郭剛剛,樊 偉,薛嘉倫,張勝茂,張 衡,唐峰華,程田飛※
(1. 中國水產(chǎn)科學(xué)研究院東海水產(chǎn)研究所,農(nóng)業(yè)部東海與遠洋漁業(yè)資源開發(fā)利用重點實驗室,上海 200090;2. 上海海洋大學(xué)海洋科學(xué)學(xué)院,上海 201306)
基于NPP/VIIRS夜光遙感影像的作業(yè)燈光圍網(wǎng)漁船識別
郭剛剛1,2,樊 偉1,薛嘉倫1,2,張勝茂1,張 衡1,唐峰華1,程田飛1※
(1. 中國水產(chǎn)科學(xué)研究院東海水產(chǎn)研究所,農(nóng)業(yè)部東海與遠洋漁業(yè)資源開發(fā)利用重點實驗室,上海 200090;2. 上海海洋大學(xué)海洋科學(xué)學(xué)院,上海 201306)
為對遠洋燈光漁船作業(yè)信息進行實時動態(tài)監(jiān)測,該研究基于可見光紅外輻射儀(visible infrared imaging radiometer suite,VIIRS)夜光遙感影像,根據(jù)遠洋燈光漁船作業(yè)時其集魚燈燈光在VIIRS白天/夜晚波段(day/night band,DNB)影像上的輻射特征,采用峰值中值指數(shù)(spike median index,SMI)對燈光漁船與背景像元間的輻射差異進行拉伸,在此基礎(chǔ)上設(shè)計了基于最大熵法(maximum entropy method,MaxEnt)閾值分割以及局部峰值檢測(local spike detection,LSD)的作業(yè)遠洋燈光漁船識別算法,并采用2015年西北太平洋公海燈光圍網(wǎng)漁場內(nèi)作業(yè)漁船船位監(jiān)控系統(tǒng)(vessel monitoring system,VMS)數(shù)據(jù)對該算法的識別精度進行檢驗。驗證結(jié)果顯示,該文提出的作業(yè)遠洋燈光漁船自動識別算法對實際作業(yè)燈光漁船的識別精度在 92%以上,可以滿足遠洋燈光漁船日常監(jiān)測的需求,可為進一步評估遠洋光誘漁業(yè)捕撈努力量、推進遠洋光誘漁業(yè)信息化管理以及打擊非法、未申報和無管制的(illegal,unregulated,unreported,IUU)捕撈活動提供技術(shù)支持。
遙感;漁船;監(jiān)測;夜光遙感;NPP/VIIRS;DNB影像
郭剛剛,樊 偉,薛嘉倫,張勝茂,張 衡,唐峰華,程田飛. 基于 NPP/VIIRS夜光遙感影像的作業(yè)燈光圍網(wǎng)漁船識別[J]. 農(nóng)業(yè)工程學(xué)報,2017,33(10):245-251. doi:10.11975/j.issn.1002-6819.2017.10.032 http://www.tcsae.org
Guo Ganggang, Fan Wei, Xue Jialun, Zhang Shengmao, Zhang Heng, Tang Fenghua, Cheng Tianfei. Identification for operating pelagic light-fishing vessels based on NPP/VIIRS low light imaging data[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(10): 245-251. (in Chinese with English abstract) doi:10.11975/j.issn.1002-6819.2017.10.032 http://www.tcsae.org
燈光漁船是指利用魚類的趨光特性,采用燈光誘捕來捕撈海洋魚類的漁船[1]。早在20世紀70年代就有學(xué)者發(fā)現(xiàn),依托星載微光探測器,可在無云狀況下實現(xiàn)對夜間使用大功率照明設(shè)備進行集魚、誘魚的燈光漁船進行監(jiān)測[2]。20世紀 90年代,美國國家地球物理數(shù)據(jù)中心(national geophysical datacenter,NGDC)開始提供國防氣象衛(wèi)星計劃的線性掃描業(yè)務(wù)系統(tǒng)(defense meteorological satellite program?s operational linescan system,DMSP/OLS)數(shù)字影像數(shù)據(jù),此后相繼有學(xué)者對基于DMSP/OLS夜光影像數(shù)據(jù)的燈光漁船識別算法進行研究[3-11]。但由于DMSP/OLS 在設(shè)計之初缺少傳感器輻射定標,因此其數(shù)據(jù)只能用于定性研究而難以對燈光漁船進行定量檢測[12]。2011年,第一顆搭載可見光紅外輻射儀(visible infrared imaging radiometer suite,VIIRS)的美國國家極軌合作伙伴(suomi national polar orbiting partnership,NPP)衛(wèi)星發(fā)射升空,VIIRS的白天/夜晚波段(day/night band,DNB)繼承并優(yōu)化了 DMSP/OLS的微光探測能力,相較于DMSP/OLS,VIIRS/DNB具有更小的瞬時視場、更多的灰度級以及更高的空間分辨率,且 DNB波段采用了和VIIRS其他波段相一致的輻射校正,因此其數(shù)據(jù)可用于燈光漁船定量監(jiān)測[13-15]。
