Classification of multispectral remote sensing data using aback-propagation neural network

The suitability of a back-propagation neural network for classification of multispectral image data is explored. A methodology is developed for selection of both training parameters and data sets for the training phase. A new technique is also developed to accelerate the learning phase. To benchmark the network, the results are compared to those obtained using three other algorithms: a statistical contextual technique, a supervised piecewise linear classifier, and an unsupervised multispectral clustering algorithm. All three techniques were applied to simulated and real satellite imagery. Results from the classification of both Monte Carlo simulation and real imagery are summarized     

A statistical self-organizing learning system for remote sensing classification

A new learning system called a statistical self-organizing learning system (SSOLS), combining functional-link neural networks, statistical hypothesis testing, and self-organization of a number of enhancement nodes, is introduced for remote sensing applications. Its structure consists of two stages, a mapping stage and a learning stage. The input training vectors are initially mapped to the enhancement vectors in the mapping stage by multiplying with a random matrix, followed by pointwise nonlinear transformations. Starting with only one enhancement node, the enhancement layer incrementally adds an extra node in each iteration. The optimum dimension of the enhancement layer is determined by using an efficient leave-one-out cross-validation method. In this way, the number of enhancement nodes is also learned automatically. A t-test algorithm can also be applied to the mapping stage to mitigate the effect of overfitting and to further reduce the number of enhancement nodes required, resulting in a more compact network. In the learning stage, both the input vectors and the enhancement vectors are fed into a least squares learning module to obtain the estimated output vectors. This is made possible by choosing the output layer linear. In addition, several SSOLSs can be trained independently in parallel to form a consensual SSOLS, whose final output is a linear combination of the outputs of each SSOLS module. The SSOLS is simple, fast to compute, and suitable for remote sensing applications, especially with hyperspectral image data of high dimensionality.     

遥感图像分类中的遗传算法LVQ神经网络运用

对于遥感图像分类过程中的问题,提出遗传算法LVQ神经网络来实现遥感图像的分类。将LVQ神经网络结合遗传算法,使用遗传算法最优阈值与权值实现网络训练,使分类精度得到提高。之后融合相似灰度值创建分类图像特征矢量,使特征矢量在神经网络中输入实现训练。学习矢量量化神经算法对初值非常敏感,对遥感图像分类精度具有一定影响。最后,为了对性能进行测试,在实验过程中对比本文分类方法和SVM决策树分类方法,通过实验结果表示,文中提出的分类方法的遥感图像分类精度为95.82%,与其他分类方法相比,分类精度得到进一步提高。     

基于卷积神经网络的卫星遥感图像区域识别

为了提高卫星遥感图像的识别与分类效果,提出一种基于卷积神经网络的卫星遥感图像识别与分类方法。该方法通过导向滤波去雾和旋转图像数据提高了模型的泛化能力,同时采用了双全连接层网络结构增强了模型数据表达能力。实验证明,该方法在卫星遥感图像的识别与分类上优于传统图像识别方法和一般卷积神经网络模型。     

Designing optimal spectral indexes for remote sensing applications

Satellite remote sensing data constitute a significant potential source of information on our environment, provided they can be adequately interpreted. Vegetation indexes, a subset of the class of spectral indexes, represent one of the most commonly used approaches to analyze data in the optical domain. An optimal spectral index is very sensitive to the desired information (e.g. the amount of vegetation), and as insensitive as possible to perturbing factors (such as soil color changes or atmospheric effects). Since both the desired signal and the perturbing factors vary spectrally, and since the instruments themselves only provide data for particular spectral bands, optimal indexes should be designed for specific applications and particular instruments. This paper describes a rational approach to the design of an optimal index to estimate vegetation properties on the basis of the red and near-infrared reflectances of the AVHRR instrument, taking into account the perturbing effects of soil brightness changes, atmospheric absorption and scattering. The rationale behind the Global Environment Monitoring index (GEMI) is explained, and this index is proposed as an alternative to the Normalized Difference Vegetation Index (NDVI) for global applications. The techniques described here are generally applicable to any multispectral sensor and application     

BP神经网络学习算法的改进及其应用

针对标准BP算法的不足给出了典型的改进算法。对两个BP网络的应用实例利用MAT LAB语言编制了仿真程序 ,并对几种算法的学习收敛速度进行了比较。结果表明改进算法的学习收敛速度大大地优于标准BP算法。     

A dynamic acceleration method for remote sensing image processing based on CUDA

Wireless Networks - The incredible increase in the volume of remote sensing data has made the concept of Remote Sensing as Big Data reality with recent technological developments. Remote sensing...     

