Identifying spatial variability and complexity in wetland vegetation using an object-based approach

Coastal wetland vegetation is complex in form and function. Accurately mapping the spatial variation of vegetation complexity within these ecosystems is important for identifying areas of high conservation value that provide essential ecosystem services. In this study we delineate wetland vegetation, particularly mangrove and saltmarsh, to a vegetative morphological level that identifies spatial complexity in vegetation structure. This was achieved by integrating light detection and ranging (Lidar) and aerial imagery with an object-based approach. The results demonstrate that this is an effective methodology to identify vegetation complexity, with all study sites having greater than 90% classification accuracy. These high classification accuracies were underpinned by the use of Lidar data that provide detailed structural information about vegetation that is not captured with aerial imagery. This research highlights the importance of identifying spatial variability in vegetation structure when considering the value of coastal ecosystems and the services they provide.     

Coastal wetland vegetation classification with a Landsat Thematic Mapper image

Coastal wetland vegetation classification with remotely sensed data has attracted increased attention but remains a challenge. This paper explored a hybrid approach on a Landsat Thematic Mapper (TM) image for classifying coastal wetland vegetation classes. Linear spectral mixture analysis was used to unmix the TM image into four fraction images, which were used for classifying major land covers with a thresholding technique. The spectral signatures of each land cover were extracted separately and then classified into clusters with the unsupervised classification method. Expert rules were finally used to modify the classified image. This research indicates that the hybrid approach employing sub-pixel information, an analyst's knowledge and characteristics of coastal wetland vegetation distribution shows promise in successfully distinguishing coastal vegetation classes, which are difficult to separate with a maximum likelihood classifier (MLC). The hybrid method provides significantly better classification results than MLC.     

结合纹理特征改进的GBIS图像分割方法

针对GBIS(efficient graph-based image segmentation)方法在分割含有较丰富纹理信息的图像时, 分割效果不理想的问题, 在L*a*b*彩色空间下, 结合图像的纹理特征, 提出了一种改进GBIS图像分割方法, 记为IGBIS(improved efficient graph-based image segmentation)。该方法首先将图像由RGB空间转换到L*a*b*颜色空间; 接着, 结合L*a*b*彩色空间, 对GBIS方法中的权值函数作了改进, 引入了一个常数s, 用于控制相邻像素之间颜色的差异程度; 然后, 用熵的方法来获取L*a*b*彩色图像的纹理特征; 最后, 结合图像的纹理信息, 改变了GBIS方法中的区域合并条件, 得到最终的分割结果。实验证明, 与原算法相比, 该方法在分割精度与分割质量上有了很大程度的提高。IGBIS有效地抑制了彩色图像在分割中存在的过分割现象, 并适合于含有丰富纹理的彩色图像。     

湿地遥感图像分割算法设计及实现

提出了一种结合熵和模糊C均值的聚类分割方法。模糊C均值（FCM）聚类算法广泛用于图像的自动分割,但是传统的FCM算法没有考虑像素的空间信息,因而对噪声十分敏感,基于二维直方图的模糊C均值聚类算法除了考虑像素点的灰度信息外还考虑了像素点邻域的空间信息,可有效地抑制噪声;在目标函数中引入熵项则能更好地抑制噪声和外围点对类中心估计的影响。实验分析结果表明,算法对湿地遥感图像的分割效果优于FCM算法。     

Scale channel attention network for image segmentation

Multimedia Tools and Applications - The object scale variation results in a negative effect on image segmentation performance. Spatial pyramid pooling module or the attention mechanism are two...     

Classification of impervious land-use features using object-based image analysis and data fusion

The proportion of impervious area within a watershed is a key indicator of the impacts of urbanization on water quality and stream health. Research has shown that object-based image analysis (OBIA) techniques are more effective for urban land-cover classification than pixel-based classifiers and are better suited to the increased complexity of high-resolution imagery. Focusing on five 2-km² study areas within the Black Creek sub-watershed of the Humber River, this research uses eCognition® software to develop a rule-based OBIA workflow for semi-automatic classification of impervious land-use features (e.g., roads, buildings, Parking Lots, driveways). The overall classification accuracy ranges from 88.7 to 94.3%, indicating the effectiveness of using an OBIA approach and developing a sequential system for data fusion and automated impervious feature extraction. Similar accuracy results between the calibrating and validating sites demonstrates the strong potential for the transferability of the rule-set from pilot study sites to a larger area.     