根據(jù)近海燈光漁船在NPP/VIIRS夜光遙感影像上的輻射特征,Elvidge等[16]提出了基于峰值檢測以及固定閾值分割的近海燈光漁船識別方法,但對于集魚燈功率較大的遠洋燈光漁船,其集魚燈所處像元的臨近像元亦可能被大功率集魚燈照亮而具有較高的輻射值,從而產(chǎn)生誤識;且固定閾值分割主要依據(jù)人為經(jīng)驗,具有一定的隨機性和局限性。針對上述問題,本文在Elvidge研究的基礎(chǔ)上進行了優(yōu)化和改進,提出了基于最大熵閾值分割以及局部峰值檢測的作業(yè)遠洋燈光漁船識別算法,以期能實現(xiàn)無云條件下基于NPP/VIIRS夜光遙感影像的作業(yè)遠洋燈光漁船精確識別。
漁業(yè)數(shù)據(jù)是進行漁業(yè)科學(xué)研究的基礎(chǔ),目前原始漁業(yè)數(shù)據(jù)主要來源于漁撈日志及船位監(jiān)控系統(tǒng)(vessel monitoring system,VMS)數(shù)據(jù),漁撈日志往往由漁民手工填寫,在漁船返港后方可帶回,且隨著數(shù)據(jù)的層層上報,錯報、誤報的情況也時有發(fā)生,其時效性和準確性均有待提高;VMS可以精確記錄漁船的實時船位信息,但其數(shù)據(jù)中卻并未包含漁船是否處于捕撈作業(yè)狀態(tài)的信息[17]?;贜PP/VIIRS夜光遙感影像,可以近實時的獲取作業(yè)遠洋燈光漁船的空間位置信息,一定程度上彌補了漁撈日志和VMS數(shù)據(jù)的不足,可為遠洋光誘漁業(yè)提供一種新的漁船作業(yè)信息數(shù)據(jù),緩解目前漁業(yè)數(shù)據(jù)來源匱乏的窘境。
1.1 數(shù)據(jù)簡介
NPP為近極地太陽同步軌道衛(wèi)星,軌道高度824 km,衛(wèi)星上共搭載了 5種傳感器,VIIRS為其中最重要的載荷[18]。VIIRS共有22個光譜波段,光譜范圍覆蓋 0.3~14mm,掃描幅寬為3 000 km,星下點過赤道周期為4 h[19]。其中DNB的光譜范圍為500~900 nm,主要用于收集微光成像數(shù)據(jù),依托 VIIRS強大的光電放大能力,能夠獲取并呈現(xiàn)夜間海上作業(yè)漁船燈光[20]。本研究采用夜間成像的DNB傳感器數(shù)據(jù)記錄(sensor data record,SDR)影像數(shù)據(jù)對夜間作業(yè)的遠洋燈光漁船進行識別,SDR影像數(shù)據(jù)的空間分辨率為742 m,數(shù)據(jù)來源于美國國家海洋和大氣管理局(national oceanic and atmospheric administration,NOAA)下屬的綜合性大型陣列數(shù)據(jù)管理系統(tǒng)(comprehensive large array-data stewardship system,CLASS)網(wǎng)站(http://www.class.ngdc.noaa.gov/saa/products/)[21]。
1.2 數(shù)據(jù)預(yù)處理
DNB影像的預(yù)處理包括輻射拉伸和濾波降噪2個部分,見圖1所示。
圖1 技術(shù)路線圖Fig.1 Flow chart of technology
DNB/SDR數(shù)據(jù)的原始輻射單位為W/(sr·cm2),其原始輻射值通常在 10-11~10-8W/(sr·cm2)之間,小數(shù)點后及有效輻射值前通常存在7到10個零,過小的輻射值會給數(shù)據(jù)讀取和處理均帶來不便,因此將原始輻射值統(tǒng)一乘以109,使其輻射單位變換為nW/(sr·cm2),對應(yīng)的輻射值也轉(zhuǎn)換至10-2nW/(sr·cm2)以上。VIIRS/DNB在掃描成像過程中,由于掃描角度的變化,易受到白噪聲的影響,且掃描帶邊緣噪聲水平高于星下點。研究選用維納濾波器對DNB影像進行濾波降噪處理,維納濾波是一種自適應(yīng)濾波,通過計算影像中3×3鄰域內(nèi)輻射值的均方差,依據(jù)最小均方差準則實現(xiàn)最優(yōu)濾波,對白噪聲有良好的濾除效果[22]。
