Remote sensing research issues of the National Land Use Change Program of China

In 1999, the Ministry of Land and Resources (MLR) of China launched the National Land Use Change Program especially to monitor the scale and distribution of urban expansion and the decrease in cultivated land through remote sensing technology. This Program has been carried out annually and continuously for seven years since then and played an important role in the policy-making of MLR about land management and planning. This paper gives an overview about this Program and discusses several research issues. First, the remote sensing data sources and other ancillary data used in this Program are presented. The approaches for image preprocessing, i.e. radiometric normalization, image geometric rectification and image fusion are then introduced with an emphasis on the algorithm development for image registration. Second, land use change detection technique is the most critical and complex aspect of the Program. The methodologies for change detection using either bi-temporal image pair or one existing land use map and one remotely sensed image are detailed. Third, since the data of land use changes derived from remote sensing will be operationally used for local and central government, field validation and accuracy assessment are crucial to ensure the reliability of change detection results. The strategy of field work and the resulting accuracy evaluations is presented. The land use and change information derived from remotely sensed data has wide applications for land management, including land use database updating, verification of land use planning and monitoring of national high-tech parks. Last, suggestions on how to make full use of the images and change detection result, to improve the consistency of land use classification and to develop change detection algorithms for diverse and complex remote sensing data are given.     

多源遥感影像像素级融合分类与决策级分类融合法的研究

首先探讨了基于像素的多源遥感影像高频调制融合法，根据成像系统特性和Heisenberg测不准原理，设计的高斯滤波器对高分辨率影像滤波的方法是合理有效的。在研究BP神经网络的基础上，采用动量法和学习率自适应调整的策略，提高了BP神经网络学习算法收敛速度，并增强了算法的可靠性。提出并实现了多源遥感影像像素级融合分类与决策级分类融合两种分类方法，并进行了比较。采用Landsat TM3，4，5和航空SAR影像进行试验，结果表明两种分类方法是行之有效的，均适用于多源遥感影像分类。     

遥感大数据智能解译的地理学认知模型与方法

随着遥感数据和计算机算力的爆炸式增长、智能分析算法瓶颈的突破,亟须提升与之相匹配的遥感大数据处理与分析能力。针对复杂场景下遥感大数据智能处理与地理学认知耦合关联和交叉融合的关键问题,本文分析了遥感大数据与地理科学各自的特点与相互关系,提出了多模态知识融合关联的深度网络构建和面向地理制图的遥感智能解译思路,建立了遥感大数据智能处理与应用体系框架;面向技术发展和行业应用,本文提出了分别建设通用高分辨率遥感智能处理系统和智能精准应用平台的总体路线,以期推动遥感智能解译技术创新和工程化应用的全面发展。     

地表蒸散定量遥感的研究进展

简要介绍了遥感监测地表蒸散和能量平衡研究的科学意义和应用价值，回顾了国内外的研究历史和现状，分析了目前该领域存在的一些问题和难点，并对今后的工作重点和研究方向提出了建议。     

An improved SVM model for relevance feedback in remote sensing image retrieval

With the rapid development of satellite remote sensing technology and an ever-increasing number of Earth observation satellites being launched, the global volume of remotely sensed imagery has been growing exponentially. Processing the variety of remotely sensed data has increasingly been complex and difficult. It is also hard to efficiently and intelligently retrieve what users need from a massive database of images. This paper introduces an improved support vector machine (SVM) model, which optimizes the model parameters and selects the feature subset based on the particle swarm optimization (PSO) method and genetic algorithm (GA) for remote sensing image retrieval. The results from an image retrieval experiment show that our method outperforms traditional methods such as GRID, PSO, and GA in terms of consistency and stability.     

