基于边缘保持滤波的高光谱影像光谱-空间联合分类

针对高光谱遥感影像分类过程中,高维数据引起的"维数灾难"以及空间邻域一致性信息没有得到充分利用的问题,提出一种基于边缘保持滤波（Edge-preserving filtering,EPF）的高光谱影像光谱-空间联合分类算法.该算法首先进行波段子集划分和主成分提取,构造新的低维特征集,在保存影像结构信息的前提下降低数据维度;其次利用支持向量机（Support vector machine,SVM）获得低维特征集的初始分类概率图;然后利用原始影像主成分对初始分类概率图进行边缘保持滤波,融合光谱信息和空间信息;最后根据滤波后分类概率图对应像素点值的大小确定每个像素的类别.在Indian Pines和Pavia University两组高光谱数据上进行仿真实验,相同实验条件下,本文算法都获得最高分类精度和最少的时间消耗.仿真结果表明本文算法在高光谱遥感影像分类任务中具有明显的优势.     

Effective feature extraction through segmentation-based folded-PCA for hyperspectral image classification

The remote sensing hyperspectral images (HSIs) usually comprise many important information of the land covers capturing through a set of hundreds of narrow and contiguous spectral wavelength bands. Appropriate classification performance can only offer the required knowledge from these immense bands of HSI since the classification result is not reasonable using all the original features (bands) of the HSI. Although it is not easy to calculate the intrinsic features from the bands, band (dimensionality) reduction techniques through feature extraction and feature selection are usually applied to increase the classification result and to fix the curse of dimensionality problem. Though the Principal Component Analysis (PCA) has been commonly adopted for the feature reduction of HSI, it can often fail to extract the local useful characteristics of the HSI for effective classification as it considers the global statistics of the HSI. Consequently, Segmented-PCA (SPCA), Spectrally-Segmented-PCA (SSPCA), Folded-PCA (FPCA) and Superpixelwise PCA (SuperPCA) have been introduced for better feature extraction of HSI in diverse ways. In this paper, feature extraction through SPCA & FPCA and SSPCA & FPCA, termed as Segmented-FPCA (SFPCA) and Spectrally-Segmented-FPCA (SSFPCA) respectively, has further been improved through applying FPCA on the highly correlated or spectrally separated bands’ segments of the HSI rather than not applying the FPCA on the entire dataset directly. The proposed methods are compared and analysed for a real mixed agricultural and an urban HSI classification using per-pixel SVM classifier. The experimental result shows that the classification performance using SSFPCA and SFPCA outperforms that of using conventional PCA, SPCA, SSPCA, FPCA, SuperPCA and using the entire original dataset without employing any feature reduction. Moreover, the proposed feature extraction methods provide the least memory and computation cost complexity.     

Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images

Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes. Much spatial information and spectral signatures of hyperspectral images (HSIs) present greater potential for detecting and classifying fine crops. The accurate classification of crop kinds utilizing hyperspectral remote sensing imaging (RSI) has become an indispensable application in the agricultural domain. It is significant for the prediction and growth monitoring of crop yields. Amongst the deep learning (DL) techniques, Convolution Neural Network (CNN) was the best method for classifying HSI for their incredible local contextual modeling ability, enabling spectral and spatial feature extraction. This article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification (HMAODL-CTC) algorithm on HSI. The proposed HMAODL-CTC model mainly intends to categorize different types of crops on HSI. To accomplish this, the presented HMAODL-CTC model initially carries out image preprocessing to improve image quality. In addition, the presented HMAODL-CTC model develops dilated convolutional neural network (CNN) for feature extraction. For hyperparameter tuning of the dilated CNN model, the HMAO algorithm is utilized. Eventually, the presented HMAODL-CTC model uses an extreme learning machine (ELM) model for crop type classification. A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC algorithm. Extensive comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.     

