Abstract:Optical remote sensing generally refers to the use of visible light,near infrared and short wave infrared sensors to detect objects on earth for digital imaging presentations by the specific spectral information.It is the earliest development of remote sensing,and also the most widely used technique for earth observation research and spatial information application.With the rapid development of optical imaging,electronics and space technology,new breakthroughs have been made in high spatial,spectral and temporal resolution remote sensing,which create unprecedented opportunities and bright future for optical remote sensing image processing and application technology.This paper firstly provide a brief review of optical remote sensing on the principles and progresses,and then focus on the characteristics,techniques and methods of optical remote sensing image processing,and introduce the applications of optical remote sensing in ecological environment,natural resources,national defense and so on.Finally several major directions and trends for future optical remote sensing are also forecasted here.

Abstract:Driven by the demand of high resolution remote sensing applications,many optical remote sensing satellites with higher spatial resolution have been launched.But the extraction of the surface information from satellite image is also more and more affected by the worse atmopsheric conditions with the increase of sensor resolution.The atmospheric correction of optical remote sensing images is facing new situation and challenges due to new payload characteristics and upgraded application demands.Therefore a brief summary with simple analysis of the atmospheric correction methods is valuable.This paper introduces the current status and principle of atmospheric correction methods for optical remote sensing.According to the different approaches obtaining atmospheric parameters,these methods are classified into four catalogues,including the image graphical processing,the radiative transfer computing,the atmospheric parameters inversion from own-image,and the synchronous atmospheric corrector methods.At the end of this paper,with consideration of the development of Chinese high resolution satellites,the application status of atmospheric correction and future developments are discussed.

Abstract:Urban remote sensing is a significant field of remote sensing applications.Under the processing of new-type urbanization in China,remote sensing will not only play an irreplaceable role in many areas such as urban ecological construction,land and space development,resource and environment carrying capacity monitoring,but also provide data source for urban planning management.Based on the researches of urban remote sensing,this paper mainly analyzes the development of several important directions of urban remote sensing.Moreover,a framework of urban remote sensing based on geographical perspective is constructed.Combined with typical examples,aspects including structure and pattern,elements and interactions,change and processes,functions and responses are further explored to demonstrate the development of urban remote sensing research.Finally,combined with the national demands and technological development,the future development of urban remote sensing is prospected from aspects of data sources,research objects,application topics,research objectives and technical methods.

Abstract:Crop pests and diseases have caused serious crop yield loss all over the world.Therefore,the establishment of un-destructive and effective method for monitoring and forecasting of crop pests and diseases at large scale is of great importance for crop management.In recent years,crop pests and diseases forecasting information is available and integrated with multi-source datasets (Earth Observation (EO),meteorological,biopesticide and crop control) to make foundation for sustainable management of pests and diseases.This paper summarizes domestic and overseas research progresses on remote sensing systems,monitoring methods,features and algorithms.Approaches for the dynamic remote sensing monitoring of pest and disease environment and development are investigated with multi-source EO data (hyperspectral,high spatial and high temporal satellite images).This paper introduces the progress for crop pests and diseases monitoring and forecasting mechanism,models,methods and applications at leaf,canopy and regional scale.It explores their system outputs to provide information for pest and disease management with new biopesticides and automatic Unmanned Aerial Vehicle (UAV) spraying system to produce an estimation of risk or potential yield losses.The crop pests and diseases remote sensing monitoring and forecasting system should be constructed by integration with EO data,meteorological data and crop control data to produce national pests and diseases maps and scientific reports.Crop pests and diseases monitoring and forecasting by remote sensing will be beneficial for the improvement of regional crop pests and diseases forecasting and sustainable management to guarantee food security and promote agricultural modernization.

Abstract:Geological remote sensing is one of the most important application fields that can reflect and exploit the characteristics and advantages of spectral remote sensing technology.In this paper,the latest research progress of spectrometry geological remote sensing,including spectral simulation and characteristics analysis for minerals and rocks,technical parameter optimization design of spectrometer,radiometric calibration and correction,derivation of surface spectra,information extraction,information product validation and geological application are summarized.On the basis of the above work,the progress,problems and development of the spectrometry geological remote sensing are discussed.

Abstract:Landmark-Isometric mapping (L-ISOMAP),as a dimensionality reduction method,has great potential in hyperspectral imagery visualization.There are two problems in L-ISOMAP algorithm,i.e.,the computational cost is high and the landmarks lack of representation for hyperspectral imagery.In this case,an improved L-ISOMAP algorithm,named KL-ISOMAP,is proposed based on K-medoids clustering algorithm.The KL-ISOMAP algorithm consists of the following steps:1)Selecting the landmarks by the improved K-medoids algorithm;2) Removing the similar pixels according to the similarity;3) Implementing the non-linear dimensionality reduction of the rest pixels;4) Implementing visualization on the reduced dataset.Experimental results show that KL-ISOMAP algorithm can improve the intrinsic structure representation of the landmarks and therefore improve the visualization performance.Furthermore,the algorithm can be speeded up by setting the similarity threshold.The visualization method is reasonable,feasible and of good visual effect,and has good performance in terms of feature distance and class separability preserving for hyperspectral imagery.

