Description: Spectral Python (SPy) is a python package for reading, viewing, manipulating, and classifying hyperspectral image (HSI) data. SPy includes functions for clustering, dimensionality reduction, supervised classification, and more. Platform: |
Size: 216064 |
Author:john |
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Description: 基于谱间预测的高光谱图像压缩。单向预测与双向预测，以及参考波段的最优选取-The prediction based on spectral hyperspectral image compression. One-way bi-directional prediction and prediction, as well as reference to select the optimal band Platform: |
Size: 3351552 |
Author:梅江涛 |
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Description: 高光谱图像的顶点成分分析，在没有先验知识背景下，提取图像中的端元-Hyperspectral Image vertex component analysis, in the absence of a priori knowledge of background, the extracted image Endmember Platform: |
Size: 1024 |
Author:yudongjun |
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Description: 该代码实现了从光谱库读取高光谱曲线，并且去除其包络。这是高光谱图像处理时一个重要的预处理技术。-The code read from the spectral library of high spectral curve, and the removal of its envelope. This is a hyperspectral image processing, when an important pre-processing technology. Platform: |
Size: 3072 |
Author:jenna |
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Description: 一种基于线性预测和算术编码的高光谱图像压缩方法，希望对您有用。-Based on linear prediction and arithmetic coding of hyperspectral image compression, and I hope useful for you. Platform: |
Size: 20480 |
Author:刘英 |
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Description: 《Hyperspectral Remote Sensing》是一本关于高光谱遥感图像处理与应用的英文经典书籍，对于学习高光谱遥感图像的人员有很大的帮助。-" Hyperspectral Remote Sensing" is a book on hyperspectral remote sensing image processing and application of the English classics, hyperspectral remote sensing images for learning the staff is very helpful. Platform: |
Size: 3196928 |
Author:王启帆 |
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Description: 一个针对高光谱图像进行主成分分析的小程序，但要求高光谱图像的格式是.mat格式。-For hyperspectral image of a principal component analysis of small programs, but requires the format of hyperspectral images. Mat format. Platform: |
Size: 1024 |
Author:王启帆 |
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Description: 高光谱图像特征分析，基于空间和谱间，识别地物的信息，用于提取地物的特点，对于高光谱的学习打下基础-Hyperspectral image characteristics analysis, in the space and spectrum differences and feature extraction, used to identify features information
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Size: 1024 |
Author:刘艳艳 |
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Description: Hyperspectral image classification with limited
number of labeled pixels is a challenging task. In this paper, we
propose a bilayer graph-based learning framework to address
this problem. For graph-based classification, how to establish
the neighboring relationship among the pixels the high
dimensional features is the key toward a successful classification.
Our graph learning algorithm contains two layers. The first-layer
constructs a simple graph, where each vertex denotes one pixel
and the edge weight encodes the similarity between two pixels.
Unsupervised learning is then conducted to estimate the grouping
relations among different pixels. These relations are subsequently
fed into the second layer to form a hypergraph structure, on top
of which, semisupervised transductive learning is conducted to
obtain the final classification results. Our experiments on three
data sets demonstrate the merits of our proposed approach,
which compares favorably with state of the art.-Hyperspectral image classification with limited
number of labeled pixels is a challenging task. In this paper, we
propose a bilayer graph-based learning framework to address
this problem. For graph-based classification, how to establish
the neighboring relationship among the pixels the high
dimensional features is the key toward a successful classification.
Our graph learning algorithm contains two layers. The first-layer
constructs a simple graph, where each vertex denotes one pixel
and the edge weight encodes the similarity between two pixels.
Unsupervised learning is then conducted to estimate the grouping
relations among different pixels. These relations are subsequently
fed into the second layer to form a hypergraph structure, on top
of which, semisupervised transductive learning is conducted to
obtain the final classification results. Our experiments on three
data sets demonstrate the merits of our proposed approach,
which compares favorably with state of the art. Platform: |
Size: 2847744 |
Author:bala |
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Search - hyperspectral image