DDM sea ice Convolution
61811787
A Residual Convolution Neural Network for Sea Ice Classification with Sentinel 1 SAR Imagery
Sea ice type classification is critically important for sea ice monitoring, and synthetic aperture radar (SAR) has become the main data source for sea ice classification. With a large number of SAR images produced every day, a more intelligent sea ice classification process is urgently needed. In this paper, we constructed a four type sea ice classification dataset using Sentinel 1 SAR images with the reference of Canadian Ice Service's ice charts and designed a residual convolution network for sea ice classification: Sea Ice Residual Convolutional Network (SI Resnet) We further designed a multi model average scoring strategy with the idea of ensemble learning to improve the classification accuracy between closely associated ice types. Based on the experiments, our proposed method outperformed MLP, AlexNet, and traditional SVM methods, reaching the overall accuracy of 94% and Kappa coefficient of 91.9. For the evaluation on regional ice concentration, the values computed from the SI Resnet's classification results are more consistent with ice chart's regional concentration data than those of MLP, AlexNet and SVM. Compared with the manually generated ice chart of CIS, our method can work automatically and provide more detailed ice distribution to a useful reference for ship route planning and sea ice changes monitoring.
2018 IEEE International Conference on Data Mining Workshops (ICDMW)
Wei Song Minghui Li Qi He Dongmei Huang Cristian Perra Antonio Liotta
235190498
Sea Ice Classification of SAR Imagery Based on Convolution Neural Networks
We explore new and existing convolutional neural network (CNN) architectures for sea ice classification using Sentinel 1 (S1) synthetic aperture radar (SAR) data by investigating two key challenges: binary sea ice versus open water classification, and a multi class sea ice type classification. The analysis of sea ice in SAR images is challenging because of the thermal noise effects and ambiguities in the radar backscatter for certain conditions that include the reflection of complex information from sea ice surfaces. We use manually annotated SAR images containing various sea ice types to construct a dataset for our Deep Learning (DL) analysis. To avoid contamination between classes we use a combination of near simultaneous SAR images from S1 and fine resolution cloud free optical data from Sentinel 2 (S2) For the classification, we use data augmentation to adjust for the imbalance of sea ice type classes in the training data. The SAR images are divided into small patches which are processed one at a time. We demonstrate that the combination of data augmentation and training of a proposed modified Visual Geometric Group 16 layer (VGG 16) network, trained from scratch, significantly improves the classification performance, compared to the original VGG 16 model and an ad hoc CNN model. The experimental results show both qualitatively and quantitatively that our models produce accurate classification results.
Remote. Sens.
Salman Khaleghian Habib Ullah Thomas Kramer Nick Hughes Torbjorn Eltoft Andrea Marinoni
211210938
Sensing Sea Ice Based on Doppler Spread Analysis of Spaceborne GNSS R Data
The spaceborne Global Navigation Satellite System Reflectometry (GNSS R) delay Doppler map (DDM) data collected over ocean carry typical feature information about the ocean surface, which may be covered by open water, mixed water/ice, complete ice, etc. A new method based on Doppler spread analysis is proposed to remotely sense sea ice using the spaceborne GNSS R data collected over the Northern and Southern Hemispheres. In order to extract useful information from DDM, three delay waveforms are defined and utilized. The delay waveform without Doppler shift is defined as central delay waveform (CDW) while the integration of delay waveforms of 20 different Doppler shift values is defined as integrated delay waveform (IDW) The differential waveform between normalized CDW (NCDW) and normalized IDW (NIDW) is defined as differential delay waveform (DDW) which is a new observable used to describe the difference between NCDW and NIDW, which have different Doppler spread characteristics. The difference is mainly caused by the roughness of reflected surface. First, a new data quality control method is proposed based on the standard deviation and root mean square error (RMSE) of the first 48 bins of DDW. Then, several different observables derived from NCDW, NIDW, and DDW are applied to distinguish sea ice from water based on their probability density function. Through validating against sea ice edge data from the Ocean and Sea Ice Satellite Application Facility, the trailing edge waveform summation of DDW achieves the best results, and its probabilities of successful detection are 98.22% and 96.65% respectively, in the Northern and Southern Hemispheres.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Yongchao Zhu Jens Wickert Tingye Tao Kegen Yu Zhenxuan Li Xiaochuan Qu Zhourun Ye Jun Geng Jingui Zou Maximilian Semmling
