張軍國(guó)，馮文釗，胡春鶴，駱有慶

（1. 北京林業(yè)大學(xué)工學(xué)院，北京 100083；2. 北京林業(yè)大學(xué)林學(xué)院，北京 100083）

·農(nóng)業(yè)航空工程·

無(wú)人機(jī)航拍林業(yè)蟲(chóng)害圖像分割復(fù)合梯度分水嶺算法

張軍國(guó)1，馮文釗1，胡春鶴1，駱有慶2※

（1. 北京林業(yè)大學(xué)工學(xué)院，北京 100083；2. 北京林業(yè)大學(xué)林學(xué)院，北京 100083）

針對(duì)林業(yè)信息監(jiān)測(cè)方式實(shí)時(shí)性差、監(jiān)測(cè)范圍有限等問(wèn)題，為更加實(shí)時(shí)、準(zhǔn)確地對(duì)林業(yè)蟲(chóng)害信息進(jìn)行監(jiān)測(cè)并計(jì)算監(jiān)測(cè)樣地中蟲(chóng)害區(qū)域比例，該文在搭建面向林區(qū)蟲(chóng)害監(jiān)測(cè)的多旋翼無(wú)人飛行器航拍監(jiān)測(cè)系統(tǒng)基礎(chǔ)上，提出了一種基于復(fù)合梯度分水嶺算法的圖像分割方法。該方法引入全局直方圖均衡化消除了圖像暗紋理的影響，并采用形態(tài)學(xué)混合開(kāi)閉重構(gòu)濾波完成了圖像樣本的去噪處理。計(jì)算灰度圖像各像素點(diǎn)的復(fù)合梯度實(shí)現(xiàn)了非相關(guān)區(qū)域（道路及裸地）的提取，最終利用分水嶺算法實(shí)現(xiàn)了監(jiān)測(cè)圖像蟲(chóng)害區(qū)域的分割提取。利用該文所提算法對(duì)8幅蟲(chóng)害侵蝕程度不同的監(jiān)測(cè)圖像進(jìn)行分割，并與傳統(tǒng)分水嶺算法、K-means聚類算法進(jìn)行對(duì)比試驗(yàn)。試驗(yàn)結(jié)果表明，該文算法蟲(chóng)害區(qū)域提取的平均相對(duì)誤差率分別降低 6.56%、3.17%，平均相對(duì)極限測(cè)量精度分別改善 7.19%、2.41%，能夠相對(duì)準(zhǔn)確地將蟲(chóng)害區(qū)域從監(jiān)測(cè)圖像中分割出來(lái)，可為后續(xù)林業(yè)蟲(chóng)害監(jiān)測(cè)與防護(hù)提供參考。

無(wú)人機(jī)；算法；監(jiān)測(cè)；圖像分割；復(fù)合梯度

0 引 言

森林作為陸地生態(tài)系統(tǒng)的主體，是人類賴以生存及社會(huì)發(fā)展不可或缺的資源[1]。而林業(yè)蟲(chóng)害對(duì)森林資源的危害非常嚴(yán)重，根據(jù)調(diào)查結(jié)果表明，平均每年發(fā)生林業(yè)蟲(chóng)害面積有1 333萬(wàn)hm2左右，約占中國(guó)林地總面積的6%[2]。因此林業(yè)蟲(chóng)害防治形勢(shì)十分嚴(yán)峻，而科學(xué)有效地對(duì)林業(yè)蟲(chóng)害信息進(jìn)行監(jiān)測(cè)則是解決這一問(wèn)題的重要前提。目前，常用的林業(yè)蟲(chóng)害監(jiān)測(cè)方法主要為人工直接測(cè)量法[3]、引誘劑誘集法[4]、衛(wèi)星遙感測(cè)量法[5]以及無(wú)線傳感器網(wǎng)絡(luò)監(jiān)測(cè)法[6-7]。然而，現(xiàn)有監(jiān)測(cè)方法仍存在一定的缺陷，人工直接測(cè)量法監(jiān)測(cè)效率低、實(shí)時(shí)性差且存在安全隱患；引誘劑的誘集效果會(huì)受誘捕器懸掛高度和生態(tài)環(huán)境類型的影響；衛(wèi)星遙感測(cè)量法不能精確測(cè)量局部微觀信息；無(wú)線傳感器網(wǎng)絡(luò)監(jiān)測(cè)法只能進(jìn)行地面監(jiān)測(cè)，監(jiān)測(cè)范圍有限。為了避免上述問(wèn)題，利用小型化無(wú)人機(jī)作為監(jiān)測(cè)載體的方法逐漸取得了學(xué)者們的廣泛關(guān)注[8-9]。其中，多旋翼無(wú)人飛行器作為一種低空遙感平臺(tái)，具有結(jié)構(gòu)簡(jiǎn)單、制造維護(hù)成本低、便于攜帶和易于操作等特點(diǎn)[10]，可以實(shí)時(shí)、高效地低空采集林區(qū)植被圖像信息。

