Adaptive Probability Thresholding in Automated Ice and Open Water Detection From RADARSAT-2 Images

Transcription

1 552 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 15, NO. 4, APRIL 2018 Adaptive Probability Thresholding in Automated Ice and Open Water Detection From RADARSAT-2 Images Alexander S. Komarov, Member, IEEE, and Mark Buehner Abstract In this letter, we introduce adaptive probability thresholding in addition to our previously developed technique for automated detection of ice and open water from RADARSAT-2 ScanSAR dual-polarization HH HV images. Situations where the probability threshold needs to be modified were identified based on the analysis of misclassified ice and water samples when the static probability threshold of 0.95 is applied. We found that with the use of the proposed approach, the fraction of misclassified ice samples decreased from 0.98% to 0.24% and the fraction of misclassified water samples decreased from 0.35% to 0.09% in the most clean verification scenario against Canadian Ice Service Image Analysis pure ice and water data, while the fraction of correctly classified ice and water samples did not decrease appreciably, from 72.2% to 65.4%. The developed approach will be implemented as a part of the data assimilation component of the operational Environment and Climate Change Canada Regional Ice-Ocean Prediction System. Index Terms Ice probability, logistic regression, RADARSAT-2, Regional Ice-Ocean Prediction System (RIOPS), synthetic aperture radar (SAR), wind speed. I. INTRODUCTION THE Regional Ice-Ocean Prediction System (RIOPS) developed at Environment and Climate Change Canada (ECCC) is an operational short-range numerical ice forecasting system covering the entire Arctic domain with an average resolution of 5 km. The assimilation component of the system utilizes multiple satellite data sources in order to produce accurate sea ice analyses and ultimately generate improved short-term forecasts [1]. Currently, SSM/I, SSMIS, AMSR2 passive microwave, and ASCAT observations as well as optical AVHRR data and ice charts manually produced by the Canadian Ice Service (CIS) are utilized. However, passive microwave and scatterometer observations have relatively coarse ( km) resolution, optical data are heavily dependent on solar illumination and clouds, and the manually derived CIS products only cover a relatively small portion of the system s geographical domain. Manuscript received October 24, 2017; revised January 11, 2018 and February 8, 2018; accepted February 10, Date of publication March 2, 2018; date of current version March 23, This work was supported by the Canadian Space Agency RCM Data Utilization and Application Plan Program. (Corresponding author: Alexander S. Komarov.) A. S. Komarov is with the Data Assimilation and Satellite Meteorology Research Section, Environment and Climate Change Canada, Ottawa, ON K1A 0H3, Canada ( alexander.komarov@canada.ca). M. Buehner is with the Data Assimilation and Satellite Meteorology Research Section, Environment and Climate Change Canada, Dorval, QC H9P 1J3 Canada. Color versions of one or more of the figures in this letter are available online at Digital Object Identifier /LGRS X 2018 Crown Copyright Synthetic aperture radar (SAR) spaceborne observations such as those acquired by Canadian RADARSAT-2 have been extensively used for monitoring and studying changes in the Arctic sea ice [2], [3]. Assimilation of automatically retrieved information from SAR in numerical sea ice prediction systems is an emerging application which will potentially improve sea ice analyses and short-term forecasts. Meanwhile, only retrievals with a very high accuracy should be assimilated in order to avoid propagating and magnifying the error within the system. Therefore, achieving very high accuracy of automatically derived retrievals from SAR, yet preserving a high number of the retrievals to create a positive impact within the system is a crucial requirement. The existing image classification methods (see [4] [6]) based on machine learning techniques were trained using a limited number of images (less than 25) over a specific geographical area. Furthermore, available classification techniques do not typically indicate the level of confidence for ice and water retrievals which is important for data assimilation. We have recently introduced a technique for automated detection of ice and open water from RADARSAT-2 ScanSAR dualpolarization HH HV images [7]. We note that unlike existing image classification methods (see [4] [6]), our approach is not aimedatclassifyingeverypixelinasarimage.itrather provides ice or water retrieval only in situations where it is possible with a high level of confidence. Our approach calculates probability of the presence of ice as a function of SAR-derived predictor variables obtained from a given area of 2.05 km 2.05 km. In order to determine if the tested area is ice or open water, a probability threshold with a high value of 0.95 was applied. With this strategy, the fraction of ice samples misclassified as water (further defined as misclassified ice samples) and the fraction of water samples misclassified as ice (further defined as misclassified water samples) calculated for the independent subset were 0.98% and 0.35%, respectively, in the most clean verification scenario against CIS Image Analysis [8] pure ice and water data. Further reduction of misclassified ice and water samples without significant decrease in the total number of retrievals is an important direction which will increase the value of SAR-derived retrievals within RIOPS. The following are the objectives of this letter: 1) to improve the accuracy of ice and water retrievals from RADARSAT-2 ScanSAR dual-polarization HH HV images; 2) to evaluate the improvement through verification against CIS Image Analysis and the U.S. Interactive Multisensor Snow and Ice Mapping System (IMS) products throughout a one-year period.

