Manitoba: 6-month SPI Analysis

Red represents dry, blue for moisture.

Extract the lengths of roots from the images

File. 2nd. 3rd.

MATLAB: Layer Indices Calculation

#NDVI [2024-07-12] file.

#NDVI/ExG/GRVI/Edge/HSV/XYZ/NTSC/Lab/YCbCr [2024-07-18] file.

#NDVI/GNDVI/NDRE/ExG/GRVI/Edge/HSV/XYZ/NTSC/Lab/YCbCr [2024-08-27] file.

MATLAB: Row Detection Collections

[2024-07-11]files.

MATLAB: Row Detection on the Image for Segmentation Using Hough Transform

file

[1] María Pérez-Ortiz, José Manuel Peña, Pedro Antonio Gutiérrez, Jorge Torres-Sánchez, César Hervás-Martínez, Francisca López-Granados. Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery. Expert Systems with Applications, Volume 47, 2016, 85-94.

[2] Hough Lines Transform Explained. 2017-06-05.

[3] 霍夫变换(Hough Transform)详解（附代码）. 2023-07-21.

eCognition: Multiband Composite

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% Created by Author % Version 1.0 % June 12, 2024 % If you have any question about this code, % please do not hesitate to contact me via E-mail: % jeremy456@163.com % Blog: % http://blog.sciencenet.cn/u/lixujeremy % http://lixuworld.blogspot.com/ clear; clc; timebegin=tic; cur_data=date; cur_time=fix(clock); str1=sprintf('%s %.2d:%.2d:%.2d', cur_data, cur_time(4), cur_time(5), cur_time(6)); fprintf('Time Begin: '); fprintf(str1); fprintf('\n'); filepath='S2A_tile_20180201_18PVS.tif'; image=imread(filepath); image=uint8(255.*rescale(image)); stdimage=double(image); rgb=image(:, :, 1:3); rgb=imadjust(rgb, stretchlim(rgb), []); % imshow(rgb); % Case_1: r(1/4)g(2)b(3) c1_1=(stdimage(:, :, 1)+stdimage(:, :, 4))./2; c1_1=uint8(c1_1); c1=cat(3, c1_1, image(:, :, 2), image(:, :, 3)); c1=imadjust(c1, stretchlim(c1), []); % imshow(c1); % Case_2: r(3)g(4)b(1/4) c2_1=(stdimage(:, :, 1)+stdimage(:, :, 4))./2; c2_1=uint8(c2_1); c2=cat(3, image(:, :, 3), image(:, :, 4), c2_1); c2=imadjust(c2, stretchlim(c2), []); % imshow(c2); % Case_3: r(2)g(3)b(3/4) c3_3=(stdimage(:, :, 3)+stdimage(:, :, 4))./2; c3_3=uint8(c3_3); c3=cat(3, image(:, :, 2), image(:, :, 3), c3_3); c3=imadjust(c3, stretchlim(c3), []); % imshow(c3); comimage=[rgb, c1; c2, c3]; imwrite(comimage, 'comimage.png'); fprintf('Time Begin: '); fprintf(str1); fprintf('\n'); cur_data=date; cur_time=fix(clock); str2=sprintf('%s %.2d:%.2d:%.2d', cur_data, cur_time(4), cur_time(5), cur_time(6)); fprintf('Time End: '); disp(str2); timespan=toc(timebegin); fprintf('Time Span: %.4f s\n', timespan); disp('**********************************************************');

