GEE 遙感數字圖像處理數字圖像分析是指利用計算機對數字圖像進行處理。數字圖像分析的領域很廣泛，但從根本上講，它側重于使用算法修改圖像中的像素值，以提取信息或增強後期處理。本實驗包括對比度處理、平滑和邊緣提取等。目標：• Grey-level thresholding 灰度分割• Level slicing 水平切片• Image convolution using boxcar kernel 圖像卷積• Edge detection 邊緣檢測• Pan sharpening 全色銳化• Texture analysis 紋理分析1 選擇影像//Create a variable to look at the Landsat 8 Top-of-atmosphere image collectionvar l8 = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA'); //Filter the image collection spatially to select images that include the point createdvar spatialFiltered = l8.filterBounds(geometry);//Narrow the data range of interest.var temporalFiltered = spatialFiltered.filterDate('2017-01-01'錛 '2018-12-31');// This may still lead to a large number of scenes so sort from least to most cloudy.var sorted = temporalFiltered.sort('CLOUD_COVER');// And then select the first (i.e. least cloudy) image.var bestscene = ee.Image(sorted.first());/*If you print the selected scene to the console you can see the image detailsincluding the id錛 which can be used to directly select that image in the future */print('Best scene'錛 bestscene);//Center map display on the point created with a zoom level of 12Map.centerObject(geometry錛12);//Display image as color infrared composite.Map.addLayer(bestscene錛 {bands: ['B5'錛 'B4'錛 'B3']錛 max: 0.5}錛 'input image');2 進行圖像增強圖像增強的主要目的是通過增加場景中特征之間的區別來提高圖像的可解釋性。圖像增強通常是在圖像預處理後進行的。圖像增強技術主要包括對比度處理、空間特征處理和多圖像處理。Grey-level thresholding 灰度分割Grey-level thresholding 被用于分割圖像，在下面的示例中，根據在一個圖像波段中定義的閾值從圖像中分離去除水像素。//Import a top-of-atmosphere reflectance Landsat 5 image.var image = ee.Image('LANDSAT/LT05/C01/T1_TOA/LT05_015030_20100531');//Center map display on Syracuse with a zoom level of 12Map.setCenter(-76.1467錛 43.0458錛12);//Display image as color infrared composite.Map.addLayer(image錛 {bands: ['B4'錛 'B3'錛 'B2']錛 max: 0.5}錛 'input image');//Define a study area that captures Onondaga Lake and the surrounding areavar studyarea = ee.Geometry.Rectangle(-76.454錛 43.692錛-76.40錛 42.82);//Select out the NIR band from the input imagevar NIRband = image.select('B4');//Create a histogram of the spectral response in the NIR band using the geometry identifiedvar histogram2 = ui.Chart.image.histogram(NIRband錛 studyarea);// Display the histogram.print(histogram2);//Create a binary mask to filter out water based on the reflectance informationvar watermask = NIRband.gte(0.07);//Use the water mask to create an image with water pixels removed.var nowaterimage= image.updateMask(watermask);//Display the output water-masked image using a CIR compositeMap.addLayer(nowaterimage錛 {bands: ['B4'錛 'B3'錛 'B2']錛 max: 0.5}錛 'water-masked');近紅外波段直方圖Level Slicing 水平切片水平切片是一種增強技術，將沿圖像直方圖x軸分布的DNs或反射值劃分為一系列的間隔(即切片)。這項技術是通過為每個數據間隔分配不同的顏色來實現的。//Load and display a top-of-atmosphere reflectance Landsat 5 imagevar image = ee.Image('LANDSAT/LT05/C01/T1_TOA/LT05_015030_20100531');//Center map display on Syracuse with a zoom level of 12Map.setCenter(-76.1467錛 43.0458錛12);//Display image as color infrared composite.Map.addLayer(image錛 {bands: ['B4'錛 'B3'錛 'B2']錛 max: 0.5}錛 'input image');//Create an NDWI image using band 4 (NIR) and 5 (mid-IR) from the selected Landsat 5 image錛var ndwi = image.normalizedDifference(['B4'錛 'B5']); //create a NDWI image/*Create an image visualization parameter to recolor the NDWI image based on pixels values:NDWI = 0錛 display as white (FFFFFF color)NDWI 0 & 0.5錛 display in cyan (00FFFF color)NDWI =0.5 & = 1錛 display as blue (0000FF color)Higher NDWI suggest higher water content錛 the three slices are represented with different colorsthat show no water content錛 low water content and high water content pixels.*/var ndwislices = ndwi.expression("(b('nd') 0) ? 3" + //nd refer to the values of each pixel in the NDWI image": (b('nd') 0.5) ? 2" +": (b('nd') 1) ? 1" +": 0");//Display final image based on level sliced image.Map.addLayer(ndwislices錛{min: 0錛 max: 3錛 palette: ["0000FF"錛"00FFFF"錛"FFFFFF"]}錛'NDWI Slices');Image Convolution 圖像卷積圖像卷積，也稱為鄰域濾波，利用給定像素附近的像素值來確定其最終輸出值。