Exploiting spectral and spatial information for the identification of hazelnut fields using self-organizing maps

Automatic land-cover identification using remote-sensing images is essential for agricultural management and monitoring, which is an ongoing challenge. For permanent crops, which are of great importance economically and environmentally, it becomes even more challenging mainly due to the varying statistics of orchards such as the existence of different orchard types, different crown sizes even for the same type, different distances between orchards among various fields and overlapping crowns. This challenge necessitates the utilization of both spectral values and spatial relations of pixels. To accurately determine the fields of permanent crops, hazelnuts in particular, a classification system with hybrid learning, which merges an image features map (IFM) and learning vector quantization (LVQ), is proposed in this study. IFM is a variant of a self-organizing map (an unsupervised neural learning paradigm successfully used in many applications of remote-sensing imagery), which exploits both spectral and spatial information without additional computation of texture. LVQ, however, is supervised learning for fine-tuning of class boundaries. Experimental results on finding hazelnut fields using multispectral QuickBird imagery indicate that the proposed method achieves acceptable accuracies and often produces more accurate extraction than the accuracies obtained based only on spectral or on spatial information.     

Detecting shadows in high-resolution remote-sensing images of urban areas using spectral and spatial features

In accordance with the characteristics of urban high-resolution (HR) remote-sensing images, we propose a shadow detection algorithm that combines spectral and spatial features. Rather than pixel-based shadow features, the proposed features are based on shadow regions obtained by the object-based segmentation method. First, based on the shadow ratio map, the candidate shadow pixels are acquired by the Otsu method. The candidate shadow regions can be identified using connected component analysis. In the candidate shadow regions, shadow spectral and spatial features are calculated. With these two features, the true shadow regions can be distinguished from candidate shadow regions. Experiments and comparisons indicate that our proposed algorithm is feasible and effective for shadow detection in both aerial and satellite images.     

New method and error analysis of lake retrieval with MetOp-A AVHRR images on the Tibetan Plateau

Land-surface water is an important factor influencing the regional environment and climate and is a key factor in the Tibetan Plateau, which is one of the most sensitive regions to global changes. Because of the high elevation, complex topography, and erratic weather of the Tibetan Plateau, direct measurement of the area of every lake is largely unfeasible. Moreover, complex natural geographic conditions increase the difficulty of image processing and information extraction with remote sensing because they enhance the uncertainty of quantitative data retrieved with satellites. Methods based on spectral features do not generate the expected results of lake area over the Tibetan Plateau due to a lack of distinction between water and other land objects, especially snow, vegetation, and low cloud cover. Therefore, a new method to extract lake area from satellite images in the Tibetan Plateau is needed. In this article, an automatic method was proposed to evaluate lake area during the wet season (from 1 September to 31 October) on the Tibetan Plateau with multi-day Advanced Very High Resolution Radiometer (AVHRR) remote-sensing images on board the Meteorological Operational satellite-A (MetOp-A) satellite. The method considers both spectral and textural features of lakes and does not need a cloud mask as an input. In addition, the Mixture Tuned Matched Filtering (MTMF) algorithm was applied to decompose the mixed pixels to better identify lakes and estimate the lake area. Based on daily lake identifications, the wet season’s lake data were composited with the maximum value composition (MVC) method to determine the lake area. A comparison of our work with the manually interpreted results from Landsat Thematic Mapper (TM) images and observational reports demonstrates the accuracy and reliability of our approach. However, certain factors, i.e. the sensor zenith angle of the polar-orbit satellite and the topography, can affect the lake area extracted from the remote-sensing images.     

基于卷积神经网络的遥感图像分类研究

遥感图像分类是模式识别技术在遥感领域的具体应用,针对遥感图像处理中的分类问题,提出了一种基于卷积神经网络(convolutional neural networks,CNN)的遥感图像分类方法,并针对单源特征无法提供有效信息的问题,设计了一种多源多特征融合的方法,将遥感图像的光谱特征、纹理特征、空间结构特征等按空间维度以向量或矩阵的形式进行有效融合,以此训练CNN模型。实验表明,多源多特征相融合能够加快模型收敛速度,有效提高遥感图像的分类精度;与其他分类方法相比,CNN能够取得更高的分类精度,获得更优的分类效果。     