2 KOMAROV AND BUEHNER: ADAPTIVE PROBABILITY THRESHOLDING 553 II. ICE PROBABILITY MODEL The probability of the presence of ice for a given area of 2.05 km 2.05 km (which is equivalent to pixels at 50-m resolution) in a RADARSAT-2 HH HV ScanSAR image [9] is modeled in a form of logistic regression as a function of the predictor vector x as follows: P(x) = (1 + exp[ f (x)]) 1 (1) where f (x) is the third degree polynomial defined in [7]. The probability of the presence of ice in the training subset was calculated according to the standard Bayes approach by assuming that the prior probabilities for ice and water are equal. We note that 2-D and 3-D versions of the ice probability model (IPM) were introduced in [7]. In the 2-D IPM version, the predictor vector has two components, x ={x 1, x 2 },andin the 3-D IPM version, the predictor vector has three components, x ={x 1, x 2, x 3 }. The predictor vector components are defined as follows: x 1 is the difference between wind speed derived from the HH HV image using the model described in [10] and wind speed from ECCC Global Environmental Multiscale Model regional deterministic forecasts [11]; x 2 is the spatial correlation between HH and HV windows with a size of pixels; and x 3 is the standard deviation of SAR-derived wind speeds within the pixel window. The wind speed derived from SAR entering in x 1 is defined as an average value over SAR wind speed retrievals. SAR wind speed is calculated without knowing if the tested area is ice or water. Prior to the calculation of predictor variables, a3 3 pixel median filter was applied to both HH and HV images to reduce speckle noise. In the ice and water detection algorithm introduced in [7], a high-probability threshold value of P t = 0.95 was proposed in order to determine if the tested area is ice or open water. If the calculated probability of ice exceeds a threshold value of P high = P t, then the tested area is assigned as ice, and if the probability is lower than P low = 1 P t, then the tested area is assigned as open water. If the probability of ice is between P low and P high, then the tested area is assigned as unknown, meaning that the tested area could not be classified with high confidence as ice or open water within that approach. We note that both 2-D and 3-D IPM versions demonstrated high accuracy, but the 3-D IPM provided a significantly larger number of retrievals compared to the 2-D IPM. More details about the IPM development and verification can be found in [7]. III. ANALYSIS OF MISCLASSIFIED ICE AND WATER SAMPLES The most clean verification scenario of the IPM in [7] was implemented through the use of pure ice and open water data in the CIS Image Analysis products for the independent year of For both the ice and open water subsets, the following statistical characteristics were calculated: 1) fraction of correctly classified ice/water samples; 2) fraction of incorrectly (i.e., misclassified) ice/water samples; 3) fraction of unknown samples; 4) accuracy of ice/water retrievals as the ratio between the fraction of correctly classified ice/water samples to the sum of correctly classified and misclassified ice/water samples. TABLE I VERIFICATION RESULTS AGAINST IMAGE ANALYSIS PURE ICE AND WATER SAMPLES (2013) FOR IPM WITH ADAPTIVE PROBABILITY THRESHOLDING VERSUS ORIGINAL IPM WITH STATIC PROBABILITY THRESHOLDS OF P high = 0.95 AND P low = 0.05 (IN PARENTHESES) Fig. 1. (a) Probability of the misclassified ice samples in the space of SAR wind speed and 2-D ice probability for the training subset. The number of correctly classified water samples is ; and the number of misclassified ice samples is (b) Derived polygon enclosing the area of low probabilities of misclassified ice samples. P t = 0.95 and Pt = In [7] we found that 88.2% of pure ice samples were classified correctly while 0.98% of pure ice samples were misclassified when 3-D IPM is applied to the independent subset (2013). The percentage of pure water samples that were classified correctly was 61.5%, while 0.35% of pure water samples were misclassified. These data are also shown as part of Table I (in parentheses). In the following discussion, we present an analysis of classified and misclassified ice and water samples for the training pure ice and water subset (for , excluding the testing year, i.e., 2013). Based on the analysis, formulation of an improved ice and water detection algorithm is presented, and verification of the improved algorithm is described in Section IV. A. Low-Probability Threshold for Detecting Water In order to investigate the possibility to further reduce the fraction of misclassified ice samples and improve the accuracy of ice retrievals, we considered misclassified ice samples and correctly classified water samples (when the original probability threshold of 0.95 is used) in the space of SAR wind speed and in the space of probability of ice calculated according to the 2-D version of the IPM. Probability of the presence of misclassified ice samples calculated in the space of these two parameters is shown in Fig. 1(a). Note that the number of misclassified ice samples is approximately 60 times smaller than the number of

