&lt;title&gt;Analysis of HYDICE noise characteristics and their impact on subpixel object detection&lt;/title&gt;

Nischan, Melissa L.; Kerekes, John P.; Baum, Jerrold E.; Basedow, Robert W.

doi:10.1117/12.366274

Cited by 21 publications

(10 citation statements)

References 6 publications

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“…However, spectrometers still suffer from significant band-to-band correlation, resulting in dimensionality issues and consequently reducing the total amount of available bands-even down to the level of multispectral systems (Chang & Du, 1999;Chang, 1999;Guo et al, 2006). In addition signal-to-noise ratios are still low in certain systems and even fractions of minimal noise might introduce considerable errors (Nischan et al, 1999;Okin et al, 2001). Data volume is another tradeoff to be considered when designing imaging spectrometers, mainly due to limited downlink capacities from satellites to ground stations.…”

Section: Limitations and Tradeoffs When Using Imaging Spectrometer Datamentioning

confidence: 98%

Earth system science related imaging spectroscopy—An assessment

Schaepman

Ustin

Plaza

et al. 2009

Remote Sensing of Environment

316

166

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Section: Limitations and Tradeoffs When Using Imaging Spectrometer Datamentioning

confidence: 98%

Earth system science related imaging spectroscopy—An assessment

Schaepman

Ustin

Plaza

et al. 2009

Remote Sensing of Environment

316

166

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“…Calculated using equation (10) with N equal to number of pixels of the minimum dimension of the target..…”

Section: Evaluation Resultsmentioning

confidence: 99%

“…Table 1 provides the details regarding each of these images. The signal-to-noise ratio (SNR) reported here is the approximate value across atmospheric window spectral regions using a nominal scene radiance and the noise level calculated from calibration sequences as discussed in Nischan, et al 10 . Note for these images, the sensor was found to be fixed noise dominated, and the SNR varies directly with the integration time.…”

Section: Run05mentioning

confidence: 99%

A comparative evaluation of spectral quality metrics for hyperspectral imagery

2005

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Quantitative methods to assess or predict the quality of a spectral image are the subject of a number of current research activities. An accepted methodology would be highly desirable to use for data collection tasking or data archive searches in way analogous to the current uses of the National Imagery Interpretation Rating Scale (NIIRS) General Image Quality Equation (GIQE). A number of approaches to the estimation of quality of a spectral image have been published. An issue with many of these approaches is that they tend to be constructed around specific tasks (target detection, background classification, etc.) While this has often been necessary to make the quality assessment tractable, it is desirable to have a method that is more general. One such general approach is presented in a companion paper (Simmons, et al 1 ). This new approach seeks to get at the heart of the general spectral imagery quality analysis problemassessing the confidence of an image analyst in performing a specified task with a specific spectral image. In this approach the quality from spatial and spectral aspects of the imagery are treated separately and then a fusion concept known as "semantic transformation" is used to combine the utility, or confidence, from these two aspects into an overall quality metric. This paper compares and contrasts the various methods published in the literature with this new General Spectral Utility Metric (GSUM). In particular, the methods are applied to a target detection problem using data from the airborne HYDICE instrument collected at Forest Radiance I. While the GSUM approach is seen to lead to intuitively pleasing results, its sensitivity to image parameters was not seen to be consistent with previously published approaches. However, this likely resulted more from limitations of the previous approaches than with problems with GSUM. Further studies with additional spectral imaging applications are recommended along with efforts to integrate a performance predication capability into the GSUM framework.

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“…Another well-known aerial spectrometer is the the Reflective Optics System Imaging Spectrometer (ROSIS-3) [3], which covers the region from 0.43-0.86 µm with more than 100 spectral bands at intervals of 4nm. Other aerial spectrometers are the Hyperspectral Digital Imagery Collection Experiment (HYDICE) [4], the HyMAP [5,6], the PROBE-1 [7] or the family of spectrometers composed by the Compact Airborne Spectrographic Imager (CASI), which capture information in the visible/near infrared regions, and its related SASI and TASI, which capture information in the short wave infrared and also in the thermal infrared, respectively [8][9][10]. Focusing on spaceborne sensors, we can highlight the Earth Observing-1 (EO-1) Hyperion [11,12], which also records information in the range 0.4-2.5 µm with 10nm spectral resolution, obtaining data cubes with 220 spectral bands.…”

Section: Hyperspectral Imaging Concept and Missionsmentioning

confidence: 99%

Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification

et al. 2018

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Deep neural networks (DNNs) have emerged as a relevant tool for the classification of remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) being the current state-of-the-art in many classification tasks. However, deep CNNs present several limitations in the context of HSI supervised classification. Although deep models are able to extract better and more abstract features, the number of parameters that must be fine-tuned requires a large amount of training data (using small learning rates) in order to avoid the overfitting and vanishing gradient problems. The acquisition of labeled data is expensive and time-consuming, and small learning rates forces the gradient descent to use many small steps to converge, slowing down the runtime of the model. To mitigate these issues, this paper introduces a new deep CNN framework for spectral-spatial classification of HSIs. Our newly proposed framework introduces shortcut connections between layers, in which the feature maps of inferior layers are used as inputs of the current layer, feeding its own output to the rest of the the upper layers. This leads to the combination of various spectral-spatial features across layers that allows us to enhance the generalization ability of the network with HSIs. Our experimental results with four well-known HSI datasets reveal that the proposed deep&dense CNN model is able to provide competitive advantages in terms of classification accuracy when compared to other state-of-the-methods for HSI classification.

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<title>Analysis of HYDICE noise characteristics and their impact on subpixel object detection</title>

Cited by 21 publications

References 6 publications

Earth system science related imaging spectroscopy—An assessment

Earth system science related imaging spectroscopy—An assessment

A comparative evaluation of spectral quality metrics for hyperspectral imagery

Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification

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