Results 31 to 40 of about 49,483 (287)
A CNN with noise inclined module and denoise framework for hyperspectral image classification
IET Image Processing, 2023 Deep Neural Networks have been successfully applied in hyperspectral image classification. However, most of prior works adopt general deep architectures while ignore the intrinsic structure of the hyperspectral image, such as the physical noise ...
Zhiqiang Gong +5 more
doaj +1 more source
Wavelet based segmentation of hyperspectral colon tissue imagery [PDF]
, 2003 Segmentation is an early stage for the automated classification of tissue cells between normal and malignant types. We present an algorithm for unsupervised segmentation of images of hyperspectral human colon tissue cells into their constituent parts by ...
Rajpoot, Kashif +1 more
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Deep learning in remote sensing: a review [PDF]
, 2017 Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields ...
Fraundorfer, Friedrich +6 more
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Deep Manifold Embedding for Hyperspectral Image Classification [PDF]
IEEE Transactions on Cybernetics, 2022 Deep learning methods have played a more and more important role in hyperspectral image classification. However, the general deep learning methods mainly take advantage of the information of sample itself or the pairwise information between samples while ignore the intrinsic data structure within the whole data.
Zhiqiang Gong +4 more
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Quality metrics evaluation of hyperspectral images [PDF]
, 2014 In this paper, the quality metrics evaluation on hyperspectral images has been presented using k-means clustering and segmentation. After classification the assessment of similarity between original image and classified image is achieved by measurements ...
Kadambi, G.R. +5 more
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Texture Based Hyperspectral Image Classification [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014 Abstract. This research work presents a supervised classification framework for hyperspectral data that takes into account both spectral and spatial information. Texture analysis is performed to model spatial characteristics that provides additional information, which is used along with rich spectral measurements for better classification of ...
Kumar, B., Dikshit, O.
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Overview of Hyperspectral Image Classification
Journal of Sensors, 2020 With the development of remote sensing technology, the application of hyperspectral images is becoming more and more widespread. The accurate classification of ground features through hyperspectral images is an important research content and has attracted widespread attention. Many methods have achieved good classification results in the classification
Wenjing Lv, Xiaofei Wang
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Advances in Electrical and Computer Engineering, 2017 Hyperspectral pixels which have high spectral resolution are used to predict decomposition of material types on area of obtained image. Due to its multidimensional form, hyperspectral image classification is a challenging task. Hyperspectral images are
AKYUREK, H. A., KOCER, B.
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Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs
Remote Sensing, 2018 Hyperspectral image classification has been acknowledged as the fundamental and challenging task of hyperspectral data processing. The abundance of spectral and spatial information has provided great opportunities to effectively characterize and identify
Feng Gao, Qun Wang, Junyu Dong, Qizhi Xu
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SpectralFormer: Rethinking Hyperspectral Image Classification With Transformers [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2022 Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies. Owing to their excellent locally contextual modeling ability, convolutional neural networks (CNNs) have been proven to be a powerful feature extractor in HS image ...
Hong, Danfeng +6 more
openaire +4 more sources