Feature extraction for high-resolution imagery based on human visual perception

Feature extraction is highly important for classification of remote-sensing (RS) images. However, extraction of comprehensive spatial features from high-resolution imagery is still challenging, leading to many misclassifications in various applications. To address the problem, a shape-adaptive neighbourhood (SAN) technique is presented based on human visual perception. The SAN technique is an adaptive feature-extraction method that not only considers spectral feature information but also the spatial neighbourhood as well as the shape of features. The distinct advantage of this approach is that it can be adjusted to different feature sizes and shapes. Assessment experiments on a Système Pour l'Observation de la Terre 5 (SPOT-5) image were conducted to perform classification of land use/land cover. Results showed that improvement with SAN features is not significant for supervised classifiers due to the spectral confusion problem that resulted from similar spectral signatures between farmland and green areas, but a particularly significant improvement is observed for the unsupervised classifier. For the unsupervised classification, the SAN features noticeably improved the overall accuracy from 0.58 to 0.86, and the kappa coefficient from 0.45 to 0.80, indicating promise in the application of SAN features in the auto-interpretation of RS images.     

Hyperspectral data spectrum and texture band selection based on the subspace-rough set method

In order to improve the utilization rate of spectroscopic data and texture information, this study proposes a method for optimal selection of spectrum and texture features based on automatic subspace division and rough set theory. This method takes advantage of rough set reduct ideology in order to realize the reduction of different types of ground object spectral features on the basis of the conventional subspace division method. In using this method, the primary spectral band based on spectral information can be determined. Then, the grey-level co-occurrence matrix method can be used to calculate the texture information of the primary spectral band and determine the reduction and optimization in order to obtain the final band based on the spectrum and texture information. Verification of this method is made by using CASI data of Heihe Region, China, and AVIRIS data of the Indiana Region, USA, and also using Support Vector Machine (SVM) classification of the original spectral, primary spectral, and final bands. The results indicate the following. (1) The method for optimal selection of the critical spectral band and texture band, based on the rough set theory, can efficiently improve the classification accuracy of high-spatial resolution remote-sensing images. However, the effects for the low-spatial resolution images are minimal. (2) For high-spatial-resolution remote-sensing images, such as roads, trenches, buildings, and other types of object with obvious textural features, the addition of image texture information can increase the degree of distinction of these different types and thereby improve the classification accuracy. However, the addition of the textural information for some objects with similar texture features will cause misclassification and reduce the classification accuracy for these types of images. (3) This method can realize the optimal selection of spectrum and texture bands of a hyperspectral image and has a certain universality. Also, the texture information will be richer and this method will be more practical through increasing the spatial resolution of images.     

Pan-sharpening based on common saliency feature analysis and multiscale spatial information extraction for multiple remote sensing images

ABSTRACT

The requirements of spectral and spatial quality differ from region to region in remote sensing images. The employment of saliency in pan-sharpening methods is an effective approach to fulfil this kind of demands. Common saliency feature analysis, which considers the mutual information between multiple images, can ensure the consistency and accuracy when assigning saliency to regions in different images. Thus, we propose a pan-sharpening method based on common saliency feature analysis and multiscale spatial information extraction for multiple remote sensing images. Firstly, we extract spatial information by the guided filter and accurate intensity component estimation. Then, a common saliency feature analysis method based on global contrast calculation and intensity feature extraction is designed to obtain preliminary pixel-wise saliency estimation, which is subsequently integrated with text-featured based compensation to generate adaptive injection gains. The introduction of common saliency feature analysis guarantees that the same pan-sharpening strategy will be applied to regions with similar features in multiple images. Finally, the injection gains are used to implement the detail injection. Our proposal satisfies diverse needs of spatial and spectral information for different regions in the single image and guarantees that regions with similar features in different images are treated consistently in the process of pan-sharpening. Both visual and quantitative results demonstrate that our method has better performance in guaranteeing consistency in multiple images, improving spatial quality and preserving spectral fidelity.     

