◽  
Residential Neighborhood

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.

scholarly journals Convolutional Neural Networks for High Spatial Resolution Remote Sensing Image Classification

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).

scholarly journals INTEGRATION OF REMOTE SENSING DATA AND BASIC GEODATA AT DIFFERENT SCALE LEVELS FOR IMPROVED LAND USE ANALYSES

2015 ◽  
Vol XL-3/W3 ◽  
pp. 85-89 ◽  
Author(s):  
G. Waldhoff ◽  
S. Eichfuss ◽  
G. Bareth
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Spatial Resolution ◽  
Geographical Information Systems ◽  
High Spatial Resolution ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Geographical Information ◽  
Sensing Data ◽  
Very High Spatial Resolution

The classification of remote sensing data is a standard method to retrieve up-to-date land use data at various scales. However, through the incorporation of additional data using geographical information systems (GIS) land use analyses can be enriched significantly. In this regard, the Multi-Data Approach (MDA) for the integration of remote sensing classifications and official basic geodata for a regional scale as well as the achievable results are summarised. On this methodological basis, we investigate the enhancement of land use analyses at a very high spatial resolution by combining WorldView-2 remote sensing data and official cadastral data for Germany (the Automated Real Estate Map, ALK). Our first results show that manifold thematic information and the improved geometric delineation of land use classes can be gained even at a high spatial resolution.

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