1.3 燈光漁船識別算法
作業(yè)遠洋燈光漁船呈現(xiàn)在夜間DNB影像上是一系列非臨近分布的高輻射值“亮點”,因此,遠洋燈光漁船識別算法的整體思想是通過峰值檢測提取出圖像中的非臨近“亮點”。
1.3.1 峰值中值指數(shù)
為獲取特征化的燈光漁船輻射信息,應(yīng)盡量拉大燈光漁船與背景像元間的輻射差異。研究采用Elvidge等[15]提出的峰值中值指數(shù)(spike median index,SMI)對燈光漁船與背景像元間的輻射差異進行放大。首先采用 3×3中值濾波器對預(yù)處理后的圖像進行平滑。中值濾波是將圖像中的每個像元在以其為中心的鄰域內(nèi)取中間亮度值來代替該像元值,從而達到去除圖像中“亮點”的同時盡量保留原圖信息的目的[23]。然后對中值濾波后的影像與預(yù)處理后的影像作差,即可進一步放大影像中“亮點”與背景像元間的差異,得到作業(yè)燈光漁船的特征化影像(圖2)。
圖2 維納濾波、中值濾波及峰值中值指數(shù)效果Fig.2 Effects of wiener filtered, median filtered and spike median index
1.3.2 最大熵閾值分割
對于目標與背景像元間輻射差異較大的圖像,閾值分割是一種有效的圖像分割方法,而閾值的選取則是閾值分割的關(guān)鍵,閾值選取的準確性直接關(guān)系到閾值分割的效果。本研究采用最大熵法(maximum entropy method,MaxEnt)對燈光漁船特征化圖像進行自適應(yīng)閾值分割。在信息論中,熵是對隨機變量不確定性的度量,如果將數(shù)字圖像的像素輻射值看作一組隨機變量,那么圖像的熵就是測量輻射級分布隨機性的一種特征參數(shù)[24]。在圖像分割的過程中,越靠近目標與背景的邊界,其分類的不確定性(熵)就越大,最大熵閾值分割正是基于以上假設(shè),即在分割過程中,應(yīng)盡量使目標與背景的熵值之和達到最大[25-26]。相較于固定閾值分割,最大熵閾值分割具有更好的分割效果和自適應(yīng)性。最大熵閾值分割效果如圖3所示,其分割結(jié)果以CSV文件形式輸出,以用于后續(xù)分析處理。最大熵閾值的計算公式如下[27]:
式中Thr為閾值(Threshold)的縮寫,ThrEnt為最大熵閾值;H為目標HF與背景HB的熵之和;x為像素輻射值;p(x)為直方圖中像素輻射值出現(xiàn)的概率;min(Rad)為圖像中的最小像素輻射值;max(Rad)為圖像中的最大像素輻射值。
圖3 基于最大熵法閾值分割的像元峰值中值指數(shù)(SMI)剖面圖Fig.3 Pixel profiles for spike median index (SMI) based on the MaxEnt threshold segmentation
1.3.3 局部峰值檢測算法
最大熵法可以實現(xiàn)圖像中“亮點”與背景像元間的最優(yōu)閾值分割,但實際情況中并非所有的“亮點”均為燈光漁船。DNB影像的空間分辨率為742 m,對于集魚燈功率較大的遠洋燈光漁船,其燈光除照亮漁船自身所處像元外,還可能導(dǎo)致其臨近像元亦被照亮,從而在DNB影像上也呈現(xiàn)為高輻射值的“亮點”,以致在閾值分割中被誤識為遠洋燈光漁船。局部峰值檢測(local spike detection,LSD)算法的實質(zhì)就是從最大熵閾值分割結(jié)果中各“亮點”間的空間臨近關(guān)系出發(fā),尋找并去除這些非燈光漁船的“亮點”,從而達到從圖像中準確提取出作業(yè)遠洋燈光漁船的目的(圖4)。LSD算法實現(xiàn)步驟如下:1)將最大熵閾值分割結(jié)果中首行像元定義為像元i,其余像元分別定義為像元j1,j2,j3…jn,計算像元i、j間的空間距離;2)DNB影像的空間分辨率為742 m,因此若i、j間的距離不超過742 m,則表明像元i、j為臨近像元,且光傳播過程中其能量隨距離增加而逐漸衰減,所以將兩像元中SMI值較大的像元定義為像元i,將SMI值較小的像元刪除,并依次循環(huán),直至搜索不到與像元i距離在742 m以內(nèi)的像元時,將最后一個像元i輸出為a1,同時將其從原CSV文件中刪除;3)完成輸出像元a1的過程后,將CSV文件所剩余像元中的首行像元定義為像元i,重復(fù)步驟 2),直至 CSV文件中所有像元均被刪除,其對應(yīng)輸出結(jié)果即為作業(yè)遠洋燈光漁船所處像元的位置及亮度信息。圖4b中綠色船形即為局部峰值檢測算法所提取出的局部唯一燈光漁船。
圖4 局部峰值檢測算法效果Fig.4 Effect of local spike detection algorithm