209985465
Sea Ice Transition Detection Using Incoherent Integration and Deconvolution
Currently, reflectometry based sea ice detection methods rely on observables extracted from delay Doppler maps (DDM)s, which are sensitive to ice in the specular zone. Due to the size of the glistening zone, the transition from open seas to sea ice in the specular zone can take up to 10 s for satellite platforms and no methods exist that can process this. In this article, using DDMs collected by TechDemoSat 1, we demonstrate that this transition is comprised of a response that is fixed in the spatial domain, at the ice edge, and moving in the delay Doppler domain. This is the first observation of persistent nonspecular coherent reflections from sea ice. The delay Doppler trajectory of the ice response is shown to correspond with a point that is located on the ambiguity free line. Furthermore, the response is point like as it suffers from delay and Doppler walk suggesting that it originates from a small spatial footprint, i.e. the first (few) Fresnel zone(s) Exploiting these facts, we then propose a technique that integrates the ice response in the spatial domain after preprocessing. This results in the edges of the ice sheet being emphasized as all of the power received during the transition phase maps to the edge of the sheet. We also propose to compensate for the delay Doppler walk during preprocessing by modifying Woodward's ambiguity function when deconvolution is performed.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Benjamin J Southwell Andrew Graham Dempster
174804723
Detecting Sea Ice From TechDemoSat 1 Data Using Support Vector Machines With Feature Selection
In this paper, a framework that employs support vector machines (SVMs) is proposed for the classification of TechDemoSat 1 delay Doppler maps (DDMs) specifically, for separating DDMs of sea ice from those of seawater. DDM data were first operated with a general data preprocessing procedure, which included noise floor subtraction and normalization. In addition, a simple and effective feature selection (FS) was devised so that the input size was significantly reduced (from 128 x 20 to 20) while the classification accuracy was enhanced. To be specific, the feature was selected as the mean value along the delay axis (128 in length) at each Doppler bin (20 in all) Here, expected classification labels (sea ice/seawater) were obtained based on reference sea ice concentration data collected by multiple passive microwave sensors. In practice, this trained SVMs FS algorithm showed improved accuracy but less data storage demands than the existing neural networks (NNs) and convolutional neural networks based methods. In addition, the designed FS was also proven to be effective for both SVMs and NNs.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Qingyun Yan Weimin Huang
202689034
Sea Ice Concentration Estimation From TechDemoSat 1 Data Using Support Vector Regression
In this paper, a framework that employs support vector regression (SVR) is proposed for estimating sea ice concentration (SIC) from TechDemoSat l (TDS l) delay Doppler maps (DDMs) After a general DDM data preprocessing procedure, which includes noise floor subtraction and normalization, a simple and effective feature was extracted from DDMs. Specifically, the feature is selected as the mean value (incoherent averaging) of 128 delay bins at each Doppler pixel (20 in total) After training this proposed SVR algorithm using reference SIC data obtained from multiple passive microwave sensors, test results showed distinct improvement over those of the existing neural networks and convolutional neural networks based methods. Furthermore, the input size was significantly reduced (from $128\times 20$ to 20) by using this proposed scheme.
