Because the reliability of feature for every pixel determines the accuracy of classification, it is important to design a specialized feature mining algorithm for hyperspectral image classification. We propose a feature learning algorithm, contextual deep learning, which is extremely effective for hyperspectral image classification. On the one hand, the learning-based feature extraction algorit...

Hyperspectral imaging sensors exibit high spectral resolution, but normally low spatial resolution. This leads to spectral signatures of pixels originating from different object types. Such pixels are called mixed pixels. Spectral unmixing methods can be employed to estimate the fractions of reflected light from the different objects within the pixel area. However, spectral unmixing does not pr...

Real-time hyperspectral image classification is a necessary primitive in many remotely sensed image analysis applications. Previous work has shown that Support Vector Machines (SVMs) can achieve high classification accuracy, but unfortunately it is very computationally expensive.This paper presents a scalable dataflow accelerator on FPGA for real-time SVM classification of hyperspectral images....

Image-level classification from hyperspectral images (HSI) has seldom been addressed in the literature. Instead, traditional classification methods have focused on individual pixels. Pixel-level HSI classification comes at a high computational burden though. In this paper, we present a novel pipeline for classification at image-level, where each band in the HSI is considered as a separate image...

The rapid development of space and computer technologies allows for the possibility to store huge amounts of remotely sensed image data, collected using airborne and satellite instruments. In particular, NASA is continuously gathering high-dimensional image data with Earth observing hyperspectral sensors such as the Jet Propulsion Laboratory’s airborne visible–infrared imaging spectrometer (AVI...

A multiscale approach to hyperspectral image data analysis using fractal signatures was proposed and implemented in the Interactive Data Language (IDL). For 2-D hyperspectral curves, fractal signature measures the changes in curve length with changing scale. Using NASA’s Earth Observing-1 (EO-1) Hyperion image from a study area near Denton, Texas, USA, the capabilities of fractal signatures in ...

In the last few years, hyperspectral imaging technique has had a bright future for application on nondestructive detection of agricultural products. However, in experiment, there are usually dozens or even hundreds of hyperspectral images. It needs to spend lots of time in preprocessing all the hyperspectral data. Software ENVI is a kind of image processing software, although the users can defi...

Hyperspectral imaging is increasingly used in various environmental measurement applications. Airborne hyperspectral sensors enables ground sampling distances (GSD) below 1 meter with high spectral resolution. In order to utilize this data in quantitative remote sensing applications, an accurate radiometric correction of the imagery has to be performed. In this article we present a radiometric ...

The idea of using artificial neural network has been proven useful for hyperspectral image classification. However, the high dimensionality of hyperspectral images usually leads to the failure of constructing an effective neural network classifier. To improve the performance of neural network classifier, wavelet-based feature extraction algorithms can be applied to extract useful features for h...

Hyperspectral images are generated by collecting hundreds of narrow and contiguously spaced spectral bands of data producing a highly correlated long sequence of images. In this study, we investigate and compare the performance of several three-dimensional embedded wavelet algorithms for compression of hyperspectral images. They are 3D-SPIHT, AT3DSPIHT, 3D-SPECK (Three-Dimensional Set Partition...