GIS支持下遥感图象中采矿塌陷地提取方法研究

采矿塌陷地动态监测是工矿区资源管理与环境保护的重要方面 ,遥感技术可在其中发挥重要作用 ,从遥感图象中提取采矿塌陷地是遥感应用于矿山资源环境监测的重要研究课题 .传统的提取方法主要基于光谱特征 ,精度与效率都难以满足应用要求 ,为了以较高的精度 ,从遥感图象中提取塌陷地 ,必须建立新的方法与模型 .将遥感技术与 GIS相结合进行专题信息提取是有效的途径之一 .本文根据研究区的特点 ,以具体应用为指导 ,遥感技术与 GIS相结合 ,提出了 GIS支持下的分层分类、基于 GIS变化区域识别的分类、基于 GIS和领域知识对遥感分类图象进行后处理、GIS支持下采矿塌陷地的直接提取等方法与模型 ,充分应用光谱特征、地学特征与信息、领域和专家知识及其他统计数据辅助进行遥感图象处理与专题信息提取 .这些方法在精度、效率等方面均较传统方法有较大提高 ,最大提取精度可达到 89% ,能够有效地对工矿区土地塌陷态势进行动态监测     

3D卷积自编码器高光谱图像分类模型

目的 高光谱图像分类是遥感领域的基础问题，高光谱图像同时包含丰富的光谱信息和空间信息，传统模型难以充分利用两种信息之间的关联性，而以卷积神经网络为主的有监督深度学习模型需要大量标注数据，但标注数据难度大且成本高。针对现有模型的不足，本文提出了一种无监督范式下的高光谱图像空谱融合方法，建立了3D卷积自编码器（3D convolutional auto-encoder，3D-CAE）高光谱图像分类模型。方法 3D卷积自编码器由编码器、解码器和分类器构成。将高光谱数据预处理后，输入到编码器中进行无监督特征提取，得到一组特征图。编码器的网络结构为3个卷积块构成的3D卷积神经网络，卷积块中加入批归一化技术防止过拟合。解码器为逆向的编码器，将提取到的特征图重构为原始数据，用均方误差函数作为损失函数判断重构误差并使用Adam算法进行参数优化。分类器由3层全连接层组成，用于判别编码器提取到的特征。以3D-CNN （three dimensional convolutional neural network）为自编码器的主干网络可以充分利用高光谱图像的空间信息和光谱信息，做到空谱融合。以端到端的方式对模型进行训练可以省去复杂的特征工程和数据预处理，模型的鲁棒性和稳定性更强。结果 在Indian Pines、Salinas、Pavia University和Botswana等4个数据集上与7种传统单特征方法及深度学习方法进行了比较，本文方法均取得最优结果，总体分类精度分别为0.948 7、0.986 6、0.986 2和0.964 9。对比实验结果表明了空谱融合和无监督学习对于高光谱遥感图像分类的有效性。结论 本文模型充分利用了高光谱图像的光谱特征和空间特征，可以做到无监督特征提取，无需大量标注数据的同时分类精度高，是一种有效的高光谱图像分类方法。     

基于光谱聚类的高分影像复杂地块特征提取方法研究

高空间分辨率遥感技术为大范围判识农用地利用类型提供了丰富的数据源.农用地类型多样性和复杂性给高效应用高分影像识别农用地类型带来很大挑战.地块矢量的引入可以帮助更好综合利用多元影像特征,进而提高农用地类型判识精度.但是,传统地块特征提取方法将地块视为一个整体,通过对地块内部像元特征平均实现地块特征表达,不能很好适用于地块...     

