Semantic-Sensitive Satellite Image Retrieval

Content-based image-retrieval techniques based on query scenes are a powerful means for exploration and mining of large remote sensing image databases. However, the gap between low-level unsupervised extracted features in content-based retrieval and the high-level semantic concepts of user queries limits the performance. Therefore, this paper proposes a specialized approach using a context-sensitive Bayesian network for semantic inference of segmented scenes. The regions' remote sensing related semantic concepts are inferred in a multistage process based on their spectral and textural characteristics as well as the semantics of adjacent regions. During the actual retrieval, the semantics are employed for the extraction of candidate scenes which are evaluated and ranked in a consecutive step. The approach was implemented and compared with a different strategy that utilizes the extracted features from the imagery directly to infer the semantics. In summary, the developed system achieved higher precision and recall rates using the same training data     

综合高分卫星图像多维特征的云检测方法

云是遥感图像分析处理的一大障碍,为了解决这一问题,基于高分1号遥感影像光谱和纹理的多维特征信息,提出一种综合优化的云检测方法。针对光谱检测出的似云区域,该算法采用新的子图分割方法,结合动态阈值设置,有效提高了纹理检测的正确率。由于固定的光谱阈值设置和纹理检测都无法获取复杂环境下的云层边界信息,算法采用大津法予以修复。结果表明,该算法可有效地检测出影像中的云覆盖区域,实现薄云、厚云以及厚云边界信息的最佳提取。     

一种高效的海量遥感栅格数据库的空间可视化检索算法

该文针对利用GIS现有空间查询接口进行海量遥感栅格数据库空间可视化检索效率低下的问题。在深入研究海量遥感栅格数据库的空间可视化检索特点的基础上,提出了一种直接针对关系数据库(RDBMS)存储过程的高效的海量遥感栅格数据库复杂空间可视化检索算法,并对算法进行了多个级别的性能优化。该算法可直接应用于海量遥感栅格数据库基于多边形、椭圆和线穿越等复杂空间可视化查询的应用环境。实验结果说明该算法具有稳定性和普适性。     

Feature fusion via dual-resolution compressive measurement matrix analysis for spectral image classification

In the compressive spectral imaging (CSI) framework, different architectures have been proposed to recover high-resolution spectral images from compressive measurements. Since CSI architectures compactly capture the relevant information of the spectral image, various methods that extract classification features from compressive samples have been recently proposed. However, these techniques require a feature extraction procedure that reorders measurements using the information embedded in the coded aperture patterns. In this paper, a method that fuses features directly from dual-resolution compressive measurements is proposed for spectral image classification. More precisely, the fusion method is formulated as an inverse problem that estimates high-spatial-resolution and low-dimensional feature bands from compressive measurements. To this end, the decimation matrices that describe the compressive measurements as degraded versions of the fused features are mathematically modeled using the information embedded in the coded aperture patterns. Furthermore, we include both a sparsity-promoting and a total-variation (TV) regularization terms to the fusion problem in order to consider the correlations between neighbor pixels, and therefore, improve the accuracy of pixel-based classifiers. To solve the fusion problem, we describe an algorithm based on the accelerated variant of the alternating direction method of multipliers (accelerated-ADMM). Additionally, a classification approach that includes the developed fusion method and a multilayer neural network is introduced. Finally, the proposed approach is evaluated on three remote sensing spectral images and a set of compressive measurements captured in the laboratory. Extensive simulations show that the proposed classification approach outperforms other approaches under various performance metrics.     

基于高分二号卫星遥感数据的建筑物高度提取

建筑高度信息提取是高分辨率卫星遥感获取城市空间信息的热点问题之一。为了实现建筑物高度信息的高精度提取,提出了一种基于 规则信息的面向对象的建筑物高度反演方法。首先根据建筑物阴影几何、纹理、和光谱特征建立基于规则信息的建筑物阴影 提取算法,进行建筑物阴影信息提取,再通过计算像元平均值算出阴影长度,构建卫星、太阳、建筑物和其阴影的空间模型反演 建筑物高度。采用扬州市两个实验区(佳家花园和杉湾小区)的高分二号卫星遥感数据进行高度反演试验,通过对反演小区的105栋 建筑物实际高度测量,对反演结果验证,总体精度为96.5%。表明该方法在城市建筑物高度信息提取方面具有较高的精度。     

基于多尺度池化和范数注意力机制的遥感图像检索

遥感图像内容丰富,一般的深度模型提取遥感图像特征时容易受复杂背景干扰,对关键特征的提取效果不佳,并且难以表达图像的空间信息,该文提出一种基于多尺度池化和范数注意力机制的深度卷积神经网络,在通道层面与空间层面自适应地给显著特征加权.首先,在多尺度池化通道注意力模块中,结合空间金字塔池化的思想,对每个通道上的特征图进行不同...     