采用西北太平洋公海燈光圍網(wǎng)漁場漁船VMS數(shù)據(jù)對基于本文算法的作業(yè)遠洋燈光漁船識別精度進行驗證。西北太平洋公海燈光圍網(wǎng)漁場空間范圍為 146°E~153°E,38°N~43°N,主要作業(yè)漁船類型為燈光圍網(wǎng)漁船。
2.1 VMS數(shù)據(jù)預(yù)處理
VMS可以實時記錄漁船的位置信息,中國遠洋漁船VMS數(shù)據(jù)的回報頻率為4 h,定位精度為10 m。燈光漁船夜間作業(yè)時其位置基本保持不動,故而4 h內(nèi)同一作業(yè)遠洋燈光漁船反映在DNB影像及VMS數(shù)據(jù)上的位置基本不變。由于VMS數(shù)據(jù)并未包含漁船是否處于捕撈作業(yè)狀態(tài)的信息,因此,驗證前需先通過作業(yè)遠洋燈光漁船VMS數(shù)據(jù)提取算法去除非作業(yè)狀態(tài)的VMS數(shù)據(jù)。出于作業(yè)安全的考慮,漁船作業(yè)時相互間需保持一定的安全距離,通過詢問有經(jīng)驗的遠洋燈光漁船船長得知,這一安全距離通常在2海里(約3.6 km)以上。因此,以該作業(yè)遠洋燈光漁船識別算法識別出的遠洋燈光漁船船位為中心,以 2海里為半徑建立緩沖區(qū)(圖 5a),若某一VMS數(shù)據(jù)記錄的漁船船位位于該2海里緩沖區(qū)內(nèi),即認為該 VMS數(shù)據(jù)對應(yīng)的燈光漁船正處于作業(yè)狀態(tài)。圖 5b中作業(yè)遠洋燈光漁船2海里緩沖區(qū)內(nèi)的VMS船位即為所提取出的作業(yè)遠洋燈光漁船VMS船位。
圖5 作業(yè)遠洋燈光漁船VMS數(shù)據(jù)提取效果示例Fig.5 Example for VMS data extraction effect of operating pelagic light-fishing vessels
2.2 驗證方法
隨機選取2015年5月24日23:15:06(北京時間)成像的研究海域DNB影像進行作業(yè)遠洋燈光漁船識別精度驗證。首先,采用本文算法對影像中的作業(yè)遠洋燈光漁船進行識別;隨后,以影像的成像時間為中心,提取前后兩小時內(nèi)研究海域中所有正處于作業(yè)狀態(tài)的中國籍遠洋燈光漁船的VMS數(shù)據(jù);最后,將本文算法檢測結(jié)果與作業(yè)遠洋燈光漁船VMS數(shù)據(jù)提取結(jié)果進行比對,即計算本文算法檢測結(jié)果與作業(yè)遠洋燈光漁船VMS數(shù)據(jù)提取結(jié)果相一致的概率。計算公式如下:
式中R為本文算法所檢測出的燈光漁船數(shù)量,Rv為作業(yè)遠洋燈光漁船VMS數(shù)據(jù)提取算法所提取出的燈光漁船數(shù)量,P為本文算法對作業(yè)遠洋燈光漁船的識別精度。
2.3 驗證結(jié)果
基于2015年5月24日西北太平洋公海燈光圍網(wǎng)漁場夜間DNB影像,采用本文算法共檢測出27艘作業(yè)遠洋燈光漁船;作業(yè)遠洋燈光漁船VMS數(shù)據(jù)提取算法共提取出25艘在研究海域內(nèi)捕撈作業(yè)的中國籍燈光漁船,與本文算法所識別出的燈光漁船數(shù)量相比,存在 2艘漁船的差異,即本文算法識別結(jié)果中存在 2艘漁船誤識,識別精度為92%(表1)。事實上,西北太平洋公海燈光圍網(wǎng)漁場內(nèi)除中國籍燈光漁船外,也有少量非中國籍燈光漁船生產(chǎn)作業(yè),受客觀因素影響,中國遠洋漁船VMS數(shù)據(jù)庫中并未包含非中國籍燈光漁船VMS數(shù)據(jù),因此,本文算法對作業(yè)遠洋燈光漁船的識別精度可能仍在92%之上。
表1 作業(yè)遠洋燈光漁船識別精度驗證Table 1 Identification accuracy verification of operating pelagic light-fishing vessels
試驗發(fā)現(xiàn),本文算法在新月前后的無云或薄云條件下對作業(yè)遠洋燈光漁船能夠保持較高的識別精度,但在滿月時其識別精度會大幅下降。究其原因DNB的光譜范圍為500~900 nm,屬于可見光和近紅外波段,不具有“穿云透霧”的能力。滿月前后,即便是薄云也會對月光進行強烈反射,導(dǎo)致云下漁船燈光的發(fā)射輻射淹沒在云層對月光的反射輻射中。云掩膜是去除云覆蓋的有效手段,但掩膜的同時也會將云下燈光漁船輻射信息一并去除,從而無法實現(xiàn)對影像中所有作業(yè)遠洋燈光漁船的完整識別[28]。VIIRS共有22個光譜波段,光譜范圍為 0.3~14mm,將DNB數(shù)據(jù)與VIIRS熱紅外波段數(shù)據(jù)相結(jié)合,通過對DNB數(shù)據(jù)的月光輻射特征和與其對應(yīng)的熱紅外波段數(shù)據(jù)進行相關(guān)分析,實現(xiàn)DNB強大的微光探測能力以及熱紅外波段較好的云層識別能力間的優(yōu)勢互補,可能是降低云覆蓋對DNB圖像質(zhì)量的影響,提高燈光漁船識別穩(wěn)定性的一種可行性方法[16]。