结合纯像元提取和ICA的高光谱降维方法*

摘要：为了实现高光谱降维并保留重要的光谱特征,通过独立分量分析（Independent Component Analysis, ICA）混合模型和高光谱线性模型的对比分析,提出了结合纯像元提取和ICA的高光谱数据降维方法。该方法通过估计虚拟维数(Virtual Dimensionality, VD)确定特征个数,采用自动目标生成过程（Automatic Target Generation Process, ATGP）从原始数据中提取纯像元向量,作为ICA算法的初始化向量,以负熵为目标函数产生独立分量,并通过高阶统计量筛选实现高光谱数据的降维。分类实验结果表明,该方法不仅解决了传统ICA的随机排序问题,而且与经典降维算法主分量分析（Principal Components Analysis, PCA）相比,分类精度提高了6.83%,在大大降低高光谱数据量的情况下很好的保留了高光谱数据的特征,有利于数据的后续分析和应用。     

Study on Glacier Classification Methods of Airborne Hyperspectral Data——A Case Study of the Zhongxi-1 Glacier

The glacier is an important natural and great potential of the fresh water resources,and plays a vital role in the regional ecological environment balance and stability.This study acquired the airborne hyperspectral data over Zhongxi-1 Glacier in August,2011.Firstly,the data preprocessing,including radiation calibration,atmospheric correction and geometric correction was performed on the hyperspectral data;secondly,using principal component analysis (PCA)and minimum noise transformation (MNF) for data dimensionality reduced respectively;thirdly,six classification methods,i.e.maximum likelihood method,minimum distance,Mahalanobis distance method,spectral angle method binary encoding,and spectral information divergence,were applied in the two datasets,and also the comparison results of the different classification methods were conducted to determine the optimal method of data dimensionality reduction and the optimal classification method;finally,the hyperspectral data for glacierclassification was compared with the HJ satellite multispectral data.The results show that: the classification accuracy of the PCA transform data from hyperspectral data is higher than that of MNF transform data;for the PCA transformed dataset of hyperspectraldata,the Mahalanobis distance method,maximum likelihood method,minimum distance method produced better classification results with the comparison to others,while for the MNF transformed dataset from hyperspectral data,the spectral angle method and spectral information divergence method is better than others.     

Spatial-spectral neighbour graph for dimensionality reduction of hyperspectral image classification

Recently, graph embedding-based methods have drawn increasing attention for dimensionality reduction (DR) of hyperspectral image (HSI) classification. Graph construction is a critical step for those DR methods. Pairwise similarity graph is generally employed to reflect the geometric structure in the original data. However, it ignores the similarity of neighbouring pixels. In order to further improve the classification performance, both spectral and spatial-contextual information should be taken into account in HSI classification. In this paper, a novel spatial-spectral neighbour graph (SSNG) is proposed for DR of HSI classification, which consists of the following four steps. First, a superpixel-based segmentation algorithm is adopted to divide HSI into many superpixels. Second, a novel distance metric is utilized to reflect the similarity of two spectral pixels in each superpixel. In the third step, a spatial-spectral neighbour graph is constructed according to the above distance metric. At last, support vector machine with a composite kernel (SVM-CK) is adopted to classify the dimensionality-reduced HSI. Experimental results on three real hyperspectral datasets demonstrate that our method can achieve higher classification accuracy with relatively less consumed time than other graph embedding-based methods.     

Sequential masking classification of multi‐temporal Landsat7 ETM+ images for field‐based crop mapping in Karacabey,Turkey

Three Landsat7 ETM+ images acquired in May, July and August during the 2000 crop growing season were used for field‐based mapping of summer crops in Karacabey, Turkey. First, the classification of each image date was performed on a standard per pixel basis. The results of per pixel classification were integrated with digital agricultural field boundaries and a crop type was determined for each field based on the modal class calculated within the field. The classification accuracy was computed by comparing the reference data, field‐by‐field, to each classified image. The individual crop accuracies were examined on each classified data and those crops whose accuracy exceeds a preset threshold level were determined. A sequential masking classification procedure was then performed using the three image dates, excluding after each classification the class properly classified. The final classified data were analysed on a field basis to assign each field a class label. An immediate update of the database was provided by directly entering the results of the analysis into the database. The sequential masking procedure for field‐based crop mapping improved the overall accuracies of the classifications of the July and August images alone by more than 10%.     

Unsupervised spectral sub-feature learning for hyperspectral image classification

Spectral pixel classification is one of the principal techniques used in hyperspectral image (HSI) analysis. In this article, we propose an unsupervised feature learning method for classification of hyperspectral images. The proposed method learns a dictionary of sub-feature basis representations from the spectral domain, which allows effective use of the correlated spectral data. The learned dictionary is then used in encoding convolutional samples from the hyperspectral input pixels to an expanded but sparse feature space. Expanded hyperspectral feature representations enable linear separation between object classes present in an image. To evaluate the proposed method, we performed experiments on several commonly used HSI data sets acquired at different locations and by different sensors. Our experimental results show that the proposed method outperforms other pixel-wise classification methods that make use of unsupervised feature extraction approaches. Additionally, even though our approach does not use any prior knowledge, or labelled training data to learn features, it yields either advantageous, or comparable, results in terms of classification accuracy with respect to recent semi-supervised methods.     