由红外遥感数据反演地物的物元可拓识别方法

遥感参数数据往往类别多,混合度大,由它反地物类别是一个不相容问题,。为了研究了红外遥感数据快速有效地识别地物类别,提出用物元可拓集理论构造不同地物类别的标准物元矩阵和节域物元矩阵,计算待识别对象与各类物物的关联度,根据关联度大小判断地物的类别。     

基于非局部相似度约束的多通道复用压缩遥感成像方法

结合压缩感知成像原理和遥感成像系统的物理可实现性,提出了采用掩膜编码的多通道复用压缩成像方法。首先,采用多组随机二值伯努利分布的掩膜为不同光学通道视场进行压缩编码,在单位积分时间内采集一帧图像重构所需欠采样数据。然后,针对传统的全变分范数最小化的重构方法易受遥感图像局部突出特征干扰的问题,提出了以遥感图像空间域非局部相似度为正则化重构标准的先验约束。实验结果验证了本文提出的压缩成像方法的可行性。与传统算法相比,本文提出的重构算法能够在保留图像细节的同时实现快速有效重构。     

Human-centered concepts for exploration and understanding of Earth observation images

The progress in information retrieval, computer vision, and image analysis makes it possible to establish very complete bases of algorithms and operators. A specialist in remote sensing or image processing now has the tools that allow him, at least in theory, to configure applications solving complex problems of image understanding. However, in reality, earth observation (EO) data analysis is still performed in a very laborious way at the end of repeated cycles of trial and error. To overcome this, we proposed a novel advanced remote sensing information processing system knowledge-driven information mining (KIM). KIM is based on human-centered concepts (HCCs), which implements new features and functions allowing improved feature extraction, search on a semantic level, the availability of collected knowledge, interactive knowledge discovery, and new visual user interfaces. We assess the HCC methodology for solving several difficult tasks in EO image interpretation, using a broad variety of sensor data, from meter-resolution synthetic aperture radar and optical images to hyperspectral data.     

Detecting Man-Made Structures and Changes in Satellite Imagery With a Content-Based Information Retrieval System Built on Self-Organizing Maps

The increasing amount and resolution of satellite sensors demand new techniques for browsing remote sensing image archives. Content-based querying allows an efficient retrieval of images based on the information they contain, rather than their acquisition date or geographical extent. Self-organizing maps (SOMs) have been successfully applied in the PicSOM system to content-based image retrieval in databases of conventional images. In this paper, we investigate and extend the potential of PicSOM for the analysis of remote sensing data. We propose methods for detecting man-made structures, as well as supervised and unsupervised change detection, based on the same framework. In this paper, a database was artificially created by splitting each satellite image to be analyzed into small images. After training the PicSOM on this imagelet database, both interactive and off-line queries were made to detect man-made structures, as well as changes between two very high resolution images from different years. Experimental results were both evaluated quantitatively and discussed qualitatively, and suggest that this new approach is suitable for analyzing very high resolution optical satellite imagery. Possible applications of this work include interactive detection of man-made structures or supervised monitoring of sensitive sites     

基于Word Embedding的遥感影像检测分割

遥感影像检测分割技术通常需提取影像特征并通过深度学习算法挖掘影像的深层特征来实现.然而传统特征（如颜色特征、纹理特征、空间关系特征等）不能充分描述影像语义信息,而单一结构或串联算法无法充分挖掘影像的深层特征和上下文语义信息.针对上述问题,本文通过词嵌入将空间关系特征映射成实数密集向量,与颜色、纹理特征的结合.其次,本文构建基于注意力机制下图卷积网络和独立循环神经网络的遥感影像检测分割并联算法（Attention Graph Convolution Networks and Independently Recurrent Neural Network,ATGIR）.该算法首先通过注意力机制对结合后的特征进行概率权重分配;然后利用图卷积网络（GCNs）算法对高权重的特征进一步挖掘并生成方向标签,同时使用独立循环神经网络（IndRNN）算法挖掘影像特征中的上下文信息,最后用Sigmoid分类器完成影像检测分割任务.以胡杨林遥感影像检测分割任务为例,我们验证了提出的特征提取方法和ATGIR算法能有效提升胡杨林检测分割任务的性能.     