對于不同作業(yè)類型的燈光漁船,由于集魚燈類型、數(shù)量、功率、放置角度以及是否裝有燈罩等因素的差異,反映在DNB影像上即表現(xiàn)為不同的輻射量級。燈光漁船識別結(jié)果與漁撈日志數(shù)據(jù)或VMS數(shù)據(jù)相結(jié)合,通過時空匹配可以獲取燈光漁船的作業(yè)類型信息。隨著大量不同作業(yè)類型燈光漁船輻射特征先驗信息的積累,結(jié)合相應(yīng)的模式識別算法,可進一步區(qū)分燈光漁船的類型,獲取不同作業(yè)類型的燈光漁船作業(yè)信息數(shù)據(jù)。
本文基于NPP/VIIRS夜光遙感影像,設(shè)計了一種作業(yè)遠洋燈光漁船精確識別方法。NPP/VIIRS夜光遙感影像的空間分辨率高達742 m,且在衛(wèi)星過境4 h后即可免費獲取,基于NPP/VIIRS夜光遙感影像的作業(yè)遠洋燈光漁船識別可為遠洋光誘漁業(yè)提供一種新的、低成本的、近實時的高精度漁船作業(yè)信息數(shù)據(jù),一定程度上彌補了漁撈日志數(shù)據(jù)及VMS數(shù)據(jù)在某些方面的不足,具有廣泛的應(yīng)用前景。通過對作業(yè)遠洋燈光漁船識別結(jié)果進行統(tǒng)計分析,可快速掌握漁場內(nèi)捕撈努力量的時空分布特征[7,10,29]。作業(yè)遠洋燈光漁船識別結(jié)果與傳統(tǒng)漁業(yè)數(shù)據(jù)相結(jié)合,可用于分析中心漁場的時空變化特征,推測目標魚種的洄游路線[3,9,30]。作業(yè)遠洋燈光漁船識別結(jié)果應(yīng)用于漁業(yè)管理上,可以幫助漁業(yè)管理部門快速了解漁船的真實作業(yè)位置信息,有效打擊非法、未申報和無管制的(illegal, unregulated, unreported, IUU)捕撈活動,大大提高漁船監(jiān)管的效率[31]。
本文根據(jù)作業(yè)遠洋燈光漁船在NPP/VIIRS夜光遙感影像上的輻射特征,提出了適用于作業(yè)遠洋燈光漁船自動識別的圖像檢測算法,并采用2015年西北太平洋公海燈光圍網(wǎng)漁場內(nèi)燈光圍網(wǎng)漁船VMS數(shù)據(jù)對該算法的識別精度進行檢驗,得到結(jié)論如下:
1)在現(xiàn)有研究仍多依據(jù)個人經(jīng)驗進行固定閾值分割的基礎(chǔ)上,采用最大熵法對NPP/VIIRS夜光遙感影像進行自適應(yīng)閾值分割,避免了固定閾值分割的主觀性及不穩(wěn)定性,從而有效提高圖像閾值分割的效果和自適應(yīng)性。
2)針對遠洋燈光漁船大功率集魚燈照亮其臨近像元而可能產(chǎn)生的誤識現(xiàn)象,自主設(shè)計了一種誤識像元自動濾除算法,通過尋找臨近“亮點”像元中的局部輻射峰值像元,濾除因被大功率集魚燈照亮從而亦具有高輻射亮度的非燈光漁船像元,提高作業(yè)遠洋燈光漁船識別的準確度。
3)采用 2015年西北太平洋公海燈光圍網(wǎng)漁場內(nèi)燈光圍網(wǎng)漁船VMS數(shù)據(jù)對該算法的識別精度進行檢驗,結(jié)果表明:該文提出的作業(yè)遠洋燈光漁船自動識別算法對實際作業(yè)燈光漁船的識別精度在 92%以上,具有較高的識別精度和良好的可行性,可以滿足遠洋燈光漁船日常監(jiān)測的需求,且相對傳統(tǒng)漁業(yè)數(shù)據(jù)來源具有實時性高及獲取成本低等優(yōu)勢。
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Identification for operating pelagic light-fishing vessels based on NPP/VIIRS low light imaging data
Guo Ganggang1,2, Fan Wei1, Xue Jialun1,2, Zhang Shengmao1, Zhang Heng1, Tang Fenghua1, Cheng Tianfei1※
(1.Key Lab of East China Sea & Oceanic Fishery Resources Exploitation and Utilization, Ministry of Agriculture;
East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai200090,China;2.College of Marine Sciences, Shanghai Ocean University, Shanghai201306,China)
