### 机器视觉代写|图像处理作业代写Image Processing代考|APPLICATIONS OF DIGITAL IMAGE PROCESSING TECHNIQUES FOR ICE PARAMETER IDENTIFICATION

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 机器视觉代写|图像处理作业代写Image Processing代考|ICE PARAMETER IDENTIFICATION

Digital images were first used for transferring newspaper pictures between London and New York in the early 1920 s, where the pictures were coded for the submarine cable transmission and reconstructed by a special telegraph printer at the receiving end. The concept of digital image processing became meaningful and many of the digital image processing capabilities were developed in the $1960 \mathrm{~s}$ when both hardware and software of computer technology were developed powerful enough to carry out image processing algorithms. In the 1970 s, digital image processing techniques began to be used in the space program, medical imaging, remote sensing, and astronomy as cheaper and dedicated computer hardware became available. Until now, with the rapid development of computer technology, the use of digital image processing techniques has been growing by leaps and bounds, and has achieved success in many applications such as remote sensing, industrial inspection, medicine, biology, astronomy, law enforcement, defense, etc. [48].

In most cases, human manual interpretation is simply impossible, and the only feasible solution for information extraction from images is through digital image processing by a computer. Digital image processing algorithms, implemented by computers, are important to replace humans in the interpretation of image data. Many image processing algorithms have been developed for the analysis of sea ice statistics and ice properties from remotely sensed sea ice images, and in this section we will give an overview of some of the relevant literature in this field.

## 机器视觉代写|图像处理作业代写Image Processing代考|ICE CONCENTRATION CALCULATION

From Equation 1.1, it is clear that the estimation of ice concentration by using ice imagery data is equivalent to the discrimination of ice pixels from water pixels. Due to the fact that ice is normally brighter than water, a thresholding approach is typically used for extracting ice from water pixels $[54,169,185]$. For instance, Markus and Dokken [103] propose that sea ice pixels can be determined by adapting thresholds between ice and open water based on local intensity distributions, while Johannessen et al. [70] introduces an algorithm of sea ice concentration retrieval from ERS (European Remote Sensing) SAR (Synthetic Aperture Radar) images by using two thresholds to separate open water from thick ice.

Ice concentration derivation is usually associated with ice type classification, since all types of sea ice should be taken into account for calculating ice concen-tration. Hence, the algorithms for classifying ice types, such as unsupervised and supervised classification [169], texture features [89], and neural networks [77], etc., can also be used for calculating ice concentration. The ice concentration is then derived by summing up the concentrations of multiple ice types existing in the ice image.

## 机器视觉代写|图像处理作业代写Image Processing代考|SEA ICE TYPE CLASSIFICATION

Unsupervised and supervised classification algorithms are popular for sea ice type classification $[81,55,42,143,133,112,138,43,142,181]$. In an unsupervised classification approach, pixels are assigned to classes based on their spectral properties, without the user having any prior knowledge of the existence of those classes; while in a supervised classification approach, pixels are grouped based on the knowledge of the user by providing sample classes to train the classifier [71]. Hughes [64] examined the use of an unsupervised $k$-means clustering method for automatic classification of the data from $7 \mathrm{SSM} / \mathrm{I}$ channels, and he demonstrated that it is possible to obtain classifications of the different ice regimes both in the seasonal and perennial ice cover by clustering using emissivities from all channels. Dabboor and Shokr [37] proposed an iteratively supervised classification approach that utilized a complex Wishart distribution-based likelihood ratio (LR) and a spatial context criterion to discriminate sea ice types for polarimetric SAR data.

Image features, particularly texture features that characterize local and statistical properties of regions in an image, have been widely used in the classification of sea ice types $[63,138,31,142,26,27]$. Several research works have been done on gray-level co-occurrence matrices (GLCM) texture analysis [56] for sea ice image classification $[138,101,89]$. Many important parameters need to be defined for GLCM. Soh and Tsatsoulis [142] quantitatively evaluated GLCM texture parameters and representations, and they determined best textural parameters and representations for mapping texture features of SAR sea ice imagery. They also developed three GLCM implementations and evaluated these developed implementations by a supervised Bayesian classifier on sea ice textural contexts. Other texture analysis methods, such as Gabor, and Markov random fields (MRF), can also used in sea ice image classification. Clausi [25] compared the ability of texture features based on GLCM, Gabor, MRF, and the combination of these three methods for classifying SAR sea ice image.

Neural networks have also been applied to classifying sea ice types $[74,14,181]$. For examples, Comiso [33] utilized a back-propagation neural network to improve the classification by using the unsupervised ISODATA cluster analysis results to train the system. Hara et al. [55] developed a neural network that employed the learning vector quantization (LVQ) method to perform the initial clustering and improved the results by an iterative maximum likelihood (ML) method for the classification of sea ice in SAR imagery. Pedersen et al. [119] used a feed-forward back propagation neural network with 3 layers for sea ice type classification based on texture features.
Besides the classification methods mentioned above, Yu and Clausi [180] developed a so-called iterative region growing using semantics (IRGS) algorithm that

combined image segmentation and classification for classifying the operational SAR sea ice imagery. In this IRGS algorithm, the watershed algorithm [167] was first used to segment the image into small homogeneous regions, then the MRF-based labeling and the region merging processes were performed iteratively until the merging cannot be performed further. The IRGS algorithm has been applied to polygons from sea ice maps provided by the Canadian Ice Service (CIS) for classifying sea ice types [112], and further extended for polarimetric SAR image classification by incorporating a polarimetric feature model based on the Wishart distribution and modifying key steps [179].

It should be noted that the works mentioned above mainly classified sea ice into first-year, multi-year, and young ice. Those sea ice types are different from the ice types that we classify in this book, as described in Section 1.2.2.

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## MATLAB代写

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