Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey (1) template matching-based object detection methods, (2) knowledge-based object detection methods, (3) object-based image analysis (OBIA)-based object detection methods, (4) machine learning-based object detection methods, and (5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.

A survey on object detection in optical remote sensing images

In this paper, we examine the role of polarimetry in synthetic aperture radar (SAR) interferometry. We first propose a general formulation for vector wave interferometry that includes conventional scalar interferometry as a special case. Then, we show how polarimetric basis transformations can be introduced into SAR interferometry and applied to form interferograms between all possible linear combinations of polarization states. This allows us to reveal the strong polarization dependency of the interferometric coherence. We then solve the coherence optimization problem involving maximization of interferometric coherence and formulate a new coherent decomposition for polarimetric SAR interferometry that allows the separation of the effective phase centers of different scattering mechanisms. A simplified stochastic scattering model for an elevated forest canopy is introduced to demonstrate the effectiveness of the proposed algorithms. In this way, we demonstrate the importance of wave polarization for the physical interpretation of SAR interferograms. We investigate the potential of polarimetric SAR interferometry using results from the evaluation of fully polarimetric interferometric shuttle imaging radar (SIR)-C/X-SAR data collected during October 8-9, 1994, over the SE Baikal Lake Selenga delta region of Buriatia, Southeast Siberia, Russia.

Polarimetric SAR interferometry

The frequency hopping (FH) signal has received much research interest due to its low interception probability. In the FH signal localization, the variation of signal frequency introduces error into localization methods involving phase or frequency information. In order to deal with the problem of positioning measurement estimation for unknown FH signal emitters, this article proposes a synthetic aperture passive positioning method. Baseband modulation of received signals is compensated by the time difference method. Then, the de-chirp method is introduced for carrier frequency estimation. The Doppler frequency of each pulse is compensated by a Doppler frequency compensation matrix, and the cost function related to the emitter position is constructed by 2-D focus results of the received signal at all frequencies. The emitter position is obtained through a gird search. Simulation and experimental data show that the proposed method is superior to several existing positioning methods especially when the signal-to-noise ratio (SNR) is low.

Passive Localization for Frequency Hopping Signal Emitter Based on Synthetic Aperture Principle

Pine wilt disease is one of the most dangerous forest diseases. Because of its strong infectivity and harm, it is very important to find out and stop it in time. In this paper, a novel spatial-spectral random forest (SRF) algorithm for pine wilt monitoring is proposed, for solving the problem of small manual detection range, long investigation time, and untimely discovery of the diseased tree. The proposed method organically combines spatial features with spectral information to quickly and efficiently mark the location of diseased trees. In this way, the online monitoring of the target area using the data of the Beijing-2 satellite is realized. This paper analyses the location of diseased trees and provides early warnings for disease-prone trees. The accuracy of the proposed algorithm is 86.66%, by the confusion matrix analysis.

A Novel Spatial-Spectral Random Forest Algorithm for Pine WILT Monitoring

Radar simulator is an effective tool for performance assessment of radar systems by accurately reproducing echo signals from complicated environment. This paper presents a design of fast multi-waveform radar echo generation based on deconvolution method. First, scene information is retrieved from outfield data based on improved conjugate gradient algorithm. Then, the new radar echoes are generated through convolution of new transmitted signal and restored scene information. A fast and area-efficient field programmable gate array realization is provided to meet the real-time requirement of radar echo simulation. Finally, a series of experiments are performed to evaluate the effectiveness of proposed radar simulation instrument.

The design and implementation of a multi-waveform radar echo simulator

Superpixel can maintain the boundary of the target and reduce the influence of speckle noise, which has been widely applied to synthetic aperture radar (SAR) image target detection. But the size of the superpixel has a great impact on the performance of superpixel-based SAR target detection algorithms. To solve this problem, we propose a multilevel ship target detection algorithm based on superpixel segmentation. First, the SAR images are segmented in different levels with different superpixel sizes. Different descriptions of the SAR images are obtained in different levels. Second, we determine the feature of the superpixels in each level. And to enhance the adaptability of the proposed algorithm, we propose an adaptive distance calculation method to select the contrast superpixels in each level. Third, the soft detection results are realized in each level using the fuzzy $C$ -means (FCM) algorithm. Finally, the soft detection results obtained in different levels are fused by a new fusion strategy to achieve the final ship target detection result. The influences caused by different superxiel sizes can be effectively eased by fusion. Experiments in different SAR images have verified the effectiveness of the proposed algorithm in accurately detecting ship targets and insensitivity to the superpixel size.

Mengdao Xing

Papers

Passive Localization for Frequency Hopping Signal Emitter Based on Synthetic Aperture Principle

A Novel Spatial-Spectral Random Forest Algorithm for Pine WILT Monitoring

The design and implementation of a multi-waveform radar echo simulator

Ship Detection in SAR Images Based on Multilevel Superpixel Segmentation and Fuzzy Fusion

Study on LMFS ISAR Imaging Algorithm Based on Coherence Processing: Study on LMFS ISAR Imaging Algorithm Based on Coherence Processing