面向农作物监测的遥感信息处理技术研究

以区域性主要农作物种类识别、长势分析与产量估算及农业种植结构现状监测为主要研究对象,开展适于农业管理部门业务化运行的卫星遥感信息处理的关键技术研究。从分析主要作物类型识别的遥感物理依据入手,提出了卫星遥感数据处理及专题信息提取的基本技术框架、主要农作物类型及种植面积信息的提取方法以及主要粮食作物长势分析和产量估算模型,并对结果进行了简要的精度分析。     

基于空间数据挖掘的遥感图像处理

数据与数据库的爆炸式增长导致了一个十分突出的问题,即如何高效、智能地从巨量的、有噪音的、随机的数据中提取有效的、潜在有用的信息和知识.近几年来,空间数据挖掘技术的广泛研究正是基于此目的.本文初步探讨了空间数据挖掘技术在遥感图像处理中的应用,其重点阐述了关联规则,以及数据挖掘技术在遥感图像数据处理中的基本方法以及如何对遥感图像数据进行离散化处理.文章最后简要介绍了遥感图像处理的决策树和人工神经网络数据挖掘技术方法.     

A kernel functions analysis for support vector machines for land cover classification

Information about the Earth's surface is required in many wide-scale applications. Land cover/use classification using remotely sensed images is one of the most common applications in remote sensing, and many algorithms have been developed and applied for this purpose in the literature. Support vector machines (SVMs) are a group of supervised classification algorithms that have been recently used in the remote sensing field. The classification accuracy produced by SVMs may show variation depending on the choice of the kernel function and its parameters. In this study, SVMs were used for land cover classification of Gebze district of Turkey using Landsat ETM+ and Terra ASTER images. Polynomial and radial basis kernel functions with their estimated optimum parameters were applied for the classification of the data sets and the results were analyzed thoroughly. Results showed that SVMs, especially with the use of radial basis function kernel, outperform the maximum likelihood classifier in terms of overall and individual class accuracies. Some important findings were also obtained concerning the changes in land use/cover in the study area. This study verifies the effectiveness and robustness of SVMs in the classification of remotely sensed images.     

高光谱遥感影像管理系统的设计与实现

由于高光谱数据的海量高维特征,使得传统的信息系统难以有效地对这些数据进行高效地存储、处理、分析,表现等管理操作。因此如何采用新的技术来开发一个能有效管理高光谱遥感影像数据的影像管理系统,是当前高光谱快速发展和深入应用的一个瓶颈之一。本文在研究的过程中,对当前影像管理系统的发展作了大量的分析,利用成熟的关系数据库和程序设计语言,开发了一个方便实用的高光谱遥感影像管理系统以管理高光遥感影像和其他遥感信息,提高影像管理效率。系统已初步实现了多景高光谱遥感影像检索,可以任意加载、导入高光谱遥感影像,运行效果良好。     

Psychological factors in remote sensing: A review of some recent research

We review recent psychological research that pertains to remote sensing, with a focus on two major problem areas: The problem of eliciting and characterizing the knowledge of expert interpreters of remotely‐sensed data, and the problem of how to best utilize color in remote sensing displays. This research has immediate relevance to many remote sensing activities, but it also has broader implications. One general implication is that experimental psychology offers concepts and research methods that can be of benefit to the field of remote sensing. It is possible to discover the principles involved in the design of “good”; remote sensing displays, and the design of methods for teaching novices how to interpret remote sensing displays. A second broad implication of the psychological research is that remote sensing, as traditionally conceived, is actually just one part of a larger process of “remote perceiving.”; This idea is pursued in a second article (Hoffman, 1990), in which an attempt is made to characterize remote sensing as a unified science.     

A New Approach to Improve the Cluster-based Parallel Processing Efficiency of High-Resolution Remotely Sensed Image

Object-oriented remotely sensed images processing method has been accepted by more and more experts of remote sensing. To advance the efficiency of data processing, parallel image computing is a good choice since large volumes of data need be analyzed efficiently and rapidly. This paper presents the information extraction method based on per-parcel extraction of high-resolution remotely sensed image; to extract efficiently different information from remotely sensed image, this paper gives the research idea of image rough-classification based on large-scale and subtle-segmentation based on small-scale; to improve the efficiency of image processing, we adapt parallel computing method to solve this problem by presenting an new data-partition method. At last this paper gives the implementation of the research idea based on Message Passing Interface (MPI) and analyzes our experimental system efficiency, and the results show that the new methods can improve the efficiency of high-resolution remotely sensed image data processing efficiently and have a good application.     

Change detection from remotely sensed images: From pixel-based to object-based approaches

The appetite for up-to-date information about earth’s surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection.     