Handwritten digit recognition: applications of neural network chipsand automatic learning

Two novel methods for achieving handwritten digit recognition are described. The first method is based on a neural network chip that performs line thinning and feature extraction using local template matching. The second method is implemented on a digital signal processor and makes extensive use of constrained automatic learning. Experimental results obtained using isolated handwritten digits taken from postal zip codes, a rather difficult data set, are reported and discussed     

Meteorological applications of remote sensing from satellites

Satellites provide meteorologists with a data source unmatched at comparable spatial and temporal coverage by any existing or practical alternate source. There are limitations, however, both instrumental and fundamental, imposed on the achievable resolution and accuracy. Current and promising future contributions to meteorology from satellite-borne sensors are discussed, with emphasis on performance and the limitations thereto. The discussion covers 1) synoptic meteorology where satellite observations of clouds provide measures of winds, cyclogenesis, and rainfall estimation; 2) atmospheric profiling wherein vertical profiles of temperature, humidity, and certain gaseous constituents are provided; 3) radiation budget or the energy exchange between the earth and the space-sun environment; and 4) surface features of importance to meteorology-temperature, soil moisture, and sea ice coverage. Satellites will be extensively used as data collectors and relays from in situ instruments on buoys, balloons, and fixed earth sites. The accuracy and coverage of such observations, however, will be determined by the in situ sensors and not by the satellite. They are therefore not discussed here.     

The military applications of remote sensing by infrared

Remote sensing is the process of acquiring information from the environment by the use of a sensor that is not in physical contact with the object under study. The military services are experienced practitioners of this old, but newly glamorous, art. Their accomplishments in the infrared, that region lying between visible light on the one hand and microwaves on the other, are both impressive and of increasing importance. Our purpose is to provide an overview of these accomplishments. We begin with a brief treatment of the characteristics and peculiarities of the infrared portion of the spectrum and of the sensors that operate there. Early military experience with remote sensing by infrared is described and an applications matrix is developed in order to provide a perspective from which the reader can view the full panorama of military applications. Specific applications ate discussed. These include strategic systems for early warning of intercontinental ballistic missile launches, methods for the detection of atmospheric contaminants, such as poison gas, under field conditions, aids for the precision delivery of weaponry (including passive, active, and laser designator guidance techniques), and sensor systems for reconnaissance and surveillance. Wherever possible, details of sensor performance are given.     

Training techniques for neural network applications in ATM

The main problems of adaptive ATM quality of service (QoS) control methods using neural networks were the exponentially wide range of the output target and the real-time training data sampling. But new practical techniques to overcome these problems may open new neural network applications. In this article, the framework of connection admission control (CAC) is described as a typical example of neural-network-based QoS estimation and two practical techniques, called relative target method and virtual output buffer method, are presented to enhance the neural network performance in CAC     

Content-based network resource allocation for real time remote laboratory applications

This paper presents a practical solution to make remote laboratories a realizable dream. A remote laboratory is an online laboratory where students can get first-hand experience of engineering labs via Internet. Video transmission can provide hands on experience to the user but the transmission channel or networks typically have variable and low bandwidth that poses a tough constraint for such implementation. This work presents a practical solution to such problems by adaptively transmitting the best available quality of laboratory videos to the user depending on network bandwidth. The concept behind our work is that not all objects or frames of the video have equal importance, and thus bandwidth reduction can be accomplished by intelligently transmitting important parts at relatively higher resolution. A localized Time adaptive mean of Gaussian (L-TAMOG) approach is used to search for moving objects which are then allocated network resources dynamically according to the varying network bandwidth variations. Adaptive motion compensated wavelet-based encoding is used to achieve scalability and high compression. The proposed system tracks the network bandwidth and delivers optimally the most important contents of video to the student. Experimental results over several remote laboratory sequences show the efficiency of the proposed framework.     

A self-organizing neural network for supervised learning,recognition, and prediction

Fuzzy ARTMAP, one of a rapidly growing family of attentive self-organizing learning, hypothesis testing, and prediction systems that have evolved from the biological theory of cognitive information processing of which ART forms an important part is discussed. It is shown that this architecture is capable of fast but stable online recognition learning, hypothesis testing and adaptive naming in response to an arbitrary stream of analog or binary input patterns. The fuzzy ARTMAP neural network combines a unique set of computational abilities that are needed to function autonomously in a changing world and that alternative models have not yet achieved. In particular, fuzzy ARTMAP can autonomously learn, recognize, and make predictions about rare events, large nonstationary databases, morphologically variable types of events, and many-to-one and one-to-many relationships. The system's fast learning of rare events and error-based learning and alternatives are described, and uses for ART systems and the development of unsupervised ART systems are reviewed     

Practical methods for rapid and accurate computation ofinterferometric spectra for remote sensing applications

The apodization of an interferogram corresponds to a linear transformation in spectral space between unapodized and apodized radiances. Many apodization functions have well-behaved numerical inverse transformations, and we show an analytic inverse for the Hamming apodization function. The inverse transformation has many practical uses for remote sensing applications and can also be used theoretically to show the equivalence between unapodized spectra and properly apodized spectra. The inverse transformation, which is a representation of the discrete convolution theorem, can be used to readily convert computed apodized spectra to spectra computed for other symmetric apodization functions (including unapodized), which may have poorer characteristics with regard to calculating channel-transmittance parameters or radiances. We also show a quantitative method for comparing apodization functions of different mathematical forms     