结合非监督分类和几何—纹理—光谱特征的高分影像道路提取

目的 目前针对复杂场景高分辨率遥感影像道路提取多采用监督分类方法,但需要人工选择样本,自动化程度低且具有不稳定性。基于像元级的方法,提取完整度低且易产生椒盐噪声;面向对象的方法易产生粘连问题。为了提高道路提取的完整度、准确度和自动化程度,提出一种基于非监督分类和几何—纹理—光谱特征的道路提取方法。方法 首先考虑光谱特征利用非监督分类进行初步分割,结合基于纹理特征分类的结果得到初始道路区域。然后根据道路特征建立一套完整的非道路区域滤除体系：边缘滤波断开道路和非道路的连接、纹理滤波滤除大面积非道路区域、形状滤波去除剩余小面积非道路区域。最后利用张量投票算法得到连贯、平滑的道路中心线。结果 选择复杂场景下的高分辨率IKONOS影像和QuickBird影像进行实验,与国内外基于像素和面向对象的两种有代表性的道路提取方法进行对比,采用完整率、正确率、检测质量3个评价指标进行定量评价。实验结果表明该方法相比于其他算法在完整率、正确率和检测质量上平均提高26.61%、5.57%和26.77%。定性分析结果表明,本文方法可以有效改善椒盐噪声和粘连现象。此外本文方法的自动化程度更高。结论 提出了一种基于非监督分类和几何—纹理—光谱特征的高分辨遥感影像道路提取方法,非监督相对于监督分类的方法有更高的自动化程度,复杂场景下的道路提取融合几何—纹理—光谱特征有效避免了基于像元级道路提取易产生的椒盐噪声现象和面向对象道路提取易产生的粘连现象。该方法适用于高分辨率遥感影像城市道路提取,能够得到较高的完整度、准确度以及自动化程度。非监督分类和多特征结合的道路提取方法有广阔的应用前景。     

基于空谱特征的核极端学习机高光谱遥感图像分类算法

近年来,局部二值模式(Local Binary Patterns,LBP)由于其在空间特征提取方面具有显著的优势被应用于高光谱遥感图像分类中,该算法在空间特征提取上虽减少类内方差,却忽视了用于区分不同地物类别的光谱特征。为避免在图像分类过程中提取单一特征导致特征提取不充分、分类效果不理想的问题,通过将空间特征和光谱特征进行矢量堆叠得到新的空谱特征向量。再将新的空谱特征向量引入到核极端学习机中,提出一种基于空谱特征的核极端学习机高光谱遥感图像分类算法(Space Spectrum feature Kernel Extreme Learning Machine,SS-KELM)。为验证所提算法的有效性,将使用两个高光谱图像数据集进行实验。实验结果表明所提SS-KELM算法的分类性能优于目前较为常见的传统分类算法。     

Urban land cover multi‐level region‐based classification of VHR data by selecting relevant features

The limited spatial resolution of satellite images used to be a problem for the adequate definition of the urban environment. This problem was expected to be solved with the availability of very high spatial resolution satellite images (IKONOS, QuickBird, OrbView‐3). However, these space‐borne sensors are limited to four multi‐spectral bands and may have specific limitations as far as detailed urban area mapping is concerned. It is therefore essential to combine spectral information with other information, such as the features used in visual interpretation (e.g. the degree and kind of texture and the shape) transposed to digital analysis. In this study, a feature selection method is used to show which features are useful for particular land‐cover classes. These features are used to improve the land‐cover classification of very high spatial resolution satellite images of urban areas. The useful features are compared with a visual feature selection. The features are calculated after segmentation into regions that become analysis units and ease the feature calculation.     

Object-based urban detailed land cover classification with high spatial resolution IKONOS imagery