如何從圖像中有效地提取出健康林區(qū)與蟲(chóng)害區(qū)域則是研究的關(guān)鍵[11]。目前圖像分割方法在林業(yè)蟲(chóng)害監(jiān)測(cè)領(lǐng)域的應(yīng)用越來(lái)越廣[12]，已有國(guó)內(nèi)外學(xué)者提出了應(yīng)用于林業(yè)蟲(chóng)害監(jiān)測(cè)領(lǐng)域的圖像分割方法。例如邊緣檢測(cè)[13-15]、聚類法[16-18]、基于分?jǐn)?shù)演算的分水嶺算法[19]和基于顯著和模糊檢測(cè)的圖像分割方法[20]等。但是，由于蟲(chóng)害監(jiān)測(cè)圖像中非相關(guān)區(qū)域與病蟲(chóng)害區(qū)域顏色相近，而目前現(xiàn)有的算法并沒(méi)有針對(duì)蟲(chóng)害區(qū)域分割提取過(guò)程中圖像非相關(guān)區(qū)域的影響問(wèn)題進(jìn)行深入研究，同時(shí)大部分算法只針對(duì)地面圖像進(jìn)行分析，不適用于多旋翼無(wú)人飛行器航拍圖像[21]。

基于以上情況，本文以遼寧省建平縣遭受油松毛蟲(chóng)侵蝕的油松林為研究對(duì)象，搭建了面向林業(yè)蟲(chóng)害監(jiān)測(cè)的多旋翼無(wú)人飛行器圖像采集平臺(tái)，提出了一種基于復(fù)合梯度分水嶺算法的圖像分割方法，實(shí)現(xiàn)了林業(yè)蟲(chóng)害監(jiān)測(cè)圖像進(jìn)行有效提取，解決了非相關(guān)區(qū)域?qū)οx(chóng)害區(qū)域分割提取的影響問(wèn)題以及圖像分割中存在的過(guò)分割現(xiàn)象。

1 林區(qū)蟲(chóng)害監(jiān)測(cè)飛行器平臺(tái)設(shè)計(jì)

本文采用自主設(shè)計(jì)的八旋翼飛行器執(zhí)行林區(qū)蟲(chóng)害監(jiān)測(cè)圖像的監(jiān)測(cè)任務(wù)。八旋翼飛行器采用 8個(gè)獨(dú)立電機(jī)驅(qū)動(dòng)，相鄰電機(jī)旋轉(zhuǎn)方向相反，相對(duì)電機(jī)旋轉(zhuǎn)方向相同。由于電機(jī)旋轉(zhuǎn)導(dǎo)致的扭矩相互抵消，保證了飛行器扭矩平衡不會(huì)產(chǎn)生自旋[22]。飛行器通過(guò)控制 8個(gè)旋翼的轉(zhuǎn)速變化實(shí)現(xiàn)對(duì)飛行器6個(gè)自由度的控制。

1.1 坐標(biāo)系定義

采用大地平面假設(shè)，建立相對(duì)于地球靜止的慣性坐標(biāo)系和相對(duì)于飛行器本體的機(jī)體坐標(biāo)系[23]，具體如圖 1所示。其中機(jī)體坐標(biāo)系X軸為飛行器前進(jìn)與后退方向，軸向與電機(jī)M4和M8所在軸重合；Y與Z軸垂直；Z軸為垂直機(jī)體本身方向。采用歐拉角（俯仰角、橫滾角、偏航角）對(duì)飛行器姿態(tài)進(jìn)行描述[24]。