3 554 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 15, NO. 4, APRIL 2018 ice samples as follows: { 1 P t, if (V SAR, P 2-D ) A low P low = 1 Pt, otherwise. (2) The analysis that resulted in criterion (2) confirmed our initial hypothesis that at moderate SAR wind speeds, there is a higher chance to correctly detect open water (given that P 2-D tends to indicate water) compared to the high or low/negative SAR wind speeds. Therefore, a conservatively low ice probability threshold of 1 P t could be retained at moderate V SAR and relatively low P 2-D, and a very low ice probability threshold of 1 Pt could be used otherwise. Fig. 2. (a) Probability of the classified ice samples in the space of SAR wind speed and 2-D ice probability for the training subset. The number of misclassified water samples is ; and the number of correctly classified ice samples is (b) Derived polygon enclosing the area of high probabilities of classified ice samples. P t = 0.95 and Pt = correctly classified water samples. One may observe distinctive areas with high probabilities of misclassified ice samples. Therefore, if we decrease the threshold from 1 P t to 1 Pt in the considered areas (when determining whether the tested area is water), the number of misclassified ice samples will decrease, while the number of correctly classified water samples will not be significantly affected. We chose P t to be very high Correspondingly, in the area of low probabilities [shown in Fig. 1(a)] of misclassified ice samples, the original probability threshold could be retained. In order to characterize the area with lower probabilities of misclassified ice samples, we implemented the following approach. First, we determined those points in the plot where the average value within the surrounding 3 3 binned values is lower than an auxiliary polygon threshold value. Second, a single conforming 2-D boundary of the obtained set of points was derived. To determine the auxiliary polygon threshold value, we considered the dependence of the fraction of correctly classified water samples in the training subset as a function of this parameter. The fraction of correctly classified water samples monotonically increases with increasing the polygon threshold parameter (not shown). This is associated with the fact that with increasing the polygon threshold value, the polygon area becomes larger which allows using the original higher probability threshold for water detection within a larger area. We chose the auxiliary polygon threshold parameter to be equal to 0.415, which corresponds to the fraction of correctly classified water samples 8% less than the original fraction of correctly classified water samples. The 8% value was arbitrary selected; however, the introduction of this parameter will allow tuning in the future: after the ice/water retrievals are assimilated, the overall positive impact on ice analyses and forecasts could be optimized by adjusting this parameter. The polygon corresponding to the derived auxiliary threshold value is shown in Fig. 1. Within this polygon, the probability threshold value could be set to the original value of 1 P t, while outside the polygon, the threshold value could be set to 1 P t as there is a higher probability of getting misclassified B. High-Probability Threshold for Detecting Ice Similar to Section III-A, we selected misclassified water samples and correctly classified ice samples in the spaces of V SAR and P 2-D, and calculated probability of having correctly classified ice samples in the space of these two variables. From Fig. 2(a), one may observe areas of low probabilities of correctly classified ice samples. Therefore, in such situations, the probability threshold could be increased from P t to Pt in order to reduce the number of misclassified water samples, yet not to considerably affect the number of correctly classified ice samples. Within the area of high probabilities of having correctly classified ice, the probability threshold P t could be retained. The polygon enclosing the area of high probabilities [shown in Fig. 2(b)] is determined using the same approach described in Section III-A. The auxiliary polygon threshold value was chosen based on the analysis of the fraction of correctly classified ice samples in the training subset as function of this parameter and was set to 0.428, which corresponds to the fraction of correctly classified ice samples 8% less than the original fraction of correctly classified ice samples. Based on the above, the high probability threshold value is calculated as follows: { P t, if (V SAR, P 2-D ) A high P high = Pt, otherwise. (3) The above criterion is also aligned with our initial hypothesis that at high SAR wind speeds (given that P 2-D tends to indicate ice), there is a greater chance to correctly detect ice compared to moderate and low SAR wind speeds. Therefore, a conservatively high-probability threshold of P t could be retained for high V SAR and relatively high P 2-D,andavery high ice probability threshold of Pt couldbeusedotherwise. C. Formulation of Improved Approach The proposed improved algorithm for ice and water detection is formulated as follows. First, for a given pixel area, the probability of the presence of ice is calculated according to the 3-D IPM given by (1). Second, both P low and P high probability thresholds are calculated according to (2) and (3). If the probability of ice exceeds P high,then the tested area is assigned as ice; if the probability of ice is lower than P low, then the tested area is assigned as water; and if the probability of ice is between P low and P high, then the tested area is assigned as unknown. We note that in the original version of the algorithm, P low and P high were constants 1 P t and P t, respectively.

