Multi- and hyperspectral cameras on drones can be valuable tools in environmental monitoring. A significant shortcoming complicating their usage quantitative remote sensing applications is insufficient robust radiometric calibration methods. In a direct reflectance transformation method, the drone equipped with camera an irradiance sensor, allowing of image pixel values to factors without groun...

Deep learning approaches based on convolutional neural networks (CNNs) have recently achieved success in computer vision, demonstrating significant superiority the domain of image processing. For hyperspectral (HSI) classification, are an efficient option. Hyperspectral classification often spectral information. Convolutional used for order to achieve greater performance. The complex computatio...

Endmember extraction and spectral unmixing is a very challenging task in multispectral/hyperspectral image processing due to the incompleteness of information. In this paper, a new method for endmember extraction and spectral unmixing of hyperspectral images is proposed, which is called as minimum distance constrained nonnegative matrix factorization (MDC-NMF). After being compared with a newly...

Hyperspectral imaging holds enormous potential to improve the state-of-the-art in aerial vehicle tracking with low spatial and temporal resolutions. Recently, adaptive multi-modal hyperspectral sensors, controlled by Dynamic Data Driven Applications Systems (DDDAS) methodology, have attracted growing interest due to their ability to record extended data quickly from the aerial platforms. In thi...

The correlation coefficient (CC) was substantiated as a simple, yet robust statistical tool in the quality assessment of hyperspectral imaging (HSI) data. The sensitivity of the metric was also characterized with respect to artificially-induced errors. The CC was found to be sensitive to spectral shifts and single feature modifications in hyperspectral ground data despite the high, artificially...

This study explored a combination of hyperspectral and lidar systems for vegetation mapping in the Florida Everglades. A framework was designed to integrate two remotely sensed datasets and four data processing techniques. Lidar elevation and intensity features were extracted from the original point cloud data to avoid the errors and uncertainties in the raster-based lidar methods. Lidar signif...

Hyperspectral images can be represented either as a set of images or as a set of spectra. Spectral classification and segmentation and data reduction are the main problems in hyperspectral image analysis. In this paper we propose a Bayesian estimation approach with an appropriate hiearchical model with hidden markovian variables which gives the possibility to jointly do data reduction, spectral...

Hyperspectral imaging comprises the technologies that incorporates remote sensing and analysis of an object or specific area of the earth at different distances with very large number of bands. Currently, a wide range of hyperspectral data sets are obtained continuously, in addition to conventional multispectral remote sensing images, and presented to users by institutions for both commercial a...

Hyperspectral images are one of the most important fundamental and strategic information resources, imaging the same ground object with hundreds of spectral bands varying from the ultraviolet to the microwave. With the emergence of huge volumes of high-resolution hyperspectral images produced by all sorts of imaging sensors, processing and analysis of these images requires effective retrieval t...