圖1 八旋翼飛行器系統(tǒng)坐標(biāo)系Fig.1 Eight rotor aircraft coordinate system

1.2 機(jī)載航電系統(tǒng)設(shè)計(jì)

本文設(shè)計(jì)的八旋翼飛行器機(jī)載航電系統(tǒng)包括如下 6個(gè)部分，即飛行控制器、慣性導(dǎo)航系統(tǒng)、數(shù)據(jù)傳輸系統(tǒng)、無(wú)刷電機(jī)調(diào)速器、供電系統(tǒng)以及信息采集系統(tǒng)，結(jié)構(gòu)框圖及其實(shí)物圖如圖2、圖3所示。其中飛控計(jì)算機(jī)微處理器采用意法半導(dǎo)體公司 STM32F103RCT6芯片，具有精度高、速度快、硬件性能強(qiáng)以及接口資源豐富等優(yōu)點(diǎn)。慣性導(dǎo)航系統(tǒng)采用 9軸數(shù)字運(yùn)動(dòng)處理器（digital motion processor，DMP）[25]MPU6050，相較于多組件方案，DMP免除了組合陀螺儀和加速器時(shí)之軸間差的問(wèn)題。光學(xué)圖像采集設(shè)備使用松下GH3單反相機(jī)，其搭載在八旋翼飛行器的三軸云臺(tái)上，可以采集高清、穩(wěn)定的林區(qū)低空航拍圖像。

圖2 機(jī)載航電系統(tǒng)結(jié)構(gòu)框圖Fig.2 Structure block diagram of airborne avionics system

圖3 八旋翼飛行器監(jiān)測(cè)平臺(tái)實(shí)物圖Fig.3 Practicality image of eight rotor aircraft monitoring platform

1.3 姿態(tài)控制系統(tǒng)設(shè)計(jì)

飛行器采用串級(jí)PID（proportion integration differentiation）控制算法設(shè)計(jì)飛行器控制系統(tǒng)，實(shí)現(xiàn)姿態(tài)跟蹤控制。控制器外環(huán)為角度環(huán)，內(nèi)環(huán)為角速度環(huán)。外環(huán)輸入量為飛行器歐拉角，給定角度由遙控器或串口控制終端設(shè)定，通過(guò) PID控制器得到輸出量角速度，作為指令輸入到內(nèi)環(huán)。內(nèi)環(huán)根據(jù)輸入，利用PID控制器輸出電機(jī)控制信號(hào)，通過(guò)PWM信號(hào)發(fā)生器控制槳葉電機(jī)轉(zhuǎn)速，進(jìn)而控制飛行器飛行姿態(tài)。系統(tǒng)控制結(jié)構(gòu)如圖4所示。

圖4 串級(jí)PID控制框圖Fig.4 Diagram of cascade PID control

1.4 監(jiān)測(cè)圖像采集

本文利用八旋翼飛行器針對(duì)遼寧省試驗(yàn)林區(qū)進(jìn)行實(shí)地圖像采集，該試驗(yàn)林區(qū)主要受到松毛蟲(chóng)和木蠹蛾的侵蝕，侵蝕后樹(shù)木表現(xiàn)為枯死現(xiàn)象，采集圖像及受災(zāi)現(xiàn)象如圖 5所示。通過(guò)對(duì)采集圖像質(zhì)量的綜合考慮，選取陰天多云天氣采集圖像。飛行器在監(jiān)測(cè)區(qū)域中心起飛，垂直上升過(guò)程中飛行器每隔5 m懸停5 s采集圖像。圖像依靠機(jī)載單反相機(jī)（分辨率為4 608×2 592像素）以正攝圖像[25]采集方式得到，共采集30～100 m圖像451張(其中油松林167張、沙棘地148張、紅松72張、白樺64張)，數(shù)據(jù)存儲(chǔ)大小為 2.58 G。綜合考慮圖像分割對(duì)采集圖像的像素要求，選取拍攝高度約為50 m的8張監(jiān)測(cè)圖像。