4 KOMAROV AND BUEHNER: ADAPTIVE PROBABILITY THRESHOLDING 555 TABLE II VERIFICATION RESULTS AGAINST ALL IMAGE ANALYSIS ICE CONCEN- TRATION SAMPLES (2013) FOR IPM WITH ADAPTIVE PROBABILITY THRESHOLDING VERSUS ORIGINALIPM WITH STATIC PROBABILITY THRESHOLDS OF P high = 0.95 AND P low = 0.05 (IN PARENTHESES) Fig. 3. Fraction of ice/water retrievals in each Image Analysis ice concentration category for the (a) training subset and the (b) testing subset in the case of IPM with adaptive probability thresholding (solid lines) and IPM with a static threshold (dashed lines). The point of intersection denotes ice concentration threshold for ice/water retrievals. IV. VERIFICATION In this section, we present a series of results of the same verification tests described in [7], but for the improved version of the ice and water detection algorithm. Table I demonstrates a comparison of verification results between the improved and previous versions of the algorithm for pure ice and pure water samples as indicated in CIS image analysis products for the entire year of One may observe that the fraction of misclassified ice samples decreased from 0.98% to 0.24%, while the fraction of correctly classified water samples did not drop appreciably (from 61.5% to 54.9%). The fraction of misclassified water samples also decreased from 0.35% to 0.09%, while the fraction of correctly classified ice samples did not decrease significantly (from 88.2% to 81.3%). The overall accuracy has increased from 99.2% to 99.8%. We note that if we simply increase the static probability threshold from 0.95 to 0.99, the overall accuracy increases from 99.2% to 99.7%, but the total number of retrievals drops by 18.5% (as opposed to 6.7% decrease in the case of using the adaptive thresholding). These results suggest that the use of adaptive probability thresholding led to improved 99.8% accuracy of ice and water retrievals without a significant change in the total number of retrievals. For further verification, we consider samples in CIS image analysis products belonging to polygons with intermediate ice concentrations. Such an analysis also allows us to determine how the ice/water detection algorithm behaves at different ice concentrations as stated in image analyses. Fig. 3(a) shows the dependences of the fraction of ice and water retrievals as functions of ice concentration obtained through the improved and original versions of the ice and water detection algorithm for the training subset. It is seen that the curve corresponding to the ice retrievals derived with the improved algorithm (solid line) behaves steeper compared to the curve corresponding to the ice retrievals derived with the original approach (dashed line). This indicates that the use of the improved algorithm leads to a larger fraction of ice retrievals at high ice concentrations and smaller fraction of ice retrievals at lower ice concentrations. A larger fraction of water retrievals at low ice concentrations and smaller fraction of water retrievals at high ice concentrations in the case of using adaptive probability thresholding are observed in Fig. 3(a). The intersection point between ice and water curves indicates the ice concentration threshold separating ice and water states that threshold has slightly increased from 21% (static threshold) to 25% (adaptive threshold) of ice concentration. Similar behavior of the ice and TABLE III VERIFICATION RESULTS AGAINST IMS (2013) FOR IPM WITH ADAPTIVE PROBABILITY THRESHOLDING VERSUS ORIGINALIPM WITH STATIC PROBABILITY THRESHOLDS OF P high = 0.95 AND P low = 0.05 (IN PARENTHESES) water curves is found for the testing subset (2013), as shown in Fig. 3(b). Table II shows a comparison of verification results for samples belonging to polygons with all ice concentrations for the testing subset (2013) in the case of using static and adaptive probability thresholds. Similar to the previous verification test for pure ice and water samples, an improvement in the fraction of misclassified ice and water samples is observed, while the total number of retrievals did not drop considerably. Table III shows the verification results against interactive multisensor snow and IMS products [12], [13] for 7411 RADARSAT-2 images acquired over the year of Improvement in the fraction of misclassified ice and water samples is also found in the case of using adaptive probability thresholding as opposed to the static threshold. We note that here we omit a discussion on limitations of the various verification approaches