对遥感科学应用的一点看法

本文提出遥感科学发展至今,需要进一步拓展和深化应用的一点思考。遥感已经无处不在,遥感学者不能停留在对传统的航空和卫星遥感数据的处理和分析,应该把现在无处不在的地面摄像、录音甚至是味觉传感数据以及其他社会经济大数据应用起来。遥感学者走出自己的熟悉领域实现跨学科发展,才能取得更大的科学突破。最后对遥感在实现联合国可持续发展目标中的作用进行展望,以期对年轻学者有一定帮助。     

动态综合层次判别分析方法──以水土流失强度综合分析为例

随着遥感技术的发展,可获取的遥感信息越来越丰富,各种分类算法也层出不穷,然而在分类精度上却始终无法突破遥感数据本身的局限,而且不同的方法在判识的结果上往往有很大的不同。本文认为不同遥感信息、不同的判别分析方法,对不同的对象在不同的地表组合条件下其判别的能力是不同的,只有综合地运用这些不同的方法才可能在现有遥感资料的能力下提高分类精度。本文提出一种动态综合层次判别分析算法可将多种并行的分类算法综合在一起,取长补短,提高最后分类精度。通过在浙江省新昌县某地进行水土流失强度分析试验,取得了良好的效果。     

遥感大数据时代与智能信息提取

近年来,天地一体化对地观测系统与智能计算技术的快速发展为遥感科技进步甚至变革提供了难得的机遇。遥感信息技术在历经20世纪60~80年代以统计数学模型为核心的数字信号处理时代、从90年代至今以遥感信息物理量化为标志的定量遥感时代之后,现在正逐渐进入一个以数据模型驱动、大数据智能分析为特征的遥感大数据时代。在总结遥感信息技术历史发展脉络的基础上,阐述了遥感大数据的内涵和智能信息提取的时代特点,并从遥感大数据的理念出发,论述了面向对象的遥感知识库构建,分析了融合遥感知识和深度学习算法的大数据智能信息提取策略。通过典型实例,介绍了以深度学习为代表的智能算法在遥感大数据目标检测、精细分类、参数反演等方面的发展现状与趋势,并讨论了深度学习在遥感大数据时代的智能信息提取方面的应用潜力。     

龙羊峡库区环境动态监测信息系统的建立与应用

以黄河上游最大的水利工程－－龙羊峡水库为实验区,围绕库区存在的主要环境问题的研究,应用遥感与ＧＩＳ一体化的方法,研究设计了系统结构、功能及数据库的组成,并建立了可运行的龙羊峡库区环境动态监测信息系统,在系统支持下有效的实现了库区环境动态监测与应用,由于该系统较强的可操作性和适应性,其成果在高寒干旱与半干旱地区环境研究中具有推广和应用价值。     

GIS辅助下的基于知识的遥感影像分类方法研究——以土地覆盖/土地利用类型为例

提高计算机遥感影像的分类精度，是遥感应用中研究的主要问题之一。作者以规则的形式表示遥感影像解译知识，使用ＴＭ影像数据和ＤＥＭ、坡度、土地利用图等地理辅助数据，从遥感影像处理／地理数据／专家知识一体化的角度出发，使用基于知识的方法进行了研究，改善了分类精度。实例研究证明了方法的正确性。     

基于整数小波变换的多光谱图像无损压缩

多光谱图像一般都采用预测方法去除空间冗余和谱内冗余实现无损压缩。通过用提升方法构造整数小波变换, 将变换方法用于去除空间冗余；通过分类方法构造谱间预测器，用预测方法去除谱间冗余，两者相结合，实现无损压缩。由于变换方法的去相关性能良好，使该方法压缩效果大大改善。     

遥感信息处理不确定性的可视化表达

如何全面、准确地度量和可视化表达遥感信息处理中不确定性的程度和空间分布方式,是遥感信息不确定性研究的关键问题之一.传统的度量方法(例如误差矩阵)是将以训练样本集为基础的度量作为总分类精度的度量,而我们需要估计模型对于"样本外数据"的性能.本文首先利用信息论和粗糙集理论等度量遥感分类影像属性信息的不确定性,提出基于像元、目标和影像的遥感信息不确定性度量指标;然后分别描述了基于不同度量指标的可视化表达方式,并对我国黄河三角洲地区的Landsat TM影像进行了分类信息不确定性度量和可视化表达实验.