除了執行局部色調調整，卷積還可以用于過濾圖像，以平滑、銳化細節、突出邊緣或去除噪聲(Szeliski 2011)。本文使用低通濾波器平滑圖像。低通濾波器的設計目的是強調圖像的低光譜頻率特征(亮度的大範圍變化)和減少高頻成分(局部細節)的表現。//Load and display a top-of-atmosphere reflectance Landsat 5 imagevar image = ee.Image('LANDSAT/LT05/C01/T1_TOA/LT05_015030_20100531');//Center map display on Syracuse with a zoom level of 12Map.setCenter(-76.1467錛 43.0458錛12);//Display image as color infrared composite.Map.addLayer(image錛 {bands: ['B4'錛 'B3'錛 'B2']錛 max: 0.5}錛 'input image');/*Define a low-pass kernel. This kernel creates a 7×7 square錛 the normalize parameter means thesum of values in the kernel will be 1 */var lowpass = ee.Kernel.square({radius: 7錛 units: 'pixels'錛 normalize: true});//Smooth the image by convolving with the low-pass kernel.var smooth = image.convolve(lowpass);Map.addLayer(smooth錛 {bands: ['B4'錛 'B3'錛 'B2']錛 max: 0.5}錛 'smoothed');Edge Detection/Enhancement 邊緣檢測/增強邊緣檢測或增強試圖同時保留局部對比度和低頻亮度信息。Canny邊緣檢測算法(Canny 1986)使用四個單獨的過濾器來識別對角線、垂直和水平邊緣。計算提取水平方向和垂直方向的一階導數值，計算梯度大小。較小幅度的梯度被抑制。對于從一個邊緣檢測器提取線，GEE嵌入了霍夫變換(Duda和Hart 1972)。/*Load a Landsat 8 image and select only the panchromatic band. The panchromatic bandincludes reflectance in the 0.503C0.676 μm wavelength range with a ground sample distance of15 m. */var image = ee.Image('LANDSAT/LC08/C01/T1/LC08_015030_20140307').select("B8");//Center map display on Syracuse with a zoom level of 12Map.setCenter(-76.1467錛 43.0458錛12);//Display image as color infrared composite.Map.addLayer(image錛 ''錛 'input image');/* Perform Canny edge detection and display the result. This function uses the threshold parameterto determine the minimum gradient magnitude; the sigma parameter is the standard deviation (SD)of a Gaussian pre-filter to remove high-frequency noise. A sigma of 0 means apply no filtering.*/var canny = ee.Algorithms.CannyEdgeDetector({image: image錛 threshold: 200錛 sigma: 1 });//Display the outcome of the canny edge detection.Map.addLayer(canny錛 {}錛 'canny');/* Perform a Hough transform of the Canny result. In this function gridSize means size of grid foranalysis (default is 256)錛 inputThreshold is the value threshold for the input image (pixels at orabove this value are considered active) and lineThreshold is the threshold for line detection(values at or above this threshold on the Hough transform are considered detected lines). Each ofthese parameters can be determined experimentally. The defaults provided in GEE for theparameters are intended as a starting point for use with unsigned 8-bit integers.*/var hough = ee.Algorithms.HoughTransform(canny錛 256錛 7000錛 80);//Display the outcome of the Hough transformed canny edge detection.Map.addLayer(hough錛 {}錛 'hough');Pan Sharpening using HSV space 全色銳化全色銳化技術用于合並全色(高空間、低光譜分辨率)和多光譜(低空間、高光譜分辨率)圖像。全色銳化的一種方法是通過RGB和強度-色調-飽和度(IHS)或色調-飽和度-值(HSV)空間之間的轉換。// Import a Landsat 8 top-of-atmosphere reflectance imagevar image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_015030_20140307');//Display image as normal color composite.Map.addLayer(image錛{bands: ['B4'錛 'B3'錛 'B2']}錛 'RGB image');// Convert the visible bands from the input image to HSV color space.var hsv = image.select(['B4'錛 'B3'錛 'B2']).rgbToHsv();/* Create a new image by concatenating the hue and saturation bands (HSV image) with the panchromatic band from the original image and converting back to RGB. *///Concatenate bandsvar combined = ee.Image.cat([hsv.select('hue')錛 hsv.select('saturation')錛 image.select('B8')]);//Convert back to RGB spacevar sharpened = ee.Image(combined).hsvToRgb();//Set the map display to center at City of Syracuse and a zoom level of 14Map.setCenter(-76.14錛 43.04錛14);// Display the pan-sharpened result.Map.addLayer(sharpened錛''錛'pan-sharpened');銳化前銳化後Performing visual interpretation through texture analysis 目視解譯和紋理分析紋理是指圖像上色調變化的頻率。它是由可能太小而無法在圖像內單獨識別的單元特征聚合而產生的。一個例子是綠色草地的光滑紋理與航空照片上的綠色樹冠的粗糙紋理。谷歌地球引擎有多種方法來計算紋理。其中之一是熵，