Improvement in remote sensing techniques in spatial/spectral resolution strengthens their applicability for urban environmental study. Unfortunately, high spatial resolution imagery also increases internal variability in land cover units and can cause a ‘salt-and-pepper’ effect, resulting in decreased accuracy using pixel-based classification results. Region-based classification techniques, using an image object (IO) rather than a pixel as a classification unit, appear to hold promise as a method for overcoming this problem. Using IKONOS high spatial resolution imagery, we examined whether the IO technique could significantly improve classification accuracy compared to the pixel-based method when applied to urban land cover mapping in Tampa Bay, FL, USA. We further compared the performance of an artificial neural network (ANN) and a minimum distance classifier (MDC) in urban detailed land cover classification and evaluated whether the classification accuracy was affected by the number of extracted IO features. Our analysis methods included IKONOS image data calibration, data fusion with the pansharpening (PS) process, Hue–Intensity–Saturation (HIS) transferred indices and textural feature extraction, and feature selection using a stepwise discriminant analysis (SDA). The classification results were evaluated with visually interpreted data from high-resolution (0.3 m) digital aerial photographs. Our results indicate a statistically significant difference in classification accuracy between pixel- and object-based techniques; ANN outperforms MDC as an object-based classifier; and the use of more features (27 vs. 9 features) increases the IO classification accuracy, although the increase is statistically significant for the MDC but not for the ANN.     

A study of cloud classification with neural networks using spectraland textural features

The problem of cloud data classification from satellite imagery using neural networks is considered. Several image transformations such as singular value decomposition (SVD) and wavelet packet (WP) were used to extract the salient spectral and textural features attributed to satellite cloud data in both visible and infrared (IR) channels. In addition, the well-known gray-level cooccurrence matrix (GLCM) method and spectral features were examined for the sake of comparison. Two different neural-network paradigms namely probability neural network (PNN) and unsupervised Kohonen self-organized feature map (SOM) were examined and their performance were also benchmarked on the geostationary operational environmental satellite (GOES) 8 data. Additionally, a postprocessing scheme was developed which utilizes the contextual information in the satellite images to improve the final classification accuracy. Overall, the performance of the PNN when used in conjunction with these feature extraction and postprocessing schemes showed the potential of this neural-network-based cloud classification system.     

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.     

Improving forest type classification using the vegetation local difference index

A spatial feature extraction method was applied to increase the accuracy of land-cover classification of forest type information extraction. Traditional spatial feature extraction applications use high-resolution images. However, improving the classification accuracy is difficult when using medium-resolution images, such as a 30 m resolution Enhanced Thematic Mapper Plus (ETM+) image. In this study, we demonstrated a novel method that used the vegetation local difference index (VLDI) derived from the normalized difference vegetation index (NDVI), which were calculated based on the topographically corrected ETM+ image, to delineate spatial features. A simple maximum likelihood classifier and two different ways to use spatial information were introduced in this study as the frameworks to incorporate both spectral and spatial information for analysis. The results of the experiments, where Landsat ETM+ and digital elevation model (DEM) images, together with ground truth data acquired in the study area were used, show that combining the spatial information extracted from medium-resolution images and spectral information improved both classification accuracy and visual qualities. Moreover, the use of spatial information extracted through the proposed method greatly improved the classification performance of particular forest types, such as sparse woodlands.     

Scene classification based on a hierarchical convolutional sparse auto-encoder for high spatial resolution imagery

Efficiently representing and recognizing the semantic classes of the subregions of large-scale high spatial resolution (HSR) remote-sensing images are challenging and critical problems. Most of the existing scene classification methods concentrate on the feature coding approach with handcrafted low-level features or the low-level unsupervised feature learning approaches, which essentially prevent them from better recognizing the semantic categories of the scene due to their limited mid-level feature representation ability. In this article, to overcome the inadequate mid-level representation, a patch-based spatial-spectral hierarchical convolutional sparse auto-encoder (HCSAE) algorithm, based on deep learning, is proposed for HSR remote-sensing imagery scene classification. The HCSAE framework uses an unsupervised hierarchical network based on a sparse auto-encoder (SAE) model. In contrast to the single-level SAE, the HCSAE framework utilizes the significant features from the single-level algorithm in a feedforward and full connection approach to the maximum extent, which adequately represents the scene semantics in the high level of the HCSAE. To ensure robust feature learning and extraction during the SAE feature extraction procedure, a ‘dropout’ strategy is also introduced. The experimental results using the UC Merced data set with 21 classes and a Google Earth data set with 12 classes demonstrate that the proposed HCSAE framework can provide better accuracy than the traditional scene classification methods and the single-level convolutional sparse auto-encoder (CSAE) algorithm.     