[1] Original File: Getting Started 1 of 4: Create a project. 2021-04-26

ML: The code of K-Nearest Neighbors

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% Created by LI Xu % Version 1.0 % May 31, 2024 % If you have any question about this code, % please do not hesitate to contact me via E-mail: % jeremy456@163.com % Blog: % http://blog.sciencenet.cn/u/lixujeremy % http://lixuworld.blogspot.com/ clear; clc; timebegin=tic; cur_data=date; cur_time=fix(clock); str1=sprintf('%s %.2d:%.2d:%.2d', cur_data, cur_time(4), cur_time(5), cur_time(6)); fprintf('Time Begin: '); fprintf(str1); fprintf('\n'); % settings******************************************** % BackValue BackVal=0; fieldname='id'; cropname='canola'; % *************************************************** % Source Directory SouDir='input'; % Destination Directory DesDir='output'; % All images files=dir(fullfile(SouDir, "*.tif")); txtpath=fullfile(DesDir, ['note.txt']); fid=fopen(txtpath, 'w', 'n', 'US-ASCII'); % Loop for ii=1:numel(files) filename=files(ii).name; filepath=fullfile(SouDir, filename); [~, fname, ext]=fileparts(filename); cr_folder=fullfile(DesDir, fname); strname=strsplit(fname, '_'); strname=strname(end-2:end); strname=strjoin(strname, '_'); if ~isfolder(cr_folder) mkdir(cr_folder); else cmd_rmdir(cr_folder); mkdir(cr_folder); end rows=['shapefiles\BL4rows_']; plots=['shapefiles\weeds_grassy_']; wfplots=['shapefiles\wf_grassy_']; samples=['shapefiles\BL_']; rows=[rows, strname, '.shp']; plots=[plots, strname, '.shp']; wfplots=[wfplots, strname, '.shp']; samples=[samples, strname, '.tif']; disp(['[', num2str(ii), '\', num2str(numel(files)), ']~', filename]); fprintf(fid, '%s\r\n', ['[', num2str(ii), '\', num2str(numel(files)), ']~', filename]); % Clip the BL/G community from the image simagepath=fullfile(cr_folder, ['BL_', strname, '.tif']); GenClip(rows, filepath, 0, simagepath); % Convert to the ordinary image tifpath=GenOrdTifImage(simagepath); % Create the Mask maskpaths=GenCompMark(simagepath, samples); end fprintf('Time Begin: '); fprintf(str1); fprintf('\n'); cur_data=date; cur_time=fix(clock); str2=sprintf('%s %.2d:%.2d:%.2d', cur_data, cur_time(4), cur_time(5), cur_time(6)); fprintf('Time End: '); disp(str2); timespan=toc(timebegin); fprintf('Time Span: %.4f s\n', timespan); disp('*******************************************************************'); function tifpath=GenOrdTifImage(inputpath) [srcdir, fname, ~]=fileparts(inputpath); tifpath=fullfile(srcdir, [fname, '_ordinary.tif']); image=imread(inputpath); imwrite(image, tifpath); end function GenClip(oneshp, filepath, BackVal, otpath) [~, fname, ~]=fileparts(oneshp); strcmd=['gdalwarp -of GTiff -cutline ', oneshp, ' -cl ', fname, ' -crop_to_cutline ']; strcmd=[strcmd, '-dstnodata ' num2str(BackVal),' ', filepath, ' ', otpath]; [~, cmdout]=system(strcmd); end function maskpaths=GenCompMark(filepath, samples) xlspath='classes.sets.xlsx'; % Color plate uniValues=readcell(xlspath, 'Sheet', 'colorplate'); uniValues(1, :)=[]; uniValues(:, 1)=[]; [soudir, fname, ~]=fileparts(filepath); maskpaths=fullfile(soudir, [fname, '_mask.tif']); sampimage=imread(samples); try [image, geo]=readgeoraster(filepath); try info=geotiffinfo(filepath); catch info=georasterinfo(filepath); end catch image=imread(filepath); end sampimage=GenFalse(sampimage); %% Use nearest neighbor classifier mask=GenNearestNeighborClass(image, sampimage); % rendering the mask outMat=zeros(size(mask, 1), size(mask, 2), 3); outMat=RenderUniValues(mask, uniValues, outMat); showmat=[image; outMat]; % imshow(showmat); knn_euclidean=fullfile(soudir, [fname, '_knn_euclidean.png']); imwrite(showmat, knn_euclidean); try geotiffwrite(maskpaths, uint8(mask), geo); catch strcmd=['gdalinfo ', filepath]; [~, cmdout]=system(strcmd); epsg=extractBetween(cmdout, "EPSG:"," got from GeoTIFF keys"); epsg=epsg{1}; % geotiffwrite(otpath, uint8(class_imag), geo, 'GeoKeyDirectoryTag', info.GeoTIFFTags.GeoKeyDirectoryTag); geotiffwrite(maskpaths, uint8(mask), geo, 'CoordRefSysCode', ['EPSG:', epsg]); end end function output=GenFalse(input) output=[]; [rows, cols, ~]=size(input); values=unique(input(:)); values(values>=100)=[]; sample_regions=false([rows, cols, numel(values)]); % Loop to assign the matrixs for ii=1:numel(values) val=values(ii); index=find(input==val); band=sample_regions(:, :, ii); band(index)=1; sample_regions(:, :, ii)=band; end output=sample_regions; end function mask=GenNearestNeighborClass(image, sampimage) [rows, cols, ~]=size(image); % Enhance the image************************** ycbcr=rgb2ycbcr(image); ycbcr(:, :, 1)=0; ycbcr=imadjust(ycbcr, stretchlim(ycbcr), []); % imwrite(ycbcr, 'ycbcr.tif'); % ***************************************** % classes={'soil', 'canola', 'soybean'}; % nClasses=numel(classes); nClasses=size(sampimage, 3); % sample_regions=false([rows, cols, nClasses]); sample_regions=sampimage; mask=[]; % select each sample region % figure; % imshow(image); % f=figure; % for ii=1:nClasses % set(f, 'name', ['Select sample region for ', classes{ii}]); % sample_regions(:, :, ii)=roipoly(image); % end % % close(f); % Convert RGB to L*a*b colorspace lab=rgb2lab(image); % Calcualate the mean 'a*' and 'b*' value for each ROI area % a=lab(:, :, 2); % b=lab(:, :, 3); a=ycbcr(:, :, 2); b=ycbcr(:, :, 3); color_markers=repmat(0, [nClasses, 2]); for count=1:nClasses color_markers(count, 1)=mean2(a(sample_regions(:, :, count))); color_markers(count, 2)=mean2(b(sample_regions(:, :, count))); end % https://www.youtube.com/watch?v=3hEvcyCJNRc&list=PLEo-jHOqGNyUWoCSD3l3V-FjX9PnHvx5n&index=33 % Classify each pixel using the nearest neighbor rule % Each class marker now has an 'a*' and 'b*' value. % You can classify each pixel in the |lab_x| image by calculating the % Euclidean distance bewteen that pixel and each marker. The smallest % distance will tell you that the pixel most closely matched that % marker. For example, if the distance between a pixel and the read % color marker is the smallest, then the pixel would be labeled as a % red pixel. color_labels=0:nClasses-1; a=double(a); b=double(b); distance=repmat(0, [size(a), nClasses]); % Perform classification for count=1:nClasses distance(:, :, count)=((a-color_markers(count, 1)).^2+... (b-color_markers(count, 2)).^2).^0.5; end % The other formulas as follows: % https://www.saedsayad.com/k_nearest_neighbors.htm [value, label]=min(distance, [], 3); label=color_labels(label); % clear value distance colors=[0, 0, 0; 0, 255, 0; 255, 0, 0]; y=zeros(size(image)); l=double(label)+1; for m=1:rows for n=1:cols y(m, n, :)=colors(l(m, n), :); end end mask=uint8(label); end

