Geostatistical and statistical classification of sea-ice properties and provinces from SAR data

Recent drastic reductions in the Arctic sea-ice cover have raised an interest in understanding the role of sea ice in the global system as well as pointed out a need to understand the physical processes that lead to such changes. Satellite remote-sensing data provide important information about remote ice areas, and Synthetic Aperture Radar (SAR) data have the advantages of penetration of the omnipresent cloud cover and of high spatial resolution. A challenge addressed in this paper is how to extract information on sea-ice types and sea-ice processes from SAR data. We introduce, validate and apply geostatistical and statistical approaches to automated classification of sea ice from SAR data, to be used as individual tools for mapping sea-ice properties and provinces or in combination. A key concept of the geostatistical classification method is the analysis of spatial surface structures and their anisotropies, more generally, of spatial surface roughness, at variable, intermediate-sized scales. The geostatistical approach utilizes vario parameters extracted from directional vario functions, the parameters can be mapped or combined into feature vectors for classification. The method is flexible with respect to window sizes and parameter types and detects anisotropies. In two applications to RADARSAT and ERS-2 SAR data from the area near Point Barrow, Alaska, it is demonstrated that vario-parameter maps may be utilized to distinguish regions of different sea-ice characteristics in the Beaufort Sea, the Chukchi Sea and in Elson Lagoon. In a third and a fourth case study the analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification. Field measurements and high-resolution aerial observations serve as basis for validation of the geostatistical-statistical classification methods. A combination of supervised classification and vario-parameter mapping yields best results, correctly identifying several sea-ice provinces in the shore-fast ice and the pack ice. Notably, sea ice does not have to be static to be classifiable with respect to spatial structures. In consequence, the geostatistical-statistical classification may be applied to detect changes in ice dynamics, kinematics or environmental changes, such as increased melt ponding, increased snowfall or changes in the equilibrium line

Artificial Neural Networks and Evolutionary Computation in Remote Sensing

Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification

A New Data Processing System for Generating Sea Ice Surface Roughness and Cloud Mask Data Products from the Multi-Angle Imaging SpectroRadiometer (MISR)

This study describes two novel data products derived from Multi-angle Imaging SpectroRadiometer (MISR) imagery: Arctic-wide maps of sea ice roughness and a binary cloud detection algorithm. The sea ice roughness maps were generated using a data processing system that matched MISR pixels with co-located and concurrent lidar-derived roughness measurements from Airborne Topographic Mapper (ATM), calibrated the multi- angle data to values of surface roughness using a K-Nearest Neighbor (KNN) algorithm, and then applied the algorithm to Arctic-wide MISR data for two 16-day periods in April and July 2016. The resulting maps show good agreement with independent ATM roughness data and enable characterization of the roughness of different ice types. The binary cloud detection algorithm was developed using a neural network approach and a training dataset constructed from Top-of-Atmosphere red band values from all MISR’s nine different viewing cameras for the same two months in various regions of the Arctic. The algorithm showed good performance in classifying pixels into cloudy and clear categories in MISR images, with better performance for clear pixels in April 2016 and better performance for cloudy pixels in July 2016. The algorithm also provides a significant advantage over existing MISR cloud mask products SDCM and ASCM in terms of accuracy and spatial resolution, with a resolution of 275 meters. The data products presented here can be used to gain insights into the seasonal and interannual changes in sea ice roughness and cloud cover over the Arctic and to develop and improve more accurate classification algorithms in the field of remote sensing

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

Optimum Feature Selection for Recognizing Objects from Satellite Imagery Using Genetic Algorithm