Fishing data are the basement of fisheries science research, but currently the source of fishing data is extraordinarily scarce, and data quality is poor in some aspects. Satellite low light sensors can detect the light-fishing vessels at night, however,its application in pelagic fishery has been limited by the lack of an algorithm for extracting the location and brightness of operating pelagic light-fishing vessels. An examination of operating pelagic light-fishing vessels features in the day/night band(DNB) image, which was from the visible infrared imaging radiometer suite (VIIRS) on the Suomi National Polar-orbiting Partnership (NPP) satellite, indicated that the features were a list of nonadjacent bright spots. In order to identify the operating pelagic light-fishing vessels from VIIRS/DNB accurately, we designed a set of identification algorithm for operating pelagic light-fishing vessels according to the light radiation characteristics of its fishing gathering lamps in NPP/VIIRS low light image. Before applying the identification algorithm, a data pre-processing step was adopted through radiation stretch and noise reduction by adaptive Wiener filter to prepare the data for further analysis and use. A spike median index (SMI) was used to enlarge the radiation difference between operating pelagic light-fishing vessel pixels and background pixels. On the basis of this, an adaptive threshold segmentation method called the maximum entropy (MaxEnt) method was used to extract the bright spot pixels, and generated a list of candidate operating pelagic light-fishing vessels detections. The candidate pixels were then filtered to remove the false identification bright spot pixels distributed near the operating pelagic light-fishing vessel pixels,and illuminated by the high-power fishing gathering lamps by a local spike detection (LSD) algorithm. A validation study was conducted at a night with weak lunar illuminance on May 24, 2015 which was selected randomly, using the vessel monitoring system (VMS) data of Chinese operating light-seiners vessels