as they were assessed in detail in [7]. These limitations include the difference between the spatial scale of the ice/water detection algorithm and size of polygons in Image Analysis products as well as low temporal resolution of IMS products. Despite the limitations, we emphasize the fact that in all verification scenarios, improvement in the accuracy of ice and water retrievals was observed in the case of using the adaptive probability thresholding. To better demonstrate the importance of improvements made to the original approach, here we show two cases of ice and water retrieval maps. Fig. 4 presents an example of ice and water retrievals using the original and modified approaches versus IMS data. Ice retrievals which agree with IMS are marked by red squares, and ice retrievals which do not agree with IMS are marked by yellow squares.

5 556 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 15, NO. 4, APRIL 2018 one to accurately capture the dynamics of a marginal ice zone, as discussed in [7]. Fig. 4. Example of ice and water detection versus IMS using the (a) original and (b) modified approaches. The image (500 km 500 km) was acquired on February 8, 2013, 10:40 GMT, over Ungava Bay and part of the Labrador Sea. Each square represents a pixel area at 50-m resolution used for ice/water retrieval. The distance between squares is 100 pixels, which is 5 km. Red/blue squares: ice/water retrievals agree with IMS, and yellow/green squares: ice/water retrievals disagree with IMS. V. C ONCLUSION In this letter, we propose an improved version of our technique for automated detection of ice and open water from RADARSAT-2 HH HV ScanSAR images [7] by introducing adaptive probability thresholding. We conducted an analysis of distributions of correctly classified and misclassified ice and water samples as identified by the original ice and water detection algorithm with a static probability threshold of Based on that analysis, situations where the original probability threshold could be increased were identified, and the improved version of the ice and water detection algorithm was formulated. A set of verification tests of the improved algorithm demonstrated a decrease in the fraction of misclassified ice and water samples (compared to that obtained with the original version of the algorithm), while the fraction of correctly classified ice and water samples did not decrease appreciably. The improved algorithm for ice and water detection from RADARSAT-2 will be implemented as part of the data assimilation component of the ECCC RIOPS. The developed approach will be adapted to the stream of SAR data from the upcoming three-satellite RADARSAT Constellation Mission [14] to be launched in R EFERENCES Fig. 5. Example of ice and water detection versus IMS using the (a) original and (b) modified approaches. The image (500 km 500 km) was acquired on November 9, 2013, 12:29 GMT, over western Hudson Bay. Water retrievals which agree with IMS are marked by blue squares, and water retrievals which do not agree with IMS are marked by green squares. The absence of ice/water retrievals indicates the unknown state, meaning that reliable classification is not possible within our approach. From Fig. 4, it is seen that the number of misclassified ice samples (i.e., water retrievals which disagree with IMS) has significantly decreased from 407 to 13 while the number of correctly classified water retrievals did not change. This example mostly demonstrates the improvement achieved through introduction of criterion (3). SAR wind speed for the misclassified ice samples in this case is relatively high, 15.9 ± 1.9 m/s, and the 2-D ice probability is 0.09 ± 0.05, which corresponds to high probability of having misclassified ice, as shown in Fig. 1(a). In the other example shown in Fig. 5, the number of misclassified water samples (i.e., ice retrievals which do not agree with IMS) has decreased from 127 to 5 while the number of correctly classified ice samples did not change appreciably. The second example mostly demonstrates the improvement through the introduction of criterion (4). SAR wind speed for the misclassified water samples in this case is moderate, 10.8 ± 1.5 m/s, and the 2-D ice probability is 0.85 ± 0.13, which corresponds to low probability of having correctly classified ice, as shown in Fig. 2(a). We note that most of the remaining misclassified ice and water retrievals in both examples are located at the ice edge. 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