圖5 遼寧省建平縣監(jiān)測(cè)圖像效果圖Fig.5 Monitoring images effect in Jianping country,Liaoning province

2 基于復(fù)合梯度分水嶺算法的圖像分割方法

傳統(tǒng)的分水嶺圖像分割算法對(duì)噪聲比較敏感[26]，在圖像受到復(fù)雜噪聲干擾及暗紋理影響時(shí)，分割結(jié)果無(wú)法滿足需求，甚至?xí)a(chǎn)生嚴(yán)重的過(guò)分割現(xiàn)象。此外，蟲(chóng)害區(qū)域的分割圖像中往往還存在大量的非相關(guān)區(qū)域，而傳統(tǒng)分水嶺算法對(duì)此缺乏處理能力。針對(duì)以上問(wèn)題，本文提出一種基于復(fù)合梯度的分水嶺圖像分割算法對(duì)蟲(chóng)害監(jiān)測(cè)圖像進(jìn)行處理。

算法關(guān)鍵步驟如下：1）對(duì)輸入圖像進(jìn)行全局直方圖均衡化圖像增強(qiáng)處理并對(duì)其進(jìn)行形態(tài)學(xué)混合開(kāi)閉重構(gòu)濾波；2）計(jì)算預(yù)處理后的灰度圖像的復(fù)合梯度提取梯度圖像；3）利用分水嶺變換對(duì)梯度圖像進(jìn)行分割，對(duì)非林區(qū)區(qū)域進(jìn)行提??；4）在原始圖像的基礎(chǔ)上去除非林區(qū)區(qū)域，進(jìn)行灰度變換，計(jì)算灰度圖像的復(fù)合梯度提取梯度圖像；5）利用分水嶺變換對(duì)梯度圖像進(jìn)行分割提取蟲(chóng)害區(qū)域并進(jìn)行區(qū)域合并。

2.1 圖像預(yù)處理

為了有效降低噪聲與暗紋理對(duì)分割圖像的影響，針對(duì)圖像噪聲，本文采用形態(tài)學(xué)混合開(kāi)閉重構(gòu)濾波[27-28]，對(duì)圖像樣本進(jìn)行處理。形態(tài)學(xué)混合開(kāi)閉重構(gòu)濾波在降低噪聲干擾的同時(shí)還可以保持圖像中剩余連續(xù)區(qū)域的邊緣，在后續(xù)分割時(shí)不會(huì)產(chǎn)生新的輪廓邊緣[29]。

針對(duì)圖像暗紋理，本文采用全局直方圖均衡化對(duì)圖像進(jìn)行增強(qiáng)處理。具體處理過(guò)程如下：假設(shè)將圖像gzh(x,y)的灰度級(jí)r歸一化到區(qū)間[0,1]，r=0時(shí)為黑色，r=1時(shí)為白色。gzh(x,y)灰度級(jí)范圍為[0,L-1]，像素的總數(shù)為n，則有灰度級(jí)為rk的像素個(gè)數(shù)為nk。其全局直方圖均衡化對(duì)應(yīng)的變換如式（1）所示。

式中Pr(rj)為gzh(x,y)的概率分布函數(shù)，Sk即為圖像gzh(x,y)的灰度級(jí)k從0取至（L-1）時(shí)，對(duì)圖像gzh(x,y)概率分布函數(shù)Pr(rj)求和，L為圖像灰度級(jí)總數(shù)。

對(duì)經(jīng)過(guò)圖像增強(qiáng)的彩色圖像進(jìn)一步進(jìn)行灰度變換得到灰度圖像并對(duì)其進(jìn)行開(kāi)運(yùn)算并對(duì)其結(jié)果進(jìn)行閉運(yùn)算（灰度圖像6a為參考圖像，結(jié)構(gòu)元素選為正方形，尺寸為3×3像素），即可利用開(kāi)閉重構(gòu)得出濾波后的圖像。輸入圖像的灰度圖像、開(kāi)操作圖像、閉操作圖像如圖6a、6b、6c所示。