on the high seas of Northwest Pacific Ocean light seine fishing ground and the result of VIIRS/DNB image visual interpretation. The validation result showed that the identification algorithm detected 27 operating pelagic light-fishing vessels on the high seas of Northwest Pacific Ocean light seine fishing ground, and the number of operating pelagic light-fishing boats and their distribution were entirely consistent with the result of VIIRS/DNB image visual interpretation; the VMS data had the record of 25 operating pelagic light-fishing vessels among the total 27 vessels, and their distribution was nearly the same with the result of identification algorithm and VIIRS/DNB image visual interpretation. The identification algorithm worked well when lunar illuminance was weak and its identification accuracy was above 92%. The identification algorithm not only avoided the subjectivity and uncertainty of certain threshold segmentation,but also removed the false identification bright spot pixels near the operating pelagic light-fishing vessel pixels, which were illuminated by the high-power fishing gathering lamps. Detection of operating pelagic light-fishing vessels based on VIIRS/DNB imaging data can provide up-to-date activity and change information of operating pelagic light-fishing vessels for pelagic light-fishing industry, which meets the need of fishing boat’s daily monitoring, and has a wide application prospect in fishing effort estimation, research of central fishing ground spatial-temporal distribution and change, and fishery forecast and management.
remote sensing; fishing vessels; monitoring; nighttime light remote sensing; NPP/VIIRS; DNB image
10.11975/j.issn.1002-6819.2017.10.032
S973.1+1; P407.8
A
1002-6819(2017)-10-0245-07
2016-09-07
2017-03-21
國家科技支撐計劃項目(2013BAD13B01);中國水產(chǎn)科學(xué)研究院基本科研業(yè)務(wù)費項目(2016PT11);中央級公益性科研院所基本科研業(yè)務(wù)費專項資助項目(2016Z01-03)
郭剛剛,男,主要從事漁業(yè)遙感方面研究。上海 中國水產(chǎn)科學(xué)研究院東海水產(chǎn)研究所,200090。Email:gzguogang@126.com
※通信作者:程田飛,男,主要從事海洋遙感反演算法方面研究。上海 中國水產(chǎn)科學(xué)研究院東海水產(chǎn)研究所,200090。
Email:chengtianfeinuist@126.com