ROSIS (Reflective Optics System Imaging Spectrometer) - A Candidate Instrument For Polar Platform Missions

Kunkel, B.; Blechinger, Fritz; Lutz, R.; Doerffer, R.; Piepen, Heinz van der; Schröder, Marc

doi:10.1117/12.943611

Cited by 68 publications

(30 citation statements)

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“…Nine land-cover categories were selected and the number of samples for each category is given in Error! Reference source not found.. Pavia University dataset was collected by the Reflective Optics System Imaging Spectrometer (ROSIS) sensor [25] during a flight campaign over the city of Pavia, in northern Italy. The dataset has 115 spectral bands in range of 0.43 to 0.86 µm with 610 × 340 pixels.…”

Section: Datasetsmentioning

confidence: 99%

A New Dataset and Deep Residual Spectral Spatial Network for Hyperspectral Image Classification

Xue¹,

Zeng²,

Chen

et al. 2020

Symmetry

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Due to the limited varieties and sizes of existing public hyperspectral image (HSI) datasets, the classification accuracies are higher than 99% with convolutional neural networks (CNNs). In this paper, we presented a new HSI dataset named Shandong Feicheng, whose size and pixel quantity are much larger. It also has a larger intra-class variance and a smaller inter-class variance. State-of-the-art methods were compared on it to verify its diversity. Otherwise, to reduce overfitting caused by the imbalance between high dimension and small quantity of labeled HSI data, existing CNNs for HSI classification are relatively shallow and suffer from low capacity of feature learning. To solve this problem, we proposed an HSI classification framework named deep residual spectral spatial setwork (DRSSN). By using shortcut connection structure, which is an asymmetry structure, DRSSN can be deeper to extract features with better discrimination. In addition, to alleviate insufficient training caused by unbalanced sample sizes between easily and hard classified samples, we proposed a novel training loss function named sample balanced loss, which allocated weights to the losses of samples according to their prediction confidence. Experimental results on two popular datasets and our proposed dataset showed that our proposed network could provide competitive results compared with state-of-the-art methods.

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Section: Datasetsmentioning

confidence: 99%

A New Dataset and Deep Residual Spectral Spatial Network for Hyperspectral Image Classification

Xue¹,

Zeng²,

Chen

et al. 2020

Symmetry

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“…La imagen University of Pavia (UP) fue obtenida por el Reflective Optics System Imaging Spectrometer (ROSIS) (Kunkel et al, 1988) durante una campaña de vuelo sobre la ciudad italiana de Pavia, concretamente sobre parte del campus universitario. La imagen cubre un entorno urbano con varias estructuras sólidas, elementos naturales y sombras.…”

Section: Dataset Hiperespectralesunclassified

Estudio Comparativo de Técnicas de Clasificación de Imágenes Hiperespectrales

Paoletti

Haut

Plaza

2019

Rev. iberoam. autom. inform. ind.

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ResumenLas imágenes hiperespectrales constituyen el núcleo de varios programas de observación remota de la Tierra. La cantidad de información que contienen estas imágenes, formadas por cientos de canales espectrales estrechos y casi continuos, resulta de gran utilidad en aplicaciones en las que la caracterización de los materiales observados en la superficie terrestre resulta de gran relevancia. Esto se debe a la posibilidad de caracterizar de forma inequívoca cada material a través de su firma espectral. Algunas de estas aplicaciones son la agricultura de precisión, la planificación de espacios urbanos, o la prevención y seguimiento de desastres naturales. Sin embargo, la gran dimensión de las imágenes hiperespectrales supone un reto en su tratamiento, almacenamiento y procesamiento, debido a la gran variabilidad espectral y la correlación existente en los datos. En la literatura se han desarrollado múltiples algoritmos de análisis de imágenes hiperespectrales. En este artículo revisamos los algoritmos más utilizados para la clasificación de este tipo de imágenes, realizando experimentos con tres imágenes públicas y presentando una comparativa entre los métodos más ampliamente utilizados en este campo. AbstractHyperspectral images are very important in many Earth Observation programs. The large amount of information is contained in hyperspectral images (hundreds of narrow and continuous spectral channels) is very useful for applications in which the characterization of the Earth surface materials is relevant. This is due to the fact that each observed element can be uniquely characterized by its spectral signature, for instance in precision agriculture, urban planning or detection/prevention of natural disasters, among others. However, the large dimensionality of hyperspectral images represents a challenge for analysis algorithms, both from the storage and processing viewpoints, resulting from data variability and correlation. Several algorithms have been proposed in the literature for the analysis of hyperspectral images. In this paper, we review the most popular techniques for hyperspectral classification. These techniques are inter-compared using three publicly available hyperspectral data sets.