Restoration of shadows in multispectral imagery using surface reflectance relationships with nearby similar areas

The presence of shadows in optical satellite images limits the application of remote-sensing technology. It is important to restore shadow radiance information for improving information extraction from remote-sensing images. Several shadow-restoration methods have been developed using complex statistical relationships between shadowed areas and their nearby sunlit areas. In this study, a simple shadow-restoration approach was proposed based on the surface reflectance equality relationship (RER) under the assumption that the surface reflectance of a feature in the shadowed area is equal to that of the same feature in the nearby sunlit area. This approach reduces the number of parameters, thus reducing the error propagated by the uncertainties of extra parameters. The new RER method was tested with three multispectral images with different shadow features. By comparing RER with the widely used mean and variance transformation, the RER was shown to be capable of restoring the image colours, texture, tone, and brightness of the shadowed areas to a visually satisfactory level. Quantitative analysis suggests that RER can help to restore the reflectance of shadow features accurately and has robust performance for a variety of land-surface types. Moreover, RER can be effectively used to restore the spectral shape information of shadow features, which is particularly important when applying RER to the restoration of multispectral imagery for the purpose of image classification.     

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     

A modified object-oriented classification algorithm and its application in high-resolution remote-sensing imagery

High-resolution satellite images offer abundant information on the Earth's surface for remote-sensing applications. The traditional pixel-based image classification method only used by spectral information has been proved to have several drawbacks. To satisfactorily interpret high-resolution imagery, other important information such as geometry, texture and semantics must be used, which are represented not only in single pixels but in meaningful image objects. So, a modified high-resolution image classification algorithm with multi-characteristics based on objects is presented in this article. First, image objects are extracted by multi-scale multi-characteristic segmentation. Second, characteristics such as spectral information, geometry, texture and semantics are extracted by the corresponding extraction algorithm. Finally, the image objects are classified by means of fuzzy-logic classification with a weighted average calculation method. Preliminary results show promise in terms of classification quality and accuracy.     

Extraction of bridges over water from high-resolution optical remote-sensing images based on mathematical morphology

Bridges over water are typical man-made structures on the land’s surface. An accurate extraction of such bridges from high-resolution optical remote-sensing images plays an important role in civil, commercial, and military applications. Considering the complex features of ground objects within high-resolution optical remote-sensing images and the inefficiency of previous methods of bridge extraction with random bridge orientation, direction-augmented linear structuring elements were constructed and applied in this study by using mathematical morphology to identify and extract bridges over water with different orientations. First, the image pre-processing is performed to facilitate the object extraction. Then by using the histogram-based threshold segmentation method, water bodies such as rivers are extracted and described as a binary image. Based on water bodies, the appropriate direction-augmented linear structuring element is then selected. Together with mathematical morphology operations, such as dilation and erosion, potential bridges are extracted by overlay analysis. Assisted by prior knowledge of bridges, false bridges are screened out and post-processing is finally performed to refine the extracted true bridges. This approach was validated with experiments in Shanghai and Beijing, China. The results show that the direction-augmented linear structuring elements are of high precision and have the capability of extracting bridges over water in different directions within the high-resolution optical remote-sensing image, considering both qualitative and quantitative aspects. Therefore, this approach may be useful in updating geographical databases of bridges and facilitating the assessment of bridge damage caused by natural disasters.     

基于对象关系特征的高分辨率光学卫星影像水上桥梁目标识别方法

利用卫星遥感技术对大中型桥梁进行识别定位,在民用上和军事上都具有很重要的意义。本研究提出了一套利用基元对象关系特征提取高分辨率卫星影像中水上桥梁的技术方法。首先利用多尺度分割算法对高分辨率卫星影像进行分割,利用水体指数或GLCM同质性纹理特征区分河水和陆地;其次,利用对象形状特征和相邻的关系特征提取桥梁潜在区;将河流片段和桥梁潜在区专题二值化,利用数学形态学算子实现河流水面的连续化;最后利用叠加分析的方法获得最终的桥梁目标。本方法充分利用了桥梁与河流相邻和相交的空间关系特征,利用QuickBird和IKONOS高分辨率卫星影像进行实验,证明所提出的方法可以高精度的实现大中型水上桥梁的识别定位。     

Multi‐scale information extraction from high resolution remote sensing imagery and region partition methods based on GMRF–SVM

This paper proposes the work flow of multi‐scale information extraction from high resolution remote sensing images based on features: rough classification – parcel unit extraction (subtle segmentation) – expression of features – intelligent illation – information extraction or target recognition. This paper then analyses its theoretical and practical significance for information extraction from enormous amounts of data on a large scale. Based on the spectrum and texture of images, this paper presents a region partition method for high resolution remote sensing images based on Gaussian Markov Random Field (GMRF)–Support Vector Machine (SVM), that is the image classification based on GMRF–SVM. This method integrates the advantages of GMRF‐based texture classification and SVM‐based pattern recognition with small samples and makes it convenient to utilize a priori knowledge. Finally, the paper reports tests on Ikonos images. The experimental results show that the method used here is superior to GMRF‐based segmentation in terms of both the time expenditure and processing effect. In addition, it is actually meaningful for the stage of information extraction and target recognition.     