圖6 濾波過(guò)程效果圖Fig.6 Result of filtering process

2.2 非相關(guān)區(qū)域提取

圖像中非相關(guān)區(qū)域（道路及裸地）的分割結(jié)果會(huì)對(duì)蟲(chóng)害區(qū)域分割結(jié)果的提取產(chǎn)生干擾，使得分割結(jié)果不準(zhǔn)確。為解決上述問(wèn)題，本文先提取出非相關(guān)區(qū)域，再對(duì)蟲(chóng)害區(qū)域進(jìn)行分割處理。

首先對(duì)預(yù)處理后的灰度圖像進(jìn)行復(fù)合梯度的求解。通過(guò)計(jì)算灰度圖像各像素點(diǎn)的復(fù)合梯度[30]得到梯度圖像，各像素點(diǎn)的復(fù)合梯度Cg的計(jì)算如式（2）所示。

式中水平復(fù)合梯度Hg和垂直復(fù)合梯度Vg分別通過(guò)微分模板計(jì)算即可得到，如矩陣（3）所示。

水平/垂直微分模板代表相對(duì)于像素點(diǎn)水平/垂直方向 0o與 180o、45o與 135o、-45o與-135o的鄰域像素點(diǎn)灰度值差值相加后取平均值。

統(tǒng)計(jì)各梯度層頻率，根據(jù)當(dāng)前像素點(diǎn)的梯度信息將其放入排序數(shù)組中的合適位置，梯度值越低的像素點(diǎn)存放的位置越靠前，相同梯度值的點(diǎn)為一個(gè)梯度層。然后尋找圖像的極小區(qū)域 (此區(qū)域通過(guò)閾值判定) 并對(duì)其進(jìn)行標(biāo)記，區(qū)域的面積為區(qū)域中像素點(diǎn)的個(gè)數(shù)，本文選取的閾值為整幅圖像面積的1%。若同一梯度層相鄰像素點(diǎn)均已標(biāo)記且標(biāo)記相同，將 2個(gè)區(qū)域合并。去除所有小于特定像素?cái)?shù)H（此處H設(shè)為20）的斑點(diǎn)污漬。最后進(jìn)行分水嶺變換，提取出非相關(guān)區(qū)域圖像。

2.3 蟲(chóng)害區(qū)域分割提取

由于非相關(guān)區(qū)域?qū)οx(chóng)害區(qū)域提取的影響，本文采用在輸入圖像的基礎(chǔ)上去除非相關(guān)區(qū)域，所的圖像作為蟲(chóng)害區(qū)域分割提取的輸入圖像。通過(guò)對(duì)三原色 RGB（red,green, blue）、色彩模型Lab(L代表亮度Luminosity, 通道a正值為紅色，負(fù)值為綠色；通道b正值為黃色，負(fù)值為藍(lán)色)、顏色模型HSL(hue, saturation, lightness)和灰度圖像顏色空間效果的比較，采用更能直接反映圖像特征的L變量（亮度信息），也就是灰度圖像作為顏色轉(zhuǎn)換空間。通過(guò)計(jì)算灰度圖像的各像素點(diǎn)的復(fù)合梯度，進(jìn)而提取梯度圖像。采用分水嶺變換對(duì)梯度圖像進(jìn)行分割，實(shí)現(xiàn)蟲(chóng)害區(qū)域的分割提取。

2.4 區(qū)域合并

基于復(fù)合梯度的分水嶺變換所得的蟲(chóng)害提取圖像可能仍存在過(guò)分割現(xiàn)象，本文采用對(duì)分割后的圖像進(jìn)行區(qū)域合并。

首先定義區(qū)域Ci和Cj的綜合距離度量ijD如公式（4）所示。

式中ui為區(qū)域Ci的顏色均值向量，uj為區(qū)域Cj的顏色均值向量，σi為區(qū)域Ci內(nèi) 3個(gè)通道顏色均方差的均值，σj為區(qū)域Cj內(nèi) 3個(gè)通道顏色均方差的均值，Eij為區(qū)域Ci和Cj公共邊緣歸一化均值，E為所有邊緣歸一化均值。其中若Ci和Cj存在鄰接關(guān)系，且Ci和Cj的綜合距離度量Dij小于閾值參數(shù)T，則合并Ci和Cj；若所有存在鄰接關(guān)系的區(qū)域綜合距離度量均大于T，則合并結(jié)束。

