西电高光谱机器学习大作业

of information and image space geometric spectral information, they are stacked together to form a hyperspectral cube. Hyperspectral cube in two-dimensional space based on the increase of one-dimensional spectral information, thus forming a three-dimensional space coordinates. Imaging spectrometer that is generated for each band hyperspectral image data as a level expression of the overall imaging spectrometer data to the three-dimensional coordinate space, thus constituting a composite sequence composed by band, with multiple levels of data cube. Shown in Figure 1.1, hyperspectral imaging spectrometer technology combine traditional remote sensing imaging technology and new spectroscopic techniques, different substances corresponding to different spectral characteristics. Because in a certain wavelength range (e.g. visible near infrared, visible - shortwave infrared), spectral overlap with adjacent bands, which is a continuous spectrum image, resulting in a high spectral cube of each image pixel may be a continuous extraction the spectral curve, which is the spectral "fingerprint effect". Imaging spectrometer according to an image obtained can be recorded using the full spectra of the various observed feature infoቤተ መጻሕፍቲ ባይዱmation, which makes use of the high spectral data inversion feature of detail as possible, so that qualitative analysis from remote sensing technology to quantitative analysis of changes in the quantitative remote sensing technology and theory to establish the technical basis.
Hyperspectral remote sensing data classification
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Abstract
Currently, hyperspectral remote sensing (HRS) has become a cutting-edge field ofremote sensing (RS) area. Hyperspectral image classification has become one of thehottest research fields of the remote sensing area. The hyperspectral classificationtechnology has very high value both in application and theory research. However,hyperspectral image processing also introduced many problems. The problems are: howto solve the problem of high-dimensional with small amout of samples, how to use theinformation ignored in current research, such as structural information, how to use thevast amounts of unlabeled samples that contain information, how to choose the fittestclassifier. In HRS applications, the requirements of hyperspectral classifiersperformance are increasingly. The corresponding hyperspectral classificationtechnology research should focus on the topic, connecting with the specific applicationof hyperspectral remote sensing.
Introduction
Hyperspectral imaging spectrometer, also known as remote sensing, which uses a lot of very narrow band of electromagnetic waves reflected from the object of interest to obtain the relevant data to achieve a breakthrough in remote sensing technology to improve spectral resolution. Hyperspectral remote sensing and general difference is that the former than the latter has more finely spectral bands, you can form a complete spectral data. Technical staff through the spectral analysis of the data, the feature can be a lot of information, so as to achieve long-range, non-contact detection of objects conducted research. From the perspective of technological innovation, hyperspectral technology combines modern high-tech in a series of important progress, the use of the spectral characteristics of the surface features fine and the intrinsic link between the feature detection, resulting in remote sensing images each pixel corresponding to the spectral curve of a smooth. Hyperspectral remote sensing image processing using the reflection spectrum of feature recognition feature distinguish its type, can be effectively quantified feature attribute recognition and information extraction. Hyperspectral remote sensing spectral resolution refers to the direction of the detector at a wavelength of recording width, also known as band width. Hyperspectral bands quantity, the number of spectral channels up to tens or even hundreds, and often between each spectral channel is continuous. Improve spectral resolution remote sensing direction of development, the narrower band of the more likely to accurately identify a single surface material composition. Imaging Spectrometer imaging in space, while at the same spatial resolution can be recorded into one hundred spectral channel data, thus contains a wealth
figure 1.1Hyperspectral image schematic diagram
Gaussian discriminant analysis
（Proposed method）
The first generative learning algorithm that we'll look at is Gaussian discriminant analysis (GDA). In this model, we'll assume that p(x|y) is distributed according to a multivariate normal distribution. Let’s talk briefly about the properties of multivariate normal distributions before moving on to the GDA model itself. The multivariate normal distribution in n-dimensions, also called the multivariate Gaussian distribution, is parameterized by a mean vectorμ ∈Rn and a covariance matrixΣ ∈RN*N, whereΣ ≥0 is symmetric and positive semi-definite. Also written N(μ ,Σ )its density is given by: p x; μ, Σ = 1 2π