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它提供了一個空間無序度的度量。//Import a high-resolution NAIP image.var image = ee.Image('USDA/NAIP/DOQQ/m_4307663_se_18_1_20130619');//Zoom map to Oakwood Cemetary at level 16 and display input image.Map.setCenter(-76.13錛 43.03錛16);//The catch with entropy is that it needs discrete values錛 hence the image is rescaled.Map.addLayer(image錛 {max: 255}錛 'Input image');//Select NIR band from input image.var nir = image.select('N');//Define size and shape of neighborhood for deriving the texture information.var square = ee.Kernel.square({radius: 3});//Compute entropy (texture) using the defined neighborhood information.var entropy = nir.entropy(square);//Display computed image texture outcome.Map.addLayer(entropy錛{min: 1錛 max: 5錛 palette: ['0000CC'錛 'CC0000']}錛 'entropy');【參考】https://zhuanlan.zhihu.com/p/149409447?from_voters_page=truehttps://link.zhihu.com/?target=https%3A//drive.google.com/file/d/1YisEcRvz1YCnFimTreVd-CZk3xAoXvKx/viewLab 3 C Image processing techniques using Google Earth Engine.pdf點滴分享，福澤你我！Add oil！徒：這是我們的邏輯課上Aristote給學生出的一道有爭議的邏輯題：1，習題：“赫拉克勒斯（Hercule）向斯芬克司（Sphinx）問路”赫拉克勒斯走到在一個岔路口，有左右二條路：一條是“正義”之路，一條是“邪惡”之路，赫拉克勒斯要走“正義”之路，但他無法辨別，然而斯芬克司能辨別，卻有時說真話，有時說假話。現在赫拉克勒斯只能問斯芬克司一個“問題”，使得斯芬克司的回答能使赫拉克勒斯辨別“正義”之路，問此“問題”是什麼？2，解答（1）定義二個原子命題：p1: “正義”之路在右邊；p2: 斯芬克司說真話。（2）建立真值表───────────p1p2AB00 1001 0010 0111 11───────────A = p1-p2B = (p1-p2)-p2A是斯芬克司的回答，當赫拉克勒斯問： “正義”之路在右邊嗎？B是斯芬克司的回答，當赫拉克勒斯問： “正義”之路在右邊當且僅當你說真話嗎？于是，赫拉克勒斯問斯芬克司的“問題”是： 正義之路在右邊當且僅當你說真話嗎？由此可見，“命題”可看成是“答案”的表達，