2.5 蟲(chóng)害監(jiān)測(cè)圖像分割效果

以油松林為例，利用本文方法對(duì)監(jiān)測(cè)圖像進(jìn)行蟲(chóng)害區(qū)域分割提取的效果圖如圖7所示，其中對(duì)輸入圖像7a進(jìn)行圖像預(yù)處理，得到濾波圖像7b和增強(qiáng)圖像7c；基于復(fù)合梯度實(shí)現(xiàn)非相關(guān)區(qū)域圖像7d的提取，如圖中白色區(qū)域所示；在圖像7a的基礎(chǔ)上去除非相關(guān)區(qū)域圖像7d并將其轉(zhuǎn)化為灰度圖像 7e，初步顯現(xiàn)出蟲(chóng)害區(qū)域范圍；計(jì)算灰度圖像7e的復(fù)合梯度得到梯度圖像7f，使得蟲(chóng)害區(qū)域更加明顯；利用分水嶺變換對(duì)圖像7f進(jìn)行分割得到蟲(chóng)害區(qū)域，如圖7g所示；最后將非相關(guān)區(qū)域圖像7d與蟲(chóng)害區(qū)域提取圖像7g進(jìn)行區(qū)域合并得到最終分割效果，如圖7h所示。

圖7 蟲(chóng)害監(jiān)測(cè)圖像分割效果圖Fig.7 Segmentation effect of pest monitoring image

3 試驗(yàn)結(jié)果與分析

3.1 不同算法對(duì)蟲(chóng)害監(jiān)測(cè)圖像分割效果

本文選取蟲(chóng)害侵蝕程度不同的監(jiān)測(cè)圖像作為測(cè)試樣本，侵蝕程度如圖 8中不同圖像樹(shù)木枯死面積所示。將本文提出的復(fù)合梯度分水嶺算法與傳統(tǒng)分水嶺算法、K-means聚類分割算法的蟲(chóng)害區(qū)域分割的提取效果進(jìn)行了比較，結(jié)果如圖 8所示。其中傳統(tǒng)分水嶺算法在分水嶺變換的基礎(chǔ)上采用形態(tài)學(xué)混合開(kāi)閉重構(gòu)濾波對(duì)分割圖像進(jìn)行去噪；聚類分割算法采用K-means聚類算法，參數(shù)K設(shè)定為20。本文借助圖像拆分器通過(guò)手動(dòng)操作方法標(biāo)注出蟲(chóng)害位置，計(jì)算蟲(chóng)害區(qū)域比例作為參照真值。

上述 8組試驗(yàn)結(jié)果表明，傳統(tǒng)分水嶺算法得到的蟲(chóng)害區(qū)域明顯大于真實(shí)情況，錯(cuò)誤率較高，同時(shí)K-means聚類分割算法能夠有效提取出蟲(chóng)害區(qū)域，但會(huì)摻雜非相關(guān)區(qū)域造成誤報(bào)。而本文算法得到的結(jié)果與人工篩選結(jié)果近似，能夠有效地提取出蟲(chóng)害區(qū)域與非林區(qū)區(qū)域，從而計(jì)算出健康區(qū)域、蟲(chóng)害區(qū)域、非相關(guān)區(qū)域在整幅圖像中的占比。

3.2 算法性能評(píng)價(jià)與分析

為了驗(yàn)證本文算法的有效性，采用相對(duì)誤差率和相對(duì)極限測(cè)量精度等作為評(píng)價(jià)指標(biāo)[31]。相對(duì)誤差率ξ用于描述分割目標(biāo)和背景之間的誤分辨率。

式中Ni為分割處理后區(qū)域i的實(shí)際像素點(diǎn)數(shù)；Ni*為區(qū)域i實(shí)際目標(biāo)像素點(diǎn)數(shù)；N為一幅圖像實(shí)際像素點(diǎn)數(shù)；c為區(qū)域分割所得的總區(qū)域數(shù)。相對(duì)誤差率的高低可以表征分割結(jié)果的好壞。

圖8 蟲(chóng)害監(jiān)測(cè)圖像分割效果對(duì)比圖Fig.8 Segmentation effect comparison of pest monitoring image