一种基于深度学习的遥感图像分类及农田识别方法

为解决便于发现我国基本农田被非法侵占的问题,针对现有神经网络收敛速度慢、识别准确率不高的缺点,提出一种基于卷积神经网络的遥感图像农田分类及识别方法。该算法使用较大的卷积核,有效地提取梯度信息;设计深度为6层的卷积神经网络,提高了网络的分类效果,且大大降低了网络的训练次数。实验结果表明,利用该识别模型对农田、建筑、荒漠以及植被的识别准确率达到98.15%,比经典AlexNet网络模型提高了6.1%;训练网络所需的迭代次数由1.49×10~6次左右降低到4 500次。因此,与经典AlexNet网络相比,改进的AlexNet网络用于遥感图像分类和目标图像识别,耗时更短、识别准确率更高。     

A multiwavelength line-of-sight experiment for remote atmospheric sensing

Simultaneous measurements have been made on a 3.57- km line-of-sight atmospheric path using a phase quadrature technique at 10.4 GHz, plus a combination of near-infrared and 23.8-GHz transmissometers operating in oxygen and water vapor absorption bands, respectively. The latter two systems provide the separate dry and wet terms of integrated radio refractivity for use in interpretation of the 10.4-GHz phase data. Direct measurement of the statistics of the phase quadrature components of the incoherent scattered field yields several incoherent field parameters not obtained before in line-of-sight experiments. The usefulness of these parameters for atmospheric characterization is being investigated. On this short path the incoherent field is primarily at phase quadrature to the coherent field, and this component is normally distributed, implying a predominantly near-field situation. The postulated decrease of the ratio of quadrature component variances, and of their cross correlation, with spectral frequency has been verified. The agreement noted between transmissometer data and 10.4-GHz phase data in the near field transfers to agreement with 10.4-GHz amplitude data in the far field. This difference, noted in a comparison of measurement spectra, is a helpful analytical tool in the characterization of the atmospheric turbulence wave number spectrum.     

A process model for remote sensing data analysis

Remote sensing data is collected and analyzed to enhance understanding of the terrestrial surface-in composition, in form, or in function. One approach for accomplishing this is to design the analysis process as an iterated composite of several analyst-directed modules. This paper proposes such a modular design for the data analysis. The proposed methodology was applied in a project to obtain the thematic map for a flightline over Washington, DC, with very satisfactory results-the qualification being in both the visual and the statistical sense. The project execution is presented as a case study in this paper     

光与雷达遥感协作及其农业应用

用遥感影像提取农作物信息是精细农业的基础.传统的光学遥感局限于大气无云的优质影像,而微波遥感在提取农作物信息方面尚未得到充分的利用.寻找一种能够克服这些局限的农作物遥感信息提取的方法是有意义的.探讨了一种光和雷达遥感协作的方法,并应用于土壤和植被信息的提取,验证其应用潜力.用Karam等(1992)的微波散射模型,并作了相应修改:1)考虑粗糙地表面后向散射;2)光学遥感作为模型变量直接输入.将这种协作方法应用到两个数据集的处理,一个来自亚利桑那Maricopa农业中心,一个来自日本国家农业-环境科学协会的Tsukuba试验基地.这些数据包括一些实测的土壤和植被的数据以及陆地卫星Landsat和欧洲遥感卫星ERS遥感影像.结果初步表明雷达影像和光学影像在提取土壤和植被信息时可以有效地结合起来,在特定地方的农业管理中有着较好的应用前景.     

VSWIR to VLWIR MBE grown HgCdTe material and detectors for remote sensing applications

The molecular beam epitaxy (MBE) growth technology is inherently flexible in its ability to change the Hg_1−xCd_xTe material’s bandgap within a growth run and from growth run to growth run. This bandgap engineering flexibility permits tailoring the device architecture to the various specific requirements. Material with active layer x values ranging from ∼0.198 to 0.570 have been grown and processed into detectors. This wide range in x values is perfectly suited for remote sensing applications, specifically the National Polar Orbiting Environmental Satellite System (NPOESS) program that requires imaging in a multitude of infrared spectral bands, ranging from the 1.58 to 1.64 μm VSWIR (very short wave infrared) band to the 11.5 to 12.5 μm LWIR (longwave infrared) band and beyond. These diverse spectral bands require high performance detectors, operating at two temperatures; detectors for the VSWIR band operate near room temperature while the SWIR, MWIR (mid wave infra red), LWIR and VLWIR (very long wave infrared) detectors operate near 100K, because of constraints imposed by the cooler for the NPOESS program. This paper uses material parameters to calculate theoretical detector performance for a range of x values. This theoretical detector performance is compared with median measured detector optical and electrical data. Measured detector optical and electrical data, combined with noise model estimates of ROIC performance are used to calculate signal to noise ratio (SNR), for each spectral band. The SNR are compared with respect to the meteorological NPOESS system derived focal plane. The derived system focal plane requirements for NPOESS are met in all the spectral bands.