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于是，“問題”和“命題”是二個互補的概念，而且“問題”更本源，也就是說，

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有“問題”才有“命題”。而在古典邏輯或者命題邏輯中，“命題（proposition）”是作本源概念介紹的，推理等都圍繞著命題展開，基本不提“問題”，。。。師：你提的問題很好，命題和問題是不同的層次，“問題”是人的認知層次，“命題”是邏輯判斷層次，問題關注內容和意義，命題只考慮形式的正確性，所以在邏輯學中不會有“問題”，但邏輯自身的正確性卻是邏輯學自身不能回答的，這就上升到哲學，維特根斯坦就是在這個意義上展開他的理論的，他獨立地開始了語言與邏輯的關系的哲學研究，他的書名表現了這一點──“邏輯哲學論”，他的開闢的道路雖然有很多學者接續，但迄今未得到更深度的發展。徒：是的，在這之前，給學生講“命題”時就一直覺得有點別扭，“命題”一般定義為可判斷真假的陳述句，這樣的定義在做形式推導習題時還說得過去，可是遇到應用題，比如，上述“赫拉克勒斯向斯芬克司問路”習題，“命題”與“問題”的關系就出來了，如果不討論此關系，諸如此類的習題是根本講不清楚的。我確實想到了維特根斯坦！我回頭去看了他的“邏輯哲學論”，以前此書大致看了看，但沒留下什麼特別的印象，這次再看，感覺就不一樣了，

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發覺維特根斯坦通篇都在討論什麼是“命題”，我隨便摘錄幾段：The proposition is a picture of reality.The proposition is a model of the reality as we think it is.The proposition shows its sense.The proposition shows how things stand錛 if it is true. And it says錛 that they do so stand.The proposition determines reality to this extent錛 that one only needs to say “Yes” or “No” to it to make it agree with reality.It must therefore be completely described by the proposition. A proposition is the description of a fact.As the description of an object describes it by its external properties so propositions describe reality by its internal properties.The proposition constructs a world with the help of a logical scaffolding錛 and therefore one can actually see in the proposition all the logical features possessed by reality if it is true. One can draw conclusions from a false proposition.To understand a proposition means to know what is the case錛 if it is true.，。。。看維特根斯坦多苦惱，他反反復復講“命題”，但就是沒說出“命題”是對“問題”的回答！怪不得，“闡釋《邏輯哲學論》的書不計其數，但真正能讀懂此書的人屈指可數。甚至羅素也承認，在讀了幾遍後，書中一些重要觀點他仍無法把握。維特根斯坦本人也曾經講過自己的一個噩夢：人們無法把握他的思想，而他也無法將思想解釋清楚。－http://www.hqrw.com.cn/2015/0209/16082_2.shtml”在思考“命題和問題”的關系時，我又一次深刻體會到了“中國陰陽互補原理”，

小昆凌

。。。師：我過去跟你說過“問題”和雙重括號，還要你不用“問題”這個來詞講話試試，就是強化在表達上形式與意義的區分，比如“正義之路”是括號強調，“正義之路”問題就是雙重括號：“’正義之路’”，相當于“所謂’正義之路’”，所以使用“問題”這個詞時，

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就總是深了一個層次，也就是說，是關乎意義的強調。維特根斯坦的貢獻就在這種區別與分離，他從直覺的事實中分離出“原子事實”（“事態”或“原子命題”），這個加了括號的“命題”就從事實中分離出來了，但這又成為一種認知和表達上的困境，

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……你現在把握住了“問題”和“命題”的區別在于，前者總是人的，後者是作為表達工具形式的，但這兩者的關系卻一直是邏輯哲學的語言哲學中的困難問題，要進一步理清這兩者的關系還有待時日，與同學的討論對你是一種助進，但要注意，總會有難以擺脫的“學而不思則罔，思而不學則殆”的問題，有了基本的學識就要建設嚴格的論域，不能放任漫無邊際思想，否則不能取得學術上真正的進步。，