Matlab: Convert TIF to PNG

file.

Matlab: Convert the segmented image into a labeled file

file

1. Labeling images using LabKit for semantic segmentation

ENVI: Instructions for converting ENVI format classification maps to GEOTIFF format

[1] Open the ENVI format classification image, such as image 2003; at this point, the image's save format is ENVI Classification, which can be checked by editing the image's header file (Edit Header). The method is to right-click on 2003 in the Available Bands List, and from the pop-up menu select Edit Header, as shown in the image below;

[2] In the Header Info dialog box, select TIFF from the File Type dropdown list and click OK. Afterward, the icon for 2003 changes, as shown below;

[3] In the ENVI main menu, select File->Save File As->TIFF/GEOTIFF, choose the output location in the dialog box, click OK to finish.

Matlab: a simple approach for classifing two kinds of types on the image

It works well on small areas (here at the Plot level) and only with two types in the image that have significant color differences. File.

I added a loop to minimize the effect of the non-Rgeion of Interest on the binzrization process. 2nd. 3rd.

The conditions for applying the binary process are quite stringent. It's important to ensure that the image to be processed contains fairly typical features of the terrain colors and in appropriate quantities. Only then is it the right image to use, as this minimizes the area of invalid regions. 4th.

The calculation process completely excludes the background area. 5th.

Matlab: Convert RGB to HSV/XYZ/LAB/Ycbcr/NTSC

File. 2nd.