基于多视图边界判别投影的高光谱图像分类

高光谱图像分类是遥感领域研究的热点问题,其关键在于利用高光谱图谱合一的 优势,同时融合高光谱图像中各个像元位置的光谱信息和空间信息,提高光谱图像分类精度。 针对高光谱图像特征维数高和冗余信息多等问题,采用多视图子空间学习方法进行特征降维, 提出了图正则化的多视图边界判别投影算法。将每个像元处的光谱特征看作一个视图,该像元 处的空间特征看作另一个视图,通过同时优化每个视图上的投影方向来寻找最优判别公共子空 间。公开测试数据集上的分类实验表明,多视图学习在高光谱图像空谱融合分类方面具有显著 的优越性,在多视图降维算法中,该算法具有最高的分类准确性。     

基于空谱融合网络的高光谱图像分类方法

针对高光谱图像分类中提取的空-谱特征表达能力弱及维数较高的问题,提出一种基于空-谱融合网络（SSF-Net）的高光谱图像分类方法。首先,利用双通道卷积神经网络（Two-CNN）同时提取高光谱图像的光谱和空间特征;其次,使用多模态压缩双线性池化（MCB）将所提取的多模态特征向量的外积投射到低维空间,以此产生空-谱联合特征。该特征融合网络,既可以分析光谱特征和空间特征向量中元素之间的复杂关系,同时也避免对光谱和空间向量直接进行外积计算,造成维数过高、计算困难的问题。最终实验表明,与现有基于神经网络的分类方法相比,所提出的高光谱图像分类算法能够获得更高的像元分类精度,表明该网络所提取的空-谱联合向量对高光谱图像具有更强的特征表达能力。     

基于近邻协同的高光谱图像谱-空联合分类

遥感高光谱成像能够获得丰富的地物光谱信息, 为高精度的地物分析提供了可能. 针对高光谱图像分类中通常面临的数据维数高、标记样本少、计算量大等问题, 提出了一种简单有效的谱--空联合分类方法. 利用高光谱图像丰富的光谱信息和地物分布的空间平滑特性, 该算法首先对光谱数据进行特征提取和空间滤波, 然后利用本文提出的基于近邻协同的支持向量机(Neighborhood collaborative support vector machine, NC-SVM)进行分类. 近邻协同进一步利用地物分布的空间平滑特性, 通过联合空间近邻的判决信息进行中心像素的类别判定, 有效减小了只有少量训练样本下的错分概率. 实验表明, 相比已有的相关方法, 该算法在不明显增加计算量的情况下可获得更高的分类正确率, 能够实现少量训练样本下高光谱图像的快速高精度分类.     

Integrating visible,near-infrared and short-wave infrared hyperspectral and multispectral thermal imagery for geological mapping at Cuprite,Nevada: a rule-based system

Previous research has shown that integrating hyperspectral visible and near-infrared (VNIR) / short-wave infrared (SWIR) with multispectral thermal infrared (TIR) data can lead to improved mineral and rock identification. However, inconsistent results were found regarding the relative accuracies of different classification methods for dealing with the integrated data set. In this study, a rule-based system was developed for integration of VNIR/SWIR hyperspectral data with TIR multispectral data and evaluated using a case study of Cuprite, Nevada. Previous geological mapping, supplemented by field work and sample spectral measurements, was used to develop a generalized knowledge base for analysis of both spectral reflectance and spectral emissivity. The characteristic absorption features, albedo and the location of the spectral emissivity minimum were used to construct the decision rules. A continuum removal algorithm was used to identify absorption features from VNIR/SWIR hyperspectral data only; spectral angle mapper (SAM) and spectral feature fitting (SFF) algorithms were used to estimate the most likely rock type. The rule-based system was found to achieve a notably higher performance than the SAM, SFF, minimum distance and maximum likelihood classification methods on their own.     