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“…The shape of an adaptive region represents the contextual features surrounding a central pixel, and the size of the adaptive region is constrained by two predefined thresholds (T1 and T2) in the spectral and spatial domains. From the investigation in [41], the proposed adaptive region has an advantage in considering contextual information in an adaptive spatial domain (see [42] for more details). Three examples are given in Figure 2 to show the shape-adaptive ability of the proposed region extension method.…”

Section: D-amvs Approach For Refining Initial Classification Mapsmentioning

confidence: 99%

“…The first data set is obtained by the Reflective Optics System Imaging Spectrometer (ROSIS-03) sensor on 8 July 2002 [14,42], and the raw data represent the hyperspectral image of a Pavia University scene with 103 bands and 1.0 m/pixel spatial resolution. The location of this data is near Pavia University, which is located north of the city Pavia, Italy.…”

Section: Data Set Descriptionmentioning

confidence: 99%

Refining Land-Cover Classification Maps Based on Dual-Adaptive Majority Voting Strategy for Very-High-Resolution Remote Sensing Images

Qing¹,

Yong²,

GuangFei³

et al. 2018

Preprint

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Land-cover classification that uses very-high-resolution (VHR) remote sensing images is a topic of considerable interest. Although many classification methods have been developed, there is still room for improvements in the accuracy and usability of classification systems. In this paper, a novel post-processing approach based on a dual-adaptive majority voting strategy (D-AMVS) is proposed for improving the performance of initial classification maps. D-AMVS defines a strategy for refining each label of a classified map that is obtained by different classification methods from the same original image and fusing the different refined classification maps to generate a final classification result. The proposed D-AMVS contains three main blocks. 1) An adaptive region is generated by extending gradually the region around a central pixel based on two predefined parameters (T1 and T2) in order to utilize the spatial feature of ground targets in a VHR image. 2) For each classified map, the label of the central pixel is refined according to the majority voting rule within the adaptive region. This is defined as adaptive majority voting (AMV). Each initial classified map is refined in this manner pixel by pixel. 3) Finally, the refined classified maps are used to generate a final classification map, and the label of the central pixel in the final classification map is determined by applying AMV again. Each entire classified map is scanned and refined pixel by pixel based on the proposed D-AMVS. The accuracies of the proposed D-AMVS approach are investigated through two remote sensing images with high spatial resolutions of 1.0 and 1.3 m, respectively. Compared with the classical majority voting method and a relatively new post-processing method called general post-classification framework, the proposed D-AMVS can achieve a land-cover classification map with less noise and higher classification accuracies.

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ROSIS (Reflective Optics System Imaging Spectrometer) - A Candidate Instrument For Polar Platform Missions

Cited by 68 publications

References 0 publications

A New Dataset and Deep Residual Spectral Spatial Network for Hyperspectral Image Classification

A New Dataset and Deep Residual Spectral Spatial Network for Hyperspectral Image Classification

Estudio Comparativo de Técnicas de Clasificación de Imágenes Hiperespectrales

Refining Land-Cover Classification Maps Based on Dual-Adaptive Majority Voting Strategy for Very-High-Resolution Remote Sensing Images

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