Robust adaptive radar beamforming based on iterative training sample selection using kurtosis of generalized inner product statistics

In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training samples used to calculate the weight vector does not contain the jamming,then the jamming cannot be removed by adaptive spatial filtering.If the weight vector is constantly updated in the range dimension,the training data may contain target echo signals,resulting in signal cancellation effect.To cope with the situation that the training samples are contaminated by target signal,an iterative training sample selection method based on non-homogeneous detector(NHD)is proposed in this paper for updating the weight vector in entire range dimension.The principle is presented,and the validity is proven by simulation results.

Method to generate training samples for neural network used in target recognition

Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.

The Single Training Sample Extraction of Visual Evoked Potentials Based on Wavelet Transform

Based on the good localization characteristic of the wavelet transform both in time and frequency domain, a de-noising method based on wavelet transform is presented, which can make the extraction of visual evoked potentials in single training sample from the EEG background noise in favor of studying the changes between the single sample response happen. The information is probably related with the different function, appearance and pathologies of the brain. At the same time this method can also be used to remove those signal’s artifacts that do not appear with EP within the same scope of time or frequency. The traditional Fourier filter can hardly attain the similar result. This method is different from other wavelet de-noising methods in which different criteria are employed in choosing wavelet coefficient. It has a biggest virtue of noting the differences among the single training sample and making use of the characteristics of high time frequency resolution to reduce the effect of interference factors to a maximum extent within the time scope that EP appear. The experiment result proves that this method is not restricted by the signal-to-noise ratio of evoked potential and electroencephalograph (EEG) and even can recognize instantaneous event under the condition of lower signal-to-noise ratio, as well as recognize the samples which evoked evident response more easily. Therefore, more evident average evoked response could be achieved by de-nosing the signals obtained through averaging out the samples that can evoke evident responses than de-nosing the average of original signals. In addition, averaging methodology can dramatically reduce the number of record samples needed, thus avoiding the effect of behavior change during the recording process. This methodology pays attention to the differences among single training sample and also accomplishes the extraction of visual evoked potentials from single trainings sample. As a result, system speed and accuracy could be improved to a great extent if this methodology is applied to brain-computer interface system based on evoked responses.

Tracking the historical urban development by classifying Landsat MSS data with training samples migrated across time and space

To reveal the historical urban development in large areas using satellite data such as Landsat MSS still need to overcome many challenges.One of them is the need for high-quality training samples.This study tested the feasibility of migrating training samples collected from Landsat MSS data across time and space.We migrated training samples collected for Washington,D.C.in 1979 to classify the city’s land covers in 1982 and 1984.The classifier trained with Washington,D.C.’s samples were used in classifying Boston’s and Tokyo’s land covers.The results showed that the overall accuracies achieved using migrated samples in 1982(66.67%)and 1984(65.67%)for Washington,D.C.were comparable to that of 1979(68.67%)using a random forest classifier.Migration of training samples between cities in the same urban ecoregion,i.e.Washington,D.C.,and Boston,achieved higher overall accuracy(59.33%)than cities in the different ecoregions(Tokyo,50.33%).We concluded that migrating training samples across time and space in the same urban ecoregion are feasible.Ourfindings can contribute to using Landsat MSS data to reveal the historical urbanization pattern on a global scale.

Integrated assessment of sea water quality based on BP artificial neural network

In order to carry out an integrated assessment of sea water quality objectively, this paper based on the concept and principle of artificial neural network, generated appropriate training samples for BP artificial neural network model through the method of producing samples to the concentration of various pollution index of sea water quality from the viewpoint of threshold, established the BP artificial neural network model of sea water quality assessment using multi-layer neural network with error back-propagation algorithm. This model was used to assess water environment and obtain sea water quality categories of offshore area in Bohai Bay through calculating. The calculations shown that pollution index in river＇s wet season was higher than that in dry season from 2004 to 2007, and the pollution was particularly serious in 2005 and 2006, but a little better in 2007. The assessed results of cases shown that the model was reasonable in design and higher in generalization, meanwhile, it was common, objective and practical to sea water quality assessment.

A fully automatic and high-accuracy surface water mapping framework on Google Earth Engine using Landsat time-series

Efficient and continuous monitoring of surface water is essential for water resource management.Much effort has been devoted to the task of water mapping based on remote sensing images.However,few studies have fully considered the diverse spectral properties of water for the collection of reference samples in an automatic manner.Moreover,water area statistics are sensitive to the satellite image observation quality.This study aims to develop a fully automatic surface water mapping framework based on Google Earth Engine(GEE)with a supervised random forest classifier.A robust scheme was built to automatically construct training samples by merging the information from multi-source water occurrence products.The samples for permanent and seasonal water were mapped and collected separately,so that the supplement of seasonal samples can increase the spectral diversity of the sample space.To reduce the uncertainty of the derived water occurrences,temporal correction was applied to repair the classification maps with invalid observations.Extensive experiments showed that the proposed method can generate reliable samples and produce good-quality water mapping results.Comparative tests indicated that the proposed method produced water maps with a higher quality than the index-based detection methods,as well as the GSWD and GLAD datasets.

The first all-season sample set for mapping global land cover with Landsat-8 data

We report the world's first all-season training and validation sample sets for global land cover classification with Landsat-8 data.Prior to this,such samples were only available at a single date primarily from the growing season.It is unknown how much limitation such a single-date sample has to mapping global land cover in other seasons of the year.To answer this question,we selected available Landsat-8 images from four seasons and collected training and validation samples from them.We compared the performances of training samples in different seasons using Random Forest algorithm.We found that the use of training samples from any individual season would result in the best overall classification accuracy when validated by samples in the same season.The global overall accuracy from combined best seasonal results was 67.2% when classifying the 11 Level-1 classes in the Finer Resolution Observation and Monitoring of Global Land Cover(FROM-GLC) classification system.The use of training samples from all seasons(named all-season training sample set hereafter) produced an overall accuracy of 67.0%.We also tested classification within 10° latitude 60° longitude zones using all-season training subsample within each zone and obtained an overall accuracy of 70.2%.This indicates that properly grouped subsamples in space can help improve classification accuracies.All the results in this study seem to suggest that it is possible to use an all-season training sample set to reach global optimality with universal applicability in classifying images acquired at any time of a year for global land cover mapping.