A probabilistic SVM approach for hyperspectral image classification using spectral and texture features

New hyperspectral sensors can collect a large number of spectral bands, which provide a capability to distinguish various objects and materials on the earth. However, the accurate classification of these images is still a big challenge. Previous studies demonstrate the effectiveness of combination of spectral data and spatial information for better classification of hyperspectral images. In this article, this approach is followed to propose a novel three-step spectral–spatial method for classification of hyperspectral images. In the first step, Gabor filters are applied for texture feature extraction. In the second step, spectral and texture features are separately classified by a probabilistic Support Vector Machine (SVM) pixel-wise classifier to estimate per-pixel probability. Therefore, two probabilities are obtained for each pixel of the image. In the third step, the total probability is calculated by a linear combination of the previous probabilities on which a control parameter determines the efficacy of each one. As a result, one pixel is assigned to one class which has the highest total probability. This method is performed in multivariate analysis framework (MAF) on which one pixel is represented by a d-dimensional vector, d is the number of spectral or texture features, and in functional data analysis (FDA) on which one pixel is considered as a continuous function. The proposed method is evaluated with different training samples on two hyperspectral data. The combination parameter is experimentally obtained for each hyperspectral data set as well as for each training samples. This parameter adjusts the efficacy of the spectral versus texture information in various areas such as forest, agricultural or urban area to get the best classification accuracy. Experimental results show high performance of the proposed method for hyperspectral image classification. In addition, these results confirm that the proposed method achieves better results in FDA than in MAF. Comparison with some state-of-the-art spectral–spatial classification methods demonstrates that the proposed method can significantly improve classification accuracies.     

Hidden Markov Models for crop recognition in remote sensing image sequences

This work proposes a Hidden Markov Model (HMM) based technique to classify agricultural crops. The method uses HMM to relate the varying spectral response along the crop cycle with plant phenology, for different crop classes, and recognizes different agricultural crops by analyzing their spectral profiles over a sequence of images. The method assigns each image segment to the crop class whose corresponding HMM delivers the highest probability of emitting the observed sequence of spectral values. Experimental analysis was conducted upon a set of 12 co-registered and radiometrically corrected LANDSAT images of region in southeast Brazil, of approximately 124.100 ha, acquired between 2002 and 2004. Reference data was provided by visual classification, validated through extensive field work. The HMM-based method achieved 93% average class accuracy in the identification of the correct crop, being, respectively, 10% and 26% superior to multi-date and single-date alternative approaches applied to the same data set.     

3D密集全卷积的高光谱地物分类算法研究

针对高光谱遥感图像训练样本较少、光谱维度较高、空间特征与频谱特征存在差异性而导致高光谱地物分类的特征提取不合理、分类精度不稳定和训练时间长等问题,提出了基于3D密集全卷积（3D-DSFCN）的高光谱图像（HSI）分类算法。算法通过密集模块中的3D卷积核分别提取光谱特征和空间特征,采用特征映射模块替换传统网络中的池化层和全连接层,最后通过softmax分类器进行分类。实验结果表明,基于3D-DSFCN的HSI分类方法提高了地物分类的准确率、增强了低频标签的分类稳定性。     

Parallel processing of remotely sensed hyperspectral imagery: full‐pixel versus mixed‐pixel classification

The rapid development of space and computer technologies allows for the possibility to store huge amounts of remotely sensed image data, collected using airborne and satellite instruments. In particular, NASA is continuously gathering high‐dimensional image data with Earth observing hyperspectral sensors such as the Jet Propulsion Laboratory's airborne visible–infrared imaging spectrometer (AVIRIS), which measures reflected radiation in hundreds of narrow spectral bands at different wavelength channels for the same area on the surface of the Earth. The development of fast techniques for transforming massive amounts of hyperspectral data into scientific understanding is critical for space‐based Earth science and planetary exploration. Despite the growing interest in hyperspectral imaging research, only a few efforts have been devoted to the design of parallel implementations in the literature, and detailed comparisons of standardized parallel hyperspectral algorithms are currently unavailable. This paper compares several existing and new parallel processing techniques for pure and mixed‐pixel classification in hyperspectral imagery. The distinction of pure versus mixed‐pixel analysis is linked to the considered application domain, and results from the very rich spectral information available from hyperspectral instruments. In some cases, such information allows image analysts to overcome the constraints imposed by limited spatial resolution. In most cases, however, the spectral bands collected by hyperspectral instruments have high statistical correlation, and efficient parallel techniques are required to reduce the dimensionality of the data while retaining the spectral information that allows for the separation of the classes. In order to address this issue, this paper also develops a new parallel feature extraction algorithm that integrates the spatial and spectral information. The proposed technique is evaluated (from the viewpoint of both classification accuracy and parallel performance) and compared with other parallel techniques for dimensionality reduction and classification in the context of three representative application case studies: urban characterization, land‐cover classification in agriculture, and mapping of geological features, using AVIRIS data sets with detailed ground‐truth. Parallel performance is assessed using Thunderhead, a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center. The detailed cross‐validation of parallel algorithms conducted in this work may specifically help image analysts in selection of parallel algorithms for specific applications. Copyright © 2008 John Wiley & Sons, Ltd.     