相對(duì)極限測(cè)量精度σ用于表示目標(biāo)區(qū)域相對(duì)參照真值的偏離程度，表征圖像分割性能。

式中w為原圖像中待分割區(qū)域?qū)嶋H像素點(diǎn)數(shù)，即為參照真值；wm為采用分割算法分割后所得目標(biāo)區(qū)域像素點(diǎn)數(shù)。σ的值越小，分割性能越好。

針對(duì)前面所述的 8幅蟲(chóng)害監(jiān)測(cè)圖像樣本，參照真值采用借助圖像拆分器人工標(biāo)注所計(jì)算出的蟲(chóng)害比例。本文算法與傳統(tǒng)分水嶺算法、K-means聚類算法分割結(jié)果的相對(duì)誤差率及相對(duì)測(cè)量精度結(jié)果如表1所示。

表1 相對(duì)誤差率與相對(duì)極限測(cè)量精度測(cè)試結(jié)果Table 1 Results of relative error rate and relative ultimate measurement accuracy%

通過(guò)對(duì)以上試驗(yàn)數(shù)據(jù)的分析，采用傳統(tǒng)分水嶺算法的平均相對(duì)誤差率為 8.19%，平均相對(duì)極限測(cè)量精度為8.50%；K-means聚類算法的平均相對(duì)誤差率為4.80%，平均相對(duì)測(cè)量精度為3.72%。而采用本文算法的平均相對(duì)誤差率為1.63%，平均相對(duì)測(cè)量精度為1.31%。試驗(yàn)結(jié)果表明，本文算法對(duì)監(jiān)測(cè)圖像蟲(chóng)害區(qū)域分割提取結(jié)果優(yōu)于傳統(tǒng)分水嶺算法、K-means聚類分割算法，其中相對(duì)誤差率平均降低 6.56%和 3.17%，相對(duì)極限測(cè)量精度平均改善7.19%和2.41%。

4 結(jié) 論

本文在搭建面向林區(qū)蟲(chóng)害監(jiān)測(cè)的多旋翼無(wú)人飛行器航拍監(jiān)測(cè)系統(tǒng)的基礎(chǔ)上，實(shí)現(xiàn)了對(duì)林區(qū)蟲(chóng)害信息實(shí)時(shí)、高效采集。針對(duì)林業(yè)航拍監(jiān)測(cè)圖像，提出了一種基于復(fù)合梯度分水嶺算法的多旋翼無(wú)人飛行器林業(yè)蟲(chóng)害監(jiān)測(cè)圖像的分割方法。利用本文所提算法對(duì)遼寧省試驗(yàn)林區(qū)實(shí)地采集到的蟲(chóng)害監(jiān)測(cè)圖像進(jìn)行處理，并從分割效果、相對(duì)誤差率及相對(duì)極限測(cè)量精度 3個(gè)方面與傳統(tǒng)分水嶺算法、K-means聚類算法進(jìn)行比較。試驗(yàn)結(jié)果表明，本文算法對(duì)監(jiān)測(cè)圖像蟲(chóng)害區(qū)域分割提取結(jié)果優(yōu)于傳統(tǒng)分水嶺算法、K-means聚類分割算法，其中相對(duì)誤差率平均降低6.56%和3.17%，相對(duì)極限測(cè)量精度平均改善7.19%和2.41%。

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Image segmentation method for forestry unmanned aerial vehicle pest monitoring based on composite gradient watershed algorithm

Zhang Junguo1, Feng Wenzhao1, Hu Chunhe1, Luo Youqing2※
(1. School of Technology, Beijing Forestry University, Beijing100083,China;2.School of Forestry, Beijing Forestry University, Beijing100083,China)