Abstract:Since the spatial distribution of surface materials is usually regular and locally continuous,it is reasonable to classify the hyperspectral images (HSI) from superpixel viewpoint,which can be considered as a process of segmenting the spatial image into many regions.In this paper,a superpixel-level Gabor feature fusion approach (abbreviated as SPGF) has been proposed for hyperspectral image classification.Firstly,a set of predefined two-dimensional (2D) Gabor filters are applied to hyperspectral images to extract sufficient features.Meanwhile,a classic superpixel segmentation method,called simple linear iterative clustering (SLIC),is adopted to divide the original hyperspectral image into disjoint superpixels.Secondly,the Support Vector Machine classifier (SVM) is applied on each extracted 2D Gabor feature cube,and the majority voting strategy is adopted to combine the classification results.Finally,the superpixel map obtained by SLIC is used to regularize the classification map.Extensive experiments on two real hyperspectral data sets have demonstrated higher performance of the proposed SPGF approach over several state-of-the-art methods in the literature.

Abstract:In recent years,swarm intelligence algorithms have made important progress and remarkable achievements in spectral unmixing of hyperspectral image by solving combinatorial optimization or continuous optimization problems.In this paper,the background of the research of spectral unmixing in hyperspectral image and the characteristics of swarm intelligence algorithm were reviewed firstly,and then the optimization model and the spectral mixture model were teased out correspondingly.Then the endmember extraction and abundance inversion method based on swarm intelligent algorithms were introduced.Finally the accuracy of spectral unmixing achieved by swarm intelligence algorithms and other traditional algorithms was evaluated through two experiments.In addition,the advantages and problems of swarm intelligence algorithm in hyperspectral image information extraction were also summarized in this paper.

Abstract:In this paper,we propose a spatially weighted collaborative sparse unmixing method aiming at fully exploiting the spatial information in the hyperspectral images,in which a collaborative sparse regularizer is used to describe the row sparsity of the abundance,while on the top of the collaborative regularizer,a spatial weighting factor introducing the spatial correlations is incorporated.The proposed model is optimized by the well known alternating direction method of multiplier.Our experimental results,conducted using both simulated and real hyperspectral data sets,illustrate the good potential of the proposed algorithm which can greatly improve the abundance estimation results when compared with other advanced sparse unmixing methods.

Abstract:Point cloud classification is a critical step in the processing of LiDAR data,and exploring new automatic,efficient,high accuracy classification method is of great importance.This paper proposed a new method for point cloud classification by analyzing the feature of optical image and LiDAR data from the same aircraft.First,a TIN model was made by interpolating the LiDAR data which was projection transformed,then the registration fusion of LiDAR and optical image was achieved according to the correspondence vertexes of the two data,and the RGB attribution information from optical image was combined into LiDAR data later.Second,classification feature set was built by extracting the spectral features from optical image and multi-scale geometric features from LiDAR data.Third,a CFS feature selection method was used to reduce dimension of the classification set.Finally,a supervised classification was conducted using a random forest algorithm to classify the point cloud.Results indicate that,the overall accuracy and Kappa coefficient of the proposed method is 89.5% and 0.844,respectively.And the proposed method get an improvement in the overall accuracy by 1.1,5.4 and 14.9 percentage point when compared with no feature selection strategy,only using LiDAR data and only using optical image,respectively.The proposed method not only efficiently reduce the interpolation error when fusion based on point cloud interpolation,but also solve the problem for choosing the optimal scale to extract geometry feature in a certain analytical scale,and the data are able to be processed more efficiently when feature selection is adopted.

Abstract:The significant radiation features acquired by aurora spectrometer include spectral lines at 557.7,630.0 and 636.4 nm emitted from oxygen atoms.And these lines are deeply influenced by energy and flux of high energy electrons which penetrate into ionosphere,by the particle density of middle and upper atmosphere,and so on.This paper presents a model to deduce oxygen radiation characteristics based on physical principles of electron impact reactions which cause aurora.By utilizing spectral information at 557.7 and 630.0 nm of aurora spectral image,this model estimates incident electron energy and flux which are closest to those under the practical imaging environment.Experimental results show that for oxygen radiation lines,the predicted values calculated by the presented model overestimate the true values by 1-1.29 times.

Abstract:In this paper,a conditional random field model based on spectral-spatial prior information (SSPCRF) is proposed to perform the task of change detection for high spatial resolution remote sensing images.The proposed method firstly introduces a saliency based sample selection strategy which considers the spectral-spatial information of observed difference image to improve the accuracy of modeling initial change detection result.Then a pairwise potential with boundary constraint is used to help keep the boundary of changed objects.Finally an inference method based on loopy belief propagation (LBP) algorithm is introduced to perform efficient optimization of the proposed model and get the final change map.The proposed SSPCRF model can greatly improve change detection accuracy while keeping detailed boundary information of changed objects.The proposed method is tested on two high resolution datasets and outperforms the commonly used change detection methods.