Supervised machine learning of fused RADAR and optical data for land cover classification

Cervone, Guido; Haack, Barry

doi:10.1117/1.jrs.6.063597

Cited by 10 publications

(2 citation statements)

References 33 publications

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“…Fuzzy decision fusion schemes for urban remote sensing classification were explored in [72]. In the context of optical data fusion, Cervone and Haack [73] applied three supervised classification machine learning algorithms, namely a decision rule, a decision tree and a Bayesian classifier. On the other hand, moment features from an SVM classifier are exploited [74] and combined using the MV gas decision fusion rule.…”

Section: Sar and Optical Datamentioning

confidence: 99%

Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification—A Review

Papadopoulos,

Koukiou,

Anastassopoulos

2024

J. Imaging

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According to existing signatures for various kinds of land cover coming from different spectral bands, i.e., optical, thermal infrared and PolSAR, it is possible to infer about the land cover type having a single decision from each of the spectral bands. Fusing these decisions, it is possible to radically improve the reliability of the decision regarding each pixel, taking into consideration the correlation of the individual decisions of the specific pixel as well as additional information transferred from the pixels’ neighborhood. Different remotely sensed data contribute their own information regarding the characteristics of the materials lying in each separate pixel. Hyperspectral and multispectral images give analytic information regarding the reflectance of each pixel in a very detailed manner. Thermal infrared images give valuable information regarding the temperature of the surface covered by each pixel, which is very important for recording thermal locations in urban regions. Finally, SAR data provide structural and electrical characteristics of each pixel. Combining information from some of these sources further improves the capability for reliable categorization of each pixel. The necessary mathematical background regarding pixel-based classification and decision fusion methods is analytically presented.

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Section: Sar and Optical Datamentioning

confidence: 99%

Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification—A Review

Papadopoulos,

Koukiou,

Anastassopoulos

2024

J. Imaging

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“…This spatial information can be extracted as textural information from the pixels (Cervone and Haack 2012;Champion et al 2008;Chen, Stow, and Gong 2004;Kurosu et al 1999). Traditional digital image classification methodologies are based purely on the use of the spectral characteristics of the data, thus ignoring any spatial information in the data collected (Maillard 2003).…”

Section: Texture Analysismentioning

confidence: 99%

Comparison and integration of spaceborne optical and radar data for mapping in Sudan

Idol

Haack

Mahabir

2015

International Journal of Remote Sensing

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The purpose of this study was to determine how different procedures and data, such as multiple wavelengths of radar imagery and radar texture measures, independently and in combination with optical imagery influence land-cover/use classification accuracies for a study site in Sudan. Radarsat-2 C-band and phased array L-band synthetic aperture radar (PALSAR) L-band quad-polarized radar were registered with ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) optical data. Spectral signatures were obtained for multiple landscape features, classified using a maximum-likelihood decision rule, and thematic accuracies were obtained using separate validation data. There were surprising differences between the thematic accuracies of the two radar data sets, with Radarsat-2 only having a 51% accuracy and PALSAR 73%. In contrast, the optical ASTER overall accuracy was 81%. Combining the original radar and a variance texture measure increased the Radarsat-2 to 78% and PALSAR to 80%, whereas the two original radar bands together had an accuracy of 87%. Sensor fusion of optical and radar obtained an accuracy of 93%. Based on these results, the use of multiwavelength quad-polarized radar imagery combined or integrated with optical imagery has great potential in improving the accuracy of landcover/use classifications. In tropical and high-latitude regions of the world, where persistent cloud cover hinders the use of optical satellite systems, land management programmes may find this research promising.

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Using Twitter for tasking remote-sensing data collection and damage assessment: 2013 Boulder flood case study

Cervone

Sava

Huang

et al. 2015

International Journal of Remote Sensing

174

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Supervised machine learning of fused RADAR and optical data for land cover classification

Cited by 10 publications

References 33 publications

Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification—A Review

Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification—A Review

Comparison and integration of spaceborne optical and radar data for mapping in Sudan

Using Twitter for tasking remote-sensing data collection and damage assessment: 2013 Boulder flood case study

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