集成特征分量的高分二号影像阴影检测

针对高分辨率遥感影像中阴影检测精度易受水体、植被等因素干扰的问题，通过分析高分二号影像中典型地物的光谱特征，构建了一种集成特征分量与面向对象分类相结合的阴影检测方法。构建的特征分量包括：主成分第一分量PC1、亮度分量I、归一化差分植被指数NDVI及水体指数WI。将各特征分量进行归一化处理，建立包含波段均值、标准差等特征的规则集，对影像的I和PC1分量进行多尺度分割 ，结合面向对象的方法进行阴影检测。选取不同区域遥感影像进行实验，实验结果表明:与传统基于像素的阴影提取方法相比，该方法提取出的阴影斑块完整，且能有效地减弱水体和植被的影响。     

Building change detection after earthquake using multi-criteria decision analysis based on extracted information from high spatial resolution satellite images

Change detection of ground surface objects can provide essential and precious information for experts in the fields of Geomatics, emergency management, urban management, agriculture, and forestry. Space-borne remote-sensing images are one of the main sources for change detection. Various change detection methods have been proposed on remote-sensing applications. However, often, no single efficient method can be selected for a case study because the existing methods sometimes have good performance and sometimes perform poorly. Therefore, it is necessary to propose an integrated change detection method according to some change detection methods. Multi-criteria decision analysis is a powerful framework that can integrate several criteria that may be in contrast to each other. In this study, a multi-criteria decision analysis framework was used to integrate the spectral, textural, and transformed features for detecting building changes with the help of high spatial resolution satellite images. First, the spectral, textural, and transformed features were extracted from the pre- and post-event satellite images. Second, the spectral, textural, and transformed factor maps were produced by entering the related features to three separate Adaptive Network-Based Fuzzy Inference Systems (ANFIS). Third, the ANFIS model was used again to integrate the mentioned factor maps for producing the preliminary building change map. And finally, a comprehensive sensitivity analysis was carried out to determine the proper parameters of the ANFIS models leading to accurate change detection results. The proposed method was tested on the earthquake data set of Bam City in Iran. The achieved results indicated an overall accuracy of 89.62% for identifying the changed and unchanged building regions. Moreover, the obtained results proved the efficiency and accuracy of the proposed method with respect to other implemented methods regarding the Bam earthquake. Furthermore, the aggregation of the spectral, transformed, and textural features resulted in improving the change detection accuracy by about 5–15%, compared with the accuracy of every one of them for the mentioned purpose.     

集成特征分量的高分二号影像阴影检测

针对高分辨率遥感影像中阴影检测精度易受水体、植被等因素干扰的问题,通过分析高分二号影像中典型地物的光谱特征,构建了一种集成特征分量与面向对象分类相结合的阴影检测方法。构建的特征分量包括：主成分第一分量PC1、亮度分量I、归一化差分植被指数NDVI及水体指数WI。将各特征分量进行归一化处理,建立包含波段均值、标准差等特征的规则集,对影像的I和PC1分量进行多尺度分割 ,结合面向对象的方法进行阴影检测。选取不同区域遥感影像进行实验,实验结果表明:与传统基于像素的阴影提取方法相比,该方法提取出的阴影斑块完整,且能有效地减弱水体和植被的影响。     

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.     

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。     

IKONOS影像白泡云识别方法研究

高空间分辨率IKONOS影像应用在海洋遥感时,白泡云的强反射特性严重影响了水体信息的提取。本文利用归一化处理后的IKONOS影像数据研究分析了白泡云与其背景地物的光谱特征。通过光谱一阶微分运算形成特定的数学参数使得地物的光谱特征参量化,提取白泡云光谱特征,研究开发了白泡云遥感识别模型。试验结果表明该识别方法准确度高,在识别水体中白泡云区域的基础上可有效处理海洋遥感水体信息提取过程中白泡云的干扰影响。