基于非局部相似度约束的多通道复用压缩遥感成像方法

结合压缩感知成像原理和遥感成像系统的物理可实现性,提出了采用掩膜编码的多通道复用压缩成像方法.首先,采用多组随机二值伯努利分布的掩膜为不同光学通道视场进行压缩编码,在单位积分时间内采集重构图像所需的欠采样数据.然后,针对传统的全变分范数最小化的重构方法易受遥感图像局部突出特征干扰的问题,提出了以遥感图像空间域非局部相似度为正则化重构标准的先验约束.实验结果验证了此压缩成像方法的可行性.与传统算法相比,此重构算法能够在保留图像细节的同时实现有效重构.     

基于遥感数据的地球背景中红外场景仿真

卫星遥感是研究地球大气背景红外辐射特性的重要手段,受大气影响及传感器观测条件的限制,无法获取多气象、多探测条件的辐射数据。针对该问题,基于JHU地物光谱数据库,分析了植被、水体、岩石等光谱辐射特性,结合传感器光谱响应,建立了3~5 m基于光谱相关性的地表辐射波段转换模型,并利用逐步回归法计算误差,模型误差小于10%。利用MODIS、AIRS等多源遥感数据产品,根据地表-大气-传感器辐射传输模型,模拟不同时空分辨率、探测条件等的地球背景辐射中红外图像。结果表明,利用多源遥感数据进行中红外图像仿真,可以实现大尺度、具有细致纹理结构的图像模拟,应用于遥感研究。     

一种高分辨率遥感影像分类的特征指数

随着影像分辨率的提高,传统的光谱特征不能有效地描述复杂的高分辨率影像信息,从而影响高分辨率遥感影像的分类。为了弥补传统光谱方法的不足,提出了一种加权对象相关指数(WOCI),并将其应用到基于支持向量机(SVM)的影像分类中。该指数是通过考虑具有相似性光谱的对象来构建的,可全面地描述影像的上下文结构。结果表明与仅考虑光谱特征和像素空间特征进行分类的方法相比,基于WOCI特征的分类结果有更高的精确性,且分类精度提高了7.16%。     

Spectral texture pattern matching: a classifier for digital imagery

Because of the difficulty of specifying general criteria for texture features, automated image analysis in the field of remote sensing has been largely restricted to the spectral domain. An algorithm that integrates spectral and textural information in the classification process is presented. The procedure is capable of classifying a region of arbitrary shape and size and operates effectively near class boundaries. Except for the requirement of user-defined training data, the algorithm can be completely automated. For all accuracy measures tested, the classification accuracy of the spectral texture pattern matching algorithm was higher for most classes than that of the maximum-likelihood classifier. Furthermore, errors with the spectral/textural algorithm are largely confined to omission, which gives a high degree of confidence to the classified pixels     

联合多流融合和多尺度学习的卷积神经网络遥感图像融合方法

为尽可能保持原始低分辨率多光谱(LRMS)图像光谱信息的同时,显著提高融合后的多光谱图像的空间分辨率,该文提出一种联合多流融合和多尺度学习的卷积神经网络遥感图融合方法.首先将原始MS图像输入频谱特征提取子网得到其光谱特征,然后分别将通过梯度算子处理全色图像得到的梯度信息和通过卷积后的全色图像与得到的光谱特征图在通道上拼...     

Query-by-shape in meteorological image archives using the pointdiffusion technique

The authors work on meteorological satellite image archives and provide a novel and useful query-by-shape tool. To this aim, they first present the point diffusion technique (PDT), a fast and efficient method for shape similarity evaluation. Thanks to its very structure, this approach is suitable to handle objects whose shape is not well defined and can be represented by a set of sparse points. PDT is thus suitable for application to similarity-based retrieval from remotely sensed image archives, where shapes are hardly defined but are still among the major features of interest. Moreover, they prove here that PDT is almost as effective as more standard procedures for shape-based database queries, although significantly faster. In other words, it manages to combine retrieval speed and precision, the features of greatest importance for a first remote sensing data prescreening in many applications. Archives of meteorological satellite images are typical examples of very large-sized, remote sensing-based databases with a special attention for shape features. Each meteorological satellite produces terabytes of data every day, a large part of which is not immediately analyzed and ends being stored in archives. The application of PDT to such a database is presented and discussed, and a comparison with a standard method developed for meteorological shape analysis is provided     

Hyperspectral image data analysis

The fundamental basis for space-based remote sensing is that information is potentially available from the electromagnetic energy field arising from the Earth's surface and, in particular, from the spatial, spectral, and temporal variations in that field. Rather than focusing on the spatial variations, which imagery perhaps best conveys, why not move on to look at how the spectral variations might be used. The idea was to enlarge the size of a pixel until it includes an area that is characteristic from a spectral response standpoint for the surface cover to be discriminated. The article includes an example of an image space representation, using three bands to simulate a color IR photograph of an airborne hyperspectral data set over the Washington, DC, mall