The application of multi-rotor unmanned aerial vehicle monitoring system for forest pest information collecting has many advantages, such as low running cost, operating flexibility, easy access to data, high image resolution etc. It has been regarded as a quick access to forest insect pest information collecting. By use of unmanned aerial vehicle system, valid segmentation and extraction of pest images acquired with the help of multi-rotor unmanned aerial vehicle can be used to calculate the insect pest proportion in monitored sample field. It can provide forest conservation experts with evidence for assessing the insect pest damage. To conduct forest monitoring work and calculate the proportion of pest infested area in monitored sample field with more preciseness and fast turnaround, in this paper, we aimed to solve poor time response circle and limited monitoring range problems that exist in current forestry information monitoring method. Firstly, in this paper, we built both hardware and software systems of multi-rotor unmanned aerial vehicle. Aerial vehicle equipped with image collecting devices was used to monitor in forestry pest insect infested area and collect data in the Liaoning testing forest. In order to obtain proper resolution images, aerial vehicle took off the center of the chosen monitoring area vertically to collect photo resources. By considering needed resolution requirements on image segmentation comprehensively, the height of about 50 m was chosen for image acquisition. On the analytical basis of monitoring images, an image segmentation method based on composite gradient watershed algorithm was proposed. This method introduced global histogram equalization to eliminate the influence of dark texture and adopted the morphological hybrid open-closing reconstruction filter to complete the denoising work of the image samples, eliminate the image interference to the segmentation effect, and suppress the over-segmentation phenomenon in image segmentation process. The gray-scale image was obtained by gray-scale transformation of the pre-processed image. The non-correlation regions (road and bare ground) were extracted by calculating the composite gradient of each pixel point in the gray image. Interference to the segmentation result may arise in segmenting process due to the similar color of non-correlation region and pest insect infested area. In this paper, the mentioned region was removed from the original image, which greatly avoided the interference of the non-related region to the pest area and ensured the accuracy of the result. Finally, the watershed algorithm was applied to realize the segmentation and extraction of insect pest area in images. In order to verify the effectiveness of the proposed method, the traditional watershed algorithm and K-means clustering algorithm were used for comparing experiment methods in the segmentation of eight images with different levels of insect pest. With the help of mage segmentation device, the accurate pest insect infested area was labeled manually, and it was taken as reference value in pest insect proportion calculating step. The experiment result showed that the segmentation effect was much more similar to the manual operation result. Specifically, the relative error rate decreased by 6.56% and 3.17% and the relative limit measuring accuracy was improved by 7.19% and 2.41% in this proposed method when traditional watershed algorithm was compared withK-means cluster algorithm. Our result showed that multi-rotor unmanned aerial vehicle was helpful in real time and effective monitoring of forest pest insect. The algorithm proposed in this paper was able to accurately segment and extract pest insect area in monitoring images and the proportion of pest area in whole sample fields was acquired, thus providing valid data support for forest pest monitoring and preventing work in the future.

unmanned aerial vehicles; algorithms; monitoring; image segmentation; composite gradient

10.11975/j.issn.1002-6819.2017.14.013

TP391.4; S126

A

1002-6819(2017)-14-0093-07

張軍國(guó)，馮文釗，胡春鶴，駱有慶. 無(wú)人機(jī)航拍林業(yè)蟲(chóng)害圖像分割復(fù)合梯度分水嶺算法[J]. 農(nóng)業(yè)工程學(xué)報(bào)，2017，33(14)：93－99.

10.11975/j.issn.1002-6819.2017.14.013 http://www.tcsae.org

Zhang Junguo, Feng Wenzhao, Hu Chunhe, Luo Youqing. Image segmentation method for forestry unmanned aerial vehicle pest monitoring based on composite gradient watershed algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE), 2017, 33(14): 93－99. (in Chinese with English abstract)

doi：10.11975/j.issn.1002-6819.2017.14.013 http://www.tcsae.org

2017-03-12

2017-05-15

林業(yè)公益性行業(yè)科研專項(xiàng)資助（201404401）；國(guó)家自然科學(xué)基金項(xiàng)目資助（31670553）；中央高?；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金資助（2016ZCQ08）

張軍國(guó)，教授，博士生導(dǎo)師，主要從事圖像處理與人工智能研究。北京 北京林業(yè)大學(xué)工學(xué)院，100083。Email：zhangjunguo@bjfu.edu.cn

※通信作者：駱有慶，教授，博士生導(dǎo)師，主要從事森林有害生物可持續(xù)控制研究。北京 北京林業(yè)大學(xué)林學(xué)院，100083。Email：yqluo@bjfu.edu.cn