Visual information fusion for object-based video image segmentation using unsupervised Bayesian online learning

An algorithm using the unsupervised Bayesian online learning process is proposed for the segmentation of object-based video images. The video image segmentation is solved using a classification method. First, different visual features (the spatial location, colour and optical-flow vectors) are fused in a probability framework for image pixel clustering. The appropriate modelling of the probability distribution function (PDF) for each feature-cluster is obtained through a Gaussian distribution. The image pixel is then assigned a cluster number in a maximum a posteriori probability framework. Different from the previous segmentation methods, the unsupervised Bayesian online learning algorithm has been developed to understand a cluster's PDF parameters through the image sequence. This online learning process uses the pixels of the previous clustered image and information from the feature-cluster to update the PDF parameters for segmentation of the current image. The unsupervised Bayesian online learning algorithm has shown satisfactory experimental results on different video sequences.     

结合多特征和SVM的SAR图像分割

为实现灰度共生矩阵(GLCM)多尺度、多方向的纹理特征提取, 提出了一种结合非下采样轮廓变换(NSCT)和GLCM的纹理特征提取方法。先用NSCT对合成孔径雷达(SAR)图像进行多尺度、多方向分解; 再对得到的子带图像使用GLCM提取灰度共生量; 然后对提取的灰度共生量进行相关性分析, 去除冗余特征量, 并将其与灰度特征构成多特征矢量; 最后, 充分利用支持向量机(SVM)在小样本数据库和泛化能力方面的优势, 由SVM完成多特征矢量的划分, 实现SAR图像分割。实验结果表明, 基于NSCT域的GLCM纹理提取方法和多特征融合用于SAR图像分割, 可以提高分割准确率, 获得较好的边缘保持效果。     

Artificial immune multi-objective SAR image segmentation with fused complementary features

Artificial immune systems (AIS) are the computational systems inspired by the principles and processes of the vertebrate immune system. AIS-based algorithms typically mimic the human immune system’s characteristics of learning and adaptability to solve some complicated problems. Here, an artificial immune multi-objective optimization framework is formulated and applied to synthetic aperture radar (SAR) image segmentation. The important innovations of the framework are listed as follows: (1) an efficient and robust immune, multi-objective optimization algorithm is proposed, which has the features of adaptive rank clones and diversity maintenance by K-nearest-neighbor list; (2) besides, two conflicting, fuzzy clustering validity indices are incorporated into this framework and optimized simultaneously and (3) moreover, an effective, fused feature set for texture representation and discrimination is constructed and researched, which utilizes both the Gabor filter’s ability to precisely extract texture features in low- and mid-frequency components and the gray level co-occurrence probability’s (GLCP) ability to measure information in high-frequency. Two experiments with synthetic texture images and SAR images are implemented to evaluate the performance of the proposed framework in comparison with other five clustering algorithms: fuzzy C-means (FCM), single-objective genetic algorithm (SOGA), self-organizing map (SOM), wavelet-domain hidden Markov models (HMTseg), and spectral clustering ensemble (SCE). Experimental results show the proposed framework has obtained the better performance in segmenting SAR images than other five algorithms and behaves insensitive to the speckle noise.     

Semantic image segmentation using low-level features and contextual cues

Semantic image segmentation aims to partition an image into non-overlapping regions and assign a pre-defined object class label to each region. In this paper, a semantic method combining low-level features and high-level contextual cues is proposed to segment natural scene images. The proposed method first takes the gist representation of an image as its global feature. The image is then over-segmented into many super-pixels and histogram representations of these super-pixels are used as local features. In addition, co-occurrence and spatial layout relations among object classes are exploited as contextual cues. Finally the features and cues are integrated into the inference framework based on conditional random field by defining specific potential terms and introducing weighting functions. The proposed method has been compared with state-of-the-art methods on the MSRC database, and the experimental results show its effectiveness.     

基于颜色特征的玉米雄穗图像分割

提出了一种基于颜色特征的玉米雄穗分割方法.利用侧抑制网络与二维Otsu结合的分割方法对玉米雄穗图像的YCbCr颜色空间的Cr分量进行分割,再利用相同方法对玉米雄穗图像的超绿特征图像进行分割,取两个分割结果的交集,去除小面积的连通域,得到玉米雄穗的分割图.为了验证算法的有效性,选用了不同生长环境的玉米雄穗图像,分别利用本文方法、二维Otsu和基于侧抑制的二维Otsu方法进行了比较实验.结果表明:该方法有很好的抗干扰性,对生长环境有很强的鲁棒性.     

An improved region-based model with local statistical features for image segmentation

In this paper, we propose a new region-based active contour model (ACM) for image segmentation. In particular, this model utilizes an improved region fitting term to partition the regions of interests in images depending on the local statistics regarding the intensity and the magnitude of gradient in the neighborhood of a contour. By this improved region fitting term, images with noise, intensity non-uniformity, and low-contrast boundaries can be well segmented. Integrated with the duality theory and the anisotropic diffusion process based on structure tensor, a new regularization term is defined through the duality formulation to penalize the length of active contour. By this new regularization term, the structural information of images is utilized to improve the ability of capturing the geometric features such as corners and cusps. From a numerical point of view, we minimize the energy function of our model by an efficient dual algorithm, which avoids the instability and the non-differentiability of traditional numerical solutions, e.g. the gradient descent method. Experiments on medical and natural images demonstrate the advantages of the proposed model over other segmentation models in terms of both efficiency and accuracy.     

