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 invention belongs to the field of radar signal processing, and discloses a multi-baseline phase unwrapping method based on a plane approximation model. The method comprises steps: an interferencephase diagram is acquired; the wrapping phase gradient of the interference phase diagram is calculated; fuzzy number gradient estimation based on plane approximation is then carried out; a fuzzy number is solved through an MCF model; the absolute phase is calculated according to the fuzzy number and the wrapping phase; and a phase unwrapping result is obtained. Thus, the problem of poor noise robustness for multi-baseline phase unwrapping under noise interference can be solved.

Multi-baseline phase unwrapping method based on plane approximation model

A synthetic aperture radar (SAR) target detection method based on the fusion of multiscale superpixel segmentations is proposed in this paper SAR images are segmented between land and sea firstly by using superpixel technology in different scales Secondly, image segmentation results together with the constant false alarm rate (CFAR) detection result are coalesced Finally, target detection is realized by fusing different scale results The effectiveness of the proposed algorithm is tested on Sentinel-1A data

A Target Detection Method in SAR Images Based on Superpixel Segmentation

We investigate the issue of combating interrupted-sampling repeater jamming (ISRJ). Due to the advantages of miniaturization, lightweight, and flexibility, the ISRJ poses a great menace to radar performance through the fast sampling and forwarding of radar signals. Given this problem, we propose an electronic counter-countermeasure (ECCM) system based on the time–frequency domain. The system mines the information of radar echoes using de-chirping processing and the short-time Fourier transform (STFT). We introduce a binarization algorithm to achieve noise suppression and utilize two different features to guarantee the correct rate of target signal extraction. Simulation experiments show that our system can be effective against ISRJ. Moreover, our system still exhibits good interference suppression performance under the condition of multiple jammers, which effectively enhances the anti-jamming capability of the radar.

Interference Countermeasure System Based on Time–Frequency Domain Characteristics

In this paper, a novel forest disaster detection method based on fuzzy c-means (FCM) algorithm and genetic algorithm (GA) with neighborhood information (F-NGA) is proposed. The proposed method adopts FCM to pre-classify the original data first. Then, the neighborhood factors are added into the GA model to reclassify the results of FCM. Experiment results on two multispectral Formosat-2 forest images present that our algorithm obtains better detection performance when compared with FCM, fuzzy local information c-means (FLICM) and original GA algorithm.

A Novel Forest Disater Monitoring Method Based on FCM and Neighborhood Factor Genetic Algorithm Using Multispectral Data

Spaceborne/Airborne Hybrid Sliding Spotlight Bistatic SAR Range-Doppler Imaging Algorithm: Spaceborne/Airborne Hybrid Sliding Spotlight Bistatic SAR Range-Doppler Imaging Algorithm

Mengdao Xing

Papers

Multi-baseline phase unwrapping method based on plane approximation model

A Target Detection Method in SAR Images Based on Superpixel Segmentation

Interference Countermeasure System Based on Time–Frequency Domain Characteristics

A Novel Forest Disater Monitoring Method Based on FCM and Neighborhood Factor Genetic Algorithm Using Multispectral Data

Spaceborne/Airborne Hybrid Sliding Spotlight Bistatic SAR Range-Doppler Imaging Algorithm: Spaceborne/Airborne Hybrid Sliding Spotlight Bistatic SAR Range-Doppler Imaging Algorithm