Object recognition is a research area that aims to associate objects to categories or classes. Usually recognition of object specific geospatial features, as building, tree, mountains, roads, and rivers from high-resolution satellite imagery is a time consuming and expensive problem in the maintenance cycle of a Geographic Information System (GIS). Feature selection is the task of selecting a small subset from original features that can achieve maximum classification accuracy and reduce data dimensionality. This subset of features has some very important benefits like, it reduces computational complexity of learning algorithms, saves time, improve accuracy and the selected features can be insightful for the people involved in problem domain. This makes feature selection as an indispensable task in classification task. In our work, we propose wrapper approach based on Genetic Algorithm (GA) as an optimization algorithm to search the space of all possible subsets related to object geospatial features set for the purpose of recognition. GA is wrapped with three different classifier algorithms namely neural network, k-nearest neighbor and decision tree J48 as subset evaluating mechanism. The GA-ANN, GA-KNN and GA-J48 methods are implemented using the WEKA software on dataset that contains 38 extracted features from satellite images using ENVI software. The proposed wrapper approach incorporated the Correlation Ranking Filter (CRF) for spatial features to remove unimportant features. Results suggest that GA based neural classifiers and using CRF for spatial features are robust and effective in finding optimal subsets of features from large data sets

Computationally efficient vessel classification using shallow neural networks on SAR data

O radar de abertura sintética (SAR) ´e um radar ativo montado em uma plataforma em movimento, que simula um comprimento de antena maior do que o comprimento real da antena física. De forma semelhante ao radar convencional, ondas eletromagnéticas são transmitidas sequencialmente e os ecos são coletados pelo radar. Com o devido processamento de sinal, este tipo de sistema ´e capaz de fornecer imagens de micro-ondas de alta resolução de uma área-alvo desejada, em praticamente todas as condições meteorológicas. Atualmente, os sistemas SAR tem sido amplamente utilizados para a deteção remota possuindo várias aplicações, como observação da superfície terrestre, cartografia e aplicações militares. Dado que ´e independente do clima e pode operar tanto de dia quanto de noite, o SAR pode ser uma fonte mais confiável quando comparado com imagens ´óticas [1]. A deteção e reconhecimento de navios em imagens SAR tornou-se um tópico importante de pesquisa nos últimos anos. Esta tese apresenta um algoritmo computacionalmente eficiente para a classificação de embarcações em imagens de SAR usando Redes Neuronais com um número reduzido de camadas, também conhecidas como shallow neural networks. A utilização de shallow networks para a classificação de embarcações será dividida em duas etapas: extração de características e classificação. A extração de características tem como objetivo reduzir a carga computacional que as deep neural networks causam nos recursos computacionais, extraindo antecipadamente características-chave da imagem SAR. Os baixos requisitos computacionais tornam esta implementação compatível com sistemas a bordo de navios e aplicações em tempo real. A classificação ´e realizada usando uma rede neural com um número reduzido de camadas, que utiliza parâmetros obtidos a partir de algoritmos de extração de características para classificar a embarcação presente na imagem de radar. O processo de extração de características processa dados do conjunto de dados Open SAR ship [2] para obter várias características da embarcação, como comprimento, largura, média, desvio padrão e o número de pontos de dispersão presentes na embarcação.Synthetic aperture radar (SAR) is an active radar that is mounted on a moving platform, simulating a longer antenna length than the physical antenna real length. Similar to a conventional radar, electromagnetic waves are sequentially transmitted and the backscattered echoes are collected by the radar. With the proper signal processing, this kind of system is able to provide high resolution microwave images of a desired target area by synthesising a larger antenna aperture, in virtually all-weather conditions. Nowadays SAR systems have been extensively used for remote sensing. It has various applications such as Earth surface monitoring, charting and militar applications. Since it is weather independent and is able to operate whether it is day or night, SAR can be a more reliable source when compared with optical imagery [1]. Ship detection and recognition in SAR images has become an importante topic in research in recent years. This thesis presents a computationally eficiente algorithm for the classification of vessels in SAR images using Neural Networks (NN) with a reduced number of hidden layers, also called Shallow Neural Networks (SNN). Herein the use of SNN for vessel classification will be divided into two main steps: feature extraction and classification. Feature extraction aims to lessen the burden deep neural networks cause on computational resources by extracting key features beforehand from the SAR image. The low computational requirements make this implementation compatible with onboard vessel systems and real time applications. The classification is implemented using a SNN that uses parameters obtained from feature extraction algorithms to classify the vessel present in the radar image. In this thesis feature extraction processes data from the Open SAR Ship dataset [2] in order to obtain the vessel’s various features, such as ship length, width, mean, standard deviation and the number of scatter points present on the vessel.N/