加权方位特征的纹理图像FCM聚类分割

针对自然纹理的弱规则性特点,提出一种结合图像灰度分布、纹理能量统计的加权FCM纹理分割方法,并在该方法中尝试引入水平位置、垂直位置、中心径向距离等三种方位特征参数。实验证明,该加权FCM聚类算法简单高效、可控性强,纹理分割效果明显,其中样本的三种加权方位特征参数能增强区域分割的聚敛度,有效地提高纹理尤其是非均质粗糙纹理的分割精度。     

Mapping vegetation morphology types in a dry savanna ecosystem: integrating hierarchical object-based image analysis with Random Forest

Savanna ecosystems are geographically extensive and both ecologically and economically important, and require monitoring over large spatial extents. Remote-sensing-based characterization of vegetation properties in savannas is methodologically challenging, mainly due to high structural and functional heterogeneity. Recent advances in object-based image analysis (OBIA) and machine learning algorithms offer new opportunities to address these challenges. Focusing on the semi-arid savanna ecosystem in the central Kalahari, this study examined the suitability of a hierarchical OBIA approach combined with in situ data and an ensemble classification technique for mapping vegetation morphology types at landscape scale. A stack of Landsat TM imagery, NDVI, and topographic variables was segmented with six different scale factors resulting in a hierarchical network of image objects. Sample objects for each vegetation morphology class were selected at each segmentation scale and classification was performed using optimal features consisting of spectral and textural features. Overall and class-specific classification accuracies were compared across the six scales to examine the influence of segmentation scale on each. Results suggest that the highest overall classification accuracy (i.e. 85.59%) was observed not at the finest segmentation scale, but at coarse segmentation. Additionally, individual vegetation morphology classes differed in the segmentation scale at which they achieved highest classification accuracy, reflecting their unique ecology and physiognomic composition. While classes with high vegetation density/height attained higher accuracy at fine segmentation scale, those with lower vegetation density/height reached higher classification accuracy at coarse segmentation scales. Contrarily, for pans and bare areas, accuracy was relatively unaffected by changing segmentation scale. Variable importance plots suggested that spectral features were the most important, followed by textural variables. These results show the utility of the OBIA approach and emphasize the requirement of multi-scale analysis for accurately characterizing savanna systems.     

基于多尺度颜色小波纹理特征的火焰图像分割

火焰图像分割质量对基于数字成像的燃烧监测十分重要。受炉膛背景及燃烧工况的影响,难以同时满足火焰图像分割速度和准确度（即火焰图像分割结果与真实火焰接近程度）的需求。提出一种基于多尺度颜色特征和小波纹理特征（MCWT）的无监督火焰图像分割方法,用于提高火焰图像分割的质量和速度。结合火焰图像颜色特征及小波纹理特征构建特征矩阵,对特征矩阵进行压缩并初步检测压缩尺度火焰区域。根据压缩尺度火焰边缘确定原始尺度火焰边缘区域并构建火焰边缘区域特征矩阵,进一步分割得到准确火焰图像分割结果。采用该方法对某工业煤燃烧实验炉内不同燃烧工况下的火焰图像进行分割,并与传统分割方法对比。实验结果表明与其他传统分割方法相比,提出方法能够更准确且快速地实现不同燃烧工况下火焰图像的分割,并且其对于含有高斯噪声和椒盐噪声的火焰图像都具有更好的分割效果。     

结合目标局部和全局特征的 CV遥感图像分割模型

摘 要：随着遥感卫星技术的发展,高分辨率遥感影像不断涌现。从含有较多信息、背景 复杂的遥感影像中自动提取目标成为一个亟待解决的难题。传统的图像分割方法主要依赖图像 光谱、纹理等底层特征,容易受到图像中遮挡和阴影等的干扰。为此,针对特定的目标类型, 提出结合目标局部和全局特征的 CV (Chan Vest)遥感图像目标分割模型,首先,采用深度学习 生成模型——卷积受限玻尔兹曼机建模表征目标全局形状特征,以及重建目标形状;其次,利 用 Canny 算子提取目标边缘信息,经过符号距离变换得到综合了局部边缘和全局形状信息的约 束项;最终,以 CV 模型为图像目标分割模型,增加新的约束项得到结合目标局部和全局特征 的 CV 遥感图像分割模型。在遥感小数据集 Levir-oil drum、Levir-ship 和 Levir-airplane 上的实 验结果表明：该模型不仅可以克服 CV 模型对噪声敏感的缺点,且在训练数据有限、目标尺寸 较小、遮挡及背景复杂的情况下依然能完整、精确地分割出目标。     