Spatial–spectral ant colony optimization for hyperspectral image classification

Hyperspectral satellite images contain a lot of information in terms of spectral behaviour of objects and this information can be extracted by several mechanisms including image classification. Traditional spectral information-based methods of hyperspectral image classification are generally followed by spatial information-driven post-processing techniques such as relaxation labelling and Markov Random Field. Spectral or spatial information alone may lead to different results depending upon scene captured. An algorithm which can incorporate influence of both spectral and spatial features is needed to address this problem. In this article, an ant colony optimisation-based hyperspectral image classification technique is proposed. This method exploits both spatial and spectral features. Five standard hyperspectral data sets have been used to validate the proposed method and comparisons with other approaches have been carried out. It was observed that the proposed method yielded a significant improvement in classification accuracy. For the instance, nearly 10% increase in accuracy was observed when compared to Support Vector Machine for Indian pines, Botswana, and Salinas images.     

双卷积池化结构的3D-CNN高光谱遥感影像分类方法

目的 高光谱遥感影像数据包含丰富的空间和光谱信息,但由于信号的高维特性、信息冗余、多种不确定性和地表覆盖的同物异谱及同谱异物现象,导致高光谱数据结构呈高度非线性。3D-CNN（3D convolutional neural network）能够利用高光谱遥感影像数据立方体的特性,实现光谱和空间信息融合,提取影像分类中重要的有判别力的特征。为此,提出了基于双卷积池化结构的3D-CNN高光谱遥感影像分类方法。方法 双卷积池化结构包括两个卷积层、两个BN（batch normalization）层和一个池化层,既考虑到高光谱遥感影像标签数据缺乏的问题,也考虑到高光谱影像高维特性和模型深度之间的平衡问题,模型充分利用空谱联合提供的语义信息,有利于提取小样本和高维特性的高光谱影像特征。基于双卷积池化结构的3D-CNN网络将没有经过特征处理的3D遥感影像作为输入数据,产生的深度学习分类器模型以端到端的方式训练,不需要做复杂的预处理,此外模型使用了BN和Dropout等正则化策略以避免过拟合现象。结果 实验对比了SVM（support vector machine）、SAE（stack autoencoder）以及目前主流的CNN方法,该模型在Indian Pines和Pavia University数据集上最高分别取得了99.65%和99.82%的总体分类精度,有效提高了高光谱遥感影像地物分类精度。结论 讨论了双卷积池化结构的数目、正则化策略、高光谱首层卷积的光谱采样步长、卷积核大小、相邻像素块大小和学习率等6个因素对实验结果的影响,本文提出的双卷积池化结构可以根据数据集特点进行组合复用,与其他深度学习模型相比,需要更少的参数,计算效率更高。     

Hyperspectral Multiple Features Optimization Using Improved Firefly Algorithm

The utilization of hyperspectral remote sensing image is mainly based on the spectral information,and the spatial information is always be ignored.To solve this problem,a novel hyperspectral multiple features optimization approach based on improved firefly algorithm is presented.Firstly,four spatial features,the local statistical features,gray level co-occurrence matrix features,Gabor filtering features and morphological features of hyperspectral remote sensing image are extracted,and some spectral bands are selected and then combined with these spatial features,and the feature set is constructed.Then,the firefly algorithm is used to optimize the extracted features.In view of the slow convergence speed of firefly algorithm,we use the random inertia weight from particle swarm optimization algorithm to modifiy the location update formula of firefly algorithm,and JM(Jeffreys-Matusita)distance and Fisher Ratio are used as the objective function.Two urban hyperspectral datasets are used for performance evaluation,and the classification results derived from spectral information and spectral-spatial information are compared.The experiments show that random inertia weight can improve the speed of FA-based feature selection algorithm,the performance with multiple features is better than that of spectral information for urban land cover classification,The statistical results of the two sets of experimental data indicate that the selected number of morphological features are the most in the four spatial features.The local statistical features and morphological features are more helpful to the classification of hyperspectral remote sensing images than GLCM and Gabor features.     