2019 IEEE Radar Conference (RadarConf)
Qingyun Yan Weimin Huang
226810704
Sea ice remote sensing using spaceborne global navigation satellite system reflectometry
In this research, the application of spaceborne Global Navigation Satellite System Reflectometry (GNSS R) delay Doppler maps (DDMs) for sea ice remote sensing is investigated. Firstly, a scheme is presented for detecting sea ice from TechDemoSat 1 (TDS 1) DDMs. Less spreading along delay and Doppler axes is observed in the DDMs of sea ice relative to those of seawater. This enables us to distinguish sea ice from seawater through studying the values of various DDM observables, which describe the extent of DDM spreading. Secondly, three machine learning based methods, specifically neural networks (NNs) convolutional neural networks (CNNs) and support vector machine (SVM) are developed for detecting sea ice and retrieving sea ice concentration (SIC) from TDS 1 data. For these three methods, the architectures with different outputs (i.e. category labels and SIC values) are separately devised for sea ice detection (classification problem) and SIC retrieval (regression problem) purposes. In the training phase, different designs of input that include the cropped DDM (40 by 20) the full size DDM (128 by 20) and the feature selection (FS) (1 by 20) are tested. The SIC data obtained by Nimbus 7 SMMR and DMSP SSM/I SSMIS sensors are used as the target data, which are also regarded as ground truth data in this work. In the experimental stage, CNN output resulted from inputting full size DDM data shows better accuracy than that of the NN based method. Besides, performance of both CNNs and NNs is enhanced with the cropped DDMs. It is found that when DDM data are adequately preprocessed CNNs and NNs share similar accuracy. Further comparison is made between NN and SVM with FS. The SVM algorithm demonstrates improved accuracy compared with the NN method. In addition, the designed FS is proven to be effective for both SVM and NN based approaches. Lastly, a reflectivity
Qingyun Yan
52895427
Sea Ice Sensing From GNSS R Data Using Convolutional Neural Networks
In this letter, a scheme that uses convolutional neural networks (CNNs) is proposed for sea ice detection and sea ice concentration (SIC) prediction from TechDemoSat 1 Global Navigation Satellite System Reflectometry delay Doppler maps (DDMs) Specifically, a classification orientated CNN was designed for sea ice detection and a regression based one for SIC estimation. Here, DDM images were used as input, and SIC data from Nimbus 7 Scanning Multi Channel Microwave Radiometer and Defense Meteorological Satellite Program Special Sensor Microwave Imager Special Sensor Microwave Imager/Sounder sensors were modified as targeted output. In the experimental phase, the CNN output resulted from inputting full size DDM data (128 by 20 pixels) showed better accuracy than that of the existing NN based method. Besides, both CNNs and NNs with further processed input data (40 by 20 pixels, and with a fixed position in each image) were evaluated and the performance of both networks was enhanced. It was found that when DDM data are adequately preprocessed, CNNs and NNs share similar accuracy; otherwise the former outperforms the latter. Further conclusion was thus drawn that CNNs were more tolerant to the data format changes than NNs.
IEEE Geoscience and Remote Sensing Letters
Qingyun Yan Weimin Huang
216361480
Spaceborne GNSS Reflectometry for Retrieving Sea Ice Concentration Using TDS 1 Data
A geophysical model function (GMF) for sea ice concentration (SIC) retrieval is developed based on the spaceborne Global Navigation Satellite System Reflectometry (GNSS R) data measured by the TechDemoSat 1 (TDS 1) satellite. The spreading characteristics of onboard processed delay Doppler maps (DDMs) change with the surface roughness, which can be related to the SIC. A GNSS R observable termed as differential delay waveform (DDW) generated from DDM is first used in this article to estimate SIC. Collocated SIC data from the Advanced Microwave Scanning Radiometer 2 (AMSR2) are used as the ground truth to develop and evaluate the SIC model based on the right edge waveform summation (REWS) of DDW. All usable TDS 1 data collected from February 2015 to February 2016 are adopted, and data collected over land were excluded. SIC models of the northern and southern hemispheres (SH) are developed, respectively, for avoiding the impact of geometry. In general, the REWS based model can achieve a root mean square error (RMSE) of 11.78% and a bias of 1.67% for the northern hemisphere, and 12.10% and 1.94% for the SH, respectively. This article demonstrates the capabilities of the spaceborne GNSS R in SIC retrieval.
IEEE Geoscience and Remote Sensing Letters
Y Zhu Tingye Tao Jingui Zou Kegen Yu Jens Wickert Maximilian Semmling
53227051
Sea Ice Scene Classification Using Aerial Images in Arctic Based on Transfer Learning
Arctic travel has become a significant way for commercial or scientific researches. Sea ice is always serious threat for Arctic navigation. In this paper, a classification method of aerial images is proposed to recognize different types of sea ice scenes, which can be further used to analyze the threat of each scene in Arctic Ocean. However, due to the diverse types and distribution of sea ice in various scenes, the categories of sea ice scenes are indistinct. So a grouping way is primarily introduced to define different sea ice scenes. These scenes are grouped into six typical categories according to the distribution of sea ice. Then a transfer learning strategy is used to fine tune a pre trained deep convolution neural network model. Finally, using that trained network to classify new sea ice scenes. Experimental results show that an acceptable classification accuracy can be obtained.
IGARSS 2018 2018 IEEE International Geoscience and Remote Sensing Symposium
Yiming Yan Zhichao Tan Nan Su