Medical image segmentation with transform and moment based features and incremental supervised neural network

In this study, a novel incremental supervised neural network (ISNN) is proposed for the segmentation of medical images. Performance of the ISNN is investigated for tissue segmentation in medical images obtained from various imaging modalities. Two feature extraction methods based on transform and moments are comparatively investigated to segment the tissues in medical images. Two-dimensional (2D) continuous wavelet transform (CWT) and the moments of the gray-level histogram (MGH) are computed in order to form the feature vectors of ultrasound (US) bladder and phantom images, X-ray computerized tomography (CT) and magnetic resonance (MR) head images. In the 2D-CWT method, feature vectors are formed by the intensity of one pixel of each wavelet-plane of different energy bands. The MGH represents the tissues within the sub-windows by using the spatial variation of image intensities. In this study, the ISNN and Grow and Learn (GAL) network are employed for the segmentation task. It is observed that the ISNN has significantly eliminated the disadvantages of the GAL network in the segmentation of the medical images.     

多尺度条形池化与通道注意力的图像语义分割

目的 针对自然场景下图像语义分割易受物体自身形状多样性、距离和光照等因素影响的问题,本文提出一种新的基于条形池化与通道注意力机制的双分支语义分割网络(strip pooling and channel attention net, SPCANet)。方法 SPCANet从空间与内容两方面对图像特征进行抽取。首先,空间感知子网引入1维膨胀卷积与多尺度思想对条形池化技术进行优化改进,进一步在编码阶段增大水平与竖直方向上的感受野;其次,为了提升模型的内容感知能力,将在ImageNet数据集上预训练好的VGG16(Visual Geometry Group 16-layer network)作为内容感知子网,以辅助空间感知子网优化语义分割的嵌入特征,改善空间感知子网造成的图像细节信息缺失问题。此外,使用二阶通道注意力进一步优化网络中间层与高层的特征选择,并在一定程度上缓解光照产生的色差对分割结果的影响。结果 使用Cityscapes作为实验数据,将本文方法与其他基于深度神经网络的分割方法进行对比,并从可视化效果和评测指标两方面进行分析。SPCANet在目标分割指标mIoU(mean inter...     

基于通道注意力机制和U-net的医学图像分割方法

随着人工智能和医学大数据的发展,基于深度学习的医学图像分割技术因具有重要的应用价值和前景,已经成为目前的研究热点.为了增强特征图的语义信息,在U-net网络的基础上引入通道注意力机制,对U-net生成的特征逐通道进行压缩,将压缩后的特征逐通道计算权重,然后将该权重与原始特征相乘得出最终的特征.通过在两个不同器官的医学图像数据集上进行实验,Dice系数相较于原始U-net网络分别提高了2.7％和1.8％,验证了该方法的可行性和有效性.     

傅里叶变换通道注意力网络的胆管癌高光谱图像分割

目的 胆管癌高光谱图像的光谱波段丰富但存在冗余,造成基于深度神经网络高光谱图像分割方法的分割精度下降,虽然一些基于通道注意力机制的网络能够关注重要通道,但在处理通道特征时存在信息表示不足问题,因此本文研究构建一种新的通道注意力机制深度网络,以提高分割准确性。方法 提出了傅里叶变换多频率通道注意力机制（frequency selecting channel attention,FSCA）。FSCA对输入特征进行2维傅里叶变换,提取部分频率特征,再通过两层全连接层得到通道权重向量,将通道权重与对应通道特征相乘,获得了融合通道注意力信息的输出。针对患癌区域和无癌区域数据不平衡问题引入了Focal损失,结合Inception模块,构建基于Inception-FSCA的胆管癌高光谱图像分割网络。结果 在采集的胆管癌高光谱数据集上进行实验,Inception-FSCA网络的准确率（accuracy）、精度（precision）、敏感性（sensitivity）、特异性（specificity）、Kappa系数分别为0.978 0、0.965 4、0.958 6、0.985 2、0.945 6,优于另外5种对比方法。与合成的假彩色图像的分割结果相比,高光谱图像上的实验指标分别提高了0.058 4、0.105 8、0.087 5、0.039 0、0.149 3。结论 本文所提出的傅里叶变换多频率通道注意力机制能够更有效地利用通道信息,基于Inception-FSCA的胆管癌高光谱图像分割网络能够提升分割效果,在胆管癌医学辅助诊断方面具有研究和应用价值。