Feature extraction method based on spectral dimensional edge preservation filtering for hyperspectral image classification

ABSTRACT

A large amount of spectral and spatial information contained in hyperspectral imagery has provided a great opportunity to effectively characterize and identify the surface materials of interest. Feature extraction plays a very important role for hyperspectral data classification, which can reduce noise from the original data and improve the separability of land classes. A novel feature extraction technique based on spectral dimensional edge preserving filter is proposed in this paper. A series of Gaussian filters are applied in the spatial domain of the hyperspectral image to produce the guidance image, then, the edge preserving filter which is guided by the guidance image is adopted and applied in the spectral domain of the hyperspectral data to get the feature. For the feature is produced by filtering in the spectral domain, the spectral curves of the feature are more continues, which avoids the spectral discontinuity problems result from the traditional two-dimensional spatial filter. The guidance image is obtained by filtering the original image in the spatial domain, so, the spatial and the spectral information are integrated together in the following spectral edge preserving filtering process. We carefully adjusted the parameters of the filter and applied it to different real hyperspectral remote sensing images, with the support vector machine, multinomial logistic regression, and random forest serving as the classifier, by comparing with other feature extraction methods presented in recent literature, the results indicate that the proposed methodology always has a great performance in different kinds of cases.     

Comparative Study of ELM and SVM in Hyperspectral Image Supervision Classification

Combining the spatial features and spectral feature of hyperspectral remote sensing image in supervised classification can effectively improve the classification time and accuracy.In this study,the spatial information extraction method,named watershed transform,was combined with the Extreme Learning Machine(ELM)and Support Vector Machine(SVM)methods.The classification results of the datasets with the spatial features and without the spatial features were synthetically evaluated and compared.Two hyperspectral datasets,the ROSIS data of Pavia university and the Hyperion data of Okavango Delta(Botswana),were selected to test the methods.After preprocessing,the training samples were selected from the images as the reference areas for each type,and the spectral features of each type were analyzed.The two classification methods were utilized to classify the hyperspectral datasets and relevant classification results were obtained.based on the validation samples selected from the images,the classification results were evaluated using the confusion matrix and the execution times.After that,the spectral features and spatial features were combined to classify the data.The results show that the Extreme Learning Machine(ELM) is superior to the Support Vector Machine(SVM)in the classification time and precision,and the spatial features are introduced in the classification process,which can effectively improve the classification accuracy.     

基于面向对象方法的沙化土地遥感信息提取技术研究

针对基于像元光谱特征提取沙化土地信息分类精度偏低的问题,以Landsat\|5 TM为数据源,基于面向对象的方法对沙化土地遥感信息提取技术进行研究。首先采用多尺度分割法对影像进行分割以获得同质区域,然后结合野外调查数据制成不同地物类型的多种特征图,从而确定提取目标地物的特征并建立沙化和非沙化地物提取决策树,最后对影像进行模糊分类,并对分类结果进行精度评价。结果表明,基于面向对象提取沙化土地信息的总精度达84.89%,Kappa系数为0.8077。研究结果为后续深入研究奠定了基础。     

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。     

A maximum noise fraction transform with improved noise estimation for hyperspectral images

Feature extraction is often performed to reduce spectral dimension of hyperspectral images before image classification. The maximum noise fraction(MNF) transform is one of the most commonly used spectral feature extraction methods. The spectral features in several bands of hyperspectral images are submerged by the noise. The MNF transform is advantageous over the principle component(PC) transform because it takes the noise information in the spatial domain into consideration. However,the experiments describ...     

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。