Hyperspectral image classification based on bilateral filter with linear spatial correlation information

Support Vector Machine (SVM) with the margin theory is widely used for the hyperspectral classification. However, the margin model is a single interval and does not represent the complete distribution of hyperspectral image data sets. In addition, the spatial texture information obtained by filtering in recent years has become a hot research topic for improving classification of hyperspectral images, but the spatial correlation information is often lost in the spatial texture information extraction. To solve this problem, this paper proposed an algorithm with large margin distribution machine (LDM) that combined the spatial information obtained by the bilateral filter and linear spatial correlation information (BFLSCI-LDM). First, spatial features were extracted by bilateral filter from hyperspectral image whose dimensionality was reduced by principal component analysis. Next, the linear spatial correlation information was constructed for hyperspectral images. Finally, the spatial information and original spectral information were combined for LDM. The experimental results of actual hyperspectral images indicated that the proposed BFLSCI-LDM method was superior to other classification methods, including the original SVM with the raw spectral features, the dimensionality reduction features, and spatial-spectral information, the method of edge-preserving filter and recursive filter, and the LDM-based method.     

基于自动子空间划分的高光谱数据特征提取

针对遥感高光谱图像数据量大、维数高的特点,提出了一种自动子空间划分方法用于高光谱图像数据量减小处理。该方法主要包括3个处理步骤:数据空间划分,子空间主成分分析和基于类别可分性准则的特征选择。该方法充分利用了高光谱图像各波段数据之间的局部相关性,将整个数据划分为若干个具有较强相关性的独立子空间,然后在子空间内利用主成分分析进行特征提取,根据各类地物间的类别可分性选择有效特征,最后利用地物分类来验证该方法的有效性。实验结果表明,该方法能够有效地实现高光谱图像数据维数减小和特征提取,同现有的自适应子空间分解方法和分段主成分变换方法相比,该方法所提取的特征用于分类时能获得较好的分类精度。利用该方法进行处理,当高光谱数据维数降低了90%时,9类地物分类实验的总体分类精度可以达到80.2%。     

Gray Wolf Optimizer for hyperspectral band selection

In this paper, we propose a new optimization-based framework to reduce the dimensionality of hyperspectral images. One of the most problems in hyperspectral image classification is the Hughes phenomenon caused by the irrelevant spectral bands and the high correlation between the adjacent bands. The problematic is how to find the relevant bands to classify the pixels of hyperspectral image without reducing the classification accuracy rate. We propose to reformulate the problem of band selection as a combinatorial problem by modeling an objective function based on class separability measures and the accuracy rate. We use the Gray Wolf Optimizer, which is a new meta-heuristic algorithm more efficient than Practical Swarm Optimization, Gravitational Search Algorithm, Differential Evolution, Evolutionary Programming and Evolution Strategy. The experimentations are performed on three widely used benchmark hyperspectral datasets. Comparisons with the state-of-the-art approaches are also conducted. The analysis of the results proves that the proposed approach can effectively investigate the spectral band selection problem and provides a high classification accuracy rate by using a few samples for training.     

基于双重L2稀疏编码的高光谱图像分类

目的 为了有效提高高光谱图像分类的精度,提出了双重L2稀疏编码的高光谱图像分类方法。方法 首先对高光谱图像进行预处理,充分结合图像的空间信息和光谱信息,利用像元的空间连续性,用L2稀疏编码重建图像中每个像元。针对重建的图像数据,依据L2稀疏编码的最小误差和编码系数实现分类。结果 在公开的数据库AVIRIS高光谱图像上进行验证,分类精度为99.44%,与支持向量机（SVM）、K最近邻（KNN）和L1稀疏编码方法比较,有效地提高了分类的准确性。结论 实验结果表明,提出的方法应用于高光谱图像分类具有较好的分类效果。