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% Created by LI Xu % Version 1.0 % February 22, 2024 % If you have any question about this code, % please do not hesitate to contact me via E-mail: % jeremy456@163.com % Blog: % http://blog.sciencenet.cn/u/lixujeremy clear; clc; timebegin=tic; cur_data=date; cur_time=fix(clock); str1=sprintf('%s %.2d:%.2d:%.2d', cur_data, cur_time(4), cur_time(5), cur_time(6)); fprintf('Time Begin: '); fprintf(str1); fprintf('\n'); filename='wheat'; % Source Directory SouDir='input'; % Destination Directory DesDir='output'; files=dir(SouDir); files=files(3:end); for ii=1:numel(files) % construct the input file path filepath=fullfile(SouDir, files(ii).name); [image, geo]=readgeoraster(filepath); image=double(image); try info=geotiffinfo(filepath); catch info=georasterinfo(filepath); end Num_bands=size(image, 3); if Num_bands==3 RGB=image; else RGB=zeros(size(image, 1), size(image, 2), 3); RGB(:, :, 1)=image(:, :, 4); RGB(:, :, 2)=image(:, :, 2); RGB(:, :, 3)=image(:, :, 1); end % Normalise the RGB bands [0, 255] RGB=GenNormalise(RGB); % https://www.mathworks.com/help/images/understanding-color-spaces-and-color-space-conversion.html xyz=rgb2xyz(RGB); lab=rgb2lab(RGB); ycbcr=rgb2ycbcr(RGB); ntsc=rgb2ntsc(RGB); HSV=rgb2hsv(RGB); % ExG = 2 * G - R - B ExG=2*RGB(:, :, 2)-RGB(:, :, 1)-RGB(:, :, 3); otpath=strsplit(files(ii).name, '_'); otpath=otpath(2:end); otpath=strjoin(otpath, '_'); otpath_rgb=[filename, '_rgb_', otpath]; otpath_hsv=[filename, '_hsv_', otpath]; otpath_xyz=[filename, '_xyz_', otpath]; otpath_lab=[filename, '_lab_', otpath]; otpath_ycbcr=[filename, '_ycbcr_', otpath]; otpath_ntsc=[filename, '_ntsc_', otpath]; otpath_exg=[filename, '_exG_', otpath]; otpath_rgb=fullfile(DesDir, otpath_rgb); otpath_hsv=fullfile(DesDir, otpath_hsv); otpath_xyz=fullfile(DesDir, otpath_xyz); otpath_lab=fullfile(DesDir, otpath_lab); otpath_ycbcr=fullfile(DesDir, otpath_ycbcr); otpath_ntsc=fullfile(DesDir, otpath_ntsc); otpath_exg=fullfile(DesDir, otpath_exg); try geotiffwrite(otpath_rgb, RGB, geo); geotiffwrite(otpath_hsv, HSV, geo); geotiffwrite(otpath_xyz, xyz, geo); geotiffwrite(otpath_lab, lab, geo); geotiffwrite(otpath_ycbcr, ycbcr, geo); geotiffwrite(otpath_ntsc, ntsc, geo); geotiffwrite(otpath_exg, ExG, geo); catch strcmd=['gdalinfo ', filepath]; [~, cmdout]=system(strcmd); epsg=extractBetween(cmdout, "EPSG:"," got from GeoTIFF keys"); epsg=epsg{1}; % geotiffwrite(otpath, uint8(class_imag), geo, 'GeoKeyDirectoryTag', info.GeoTIFFTags.GeoKeyDirectoryTag); geotiffwrite(otpath_rgb, RGB, geo, 'CoordRefSysCode', ['EPSG:', epsg], 'TiffType', 'bigtiff'); geotiffwrite(otpath_hsv, HSV, geo, 'CoordRefSysCode', ['EPSG:', epsg], 'TiffType', 'bigtiff'); geotiffwrite(otpath_xyz, xyz, geo, 'CoordRefSysCode', ['EPSG:', epsg], 'TiffType', 'bigtiff'); geotiffwrite(otpath_lab, lab, geo, 'CoordRefSysCode', ['EPSG:', epsg], 'TiffType', 'bigtiff'); geotiffwrite(otpath_ycbcr, ycbcr, geo, 'CoordRefSysCode', ['EPSG:', epsg], 'TiffType', 'bigtiff'); geotiffwrite(otpath_ntsc, ntsc, geo, 'CoordRefSysCode', ['EPSG:', epsg], 'TiffType', 'bigtiff'); geotiffwrite(otpath_exg, ExG, geo, 'CoordRefSysCode', ['EPSG:', epsg], 'TiffType', 'bigtiff'); end disp(['[', num2str(ii), '/', num2str(numel(files)), ']~', files(ii).name]); end fprintf('Time Begin: '); fprintf(str1); fprintf('\n'); cur_data=date; cur_time=fix(clock); str2=sprintf('%s %.2d:%.2d:%.2d', cur_data, cur_time(4), cur_time(5), cur_time(6)); fprintf('Time End: '); disp(str2); timespan=toc(timebegin); fprintf('Time Span: %.4f s\n', timespan); disp('***********************************************'); function output=GenNormalise(input) output=input*0; for ii=1:size(input, 3) image=input(:, :, ii); % red_normalized = (red - red.min()) / (red.max() - red.min()) * 255 aa=image(:); image_normalized=(image-min(aa))./(max(aa)-min(aa))*255.0; output(:, :, ii)=image_normalized; end end

Matlab: Decision Tree Tool for Image

This is a simple tool of decison tree for the image analysis. It can export the compared image and the ruleset file at once. File.

GDAL: Instructions of function 'gdal_rasterize'

Files

Canopeo: Green and Non-Green

I am using Canopeo here to calculate the percentage of green and non-green (RGB) in photos, and also to extract the green parts of the photos. Files. Delete the soil and everything else from the image.

[1] Canopeo.

[2] Andres Patrignani, Tyson E. Ochsner. 2015. Canopeo: A Powerful New Tool for Measuring Fractional Green Canopy Cover. Agronomy Journal, 107(6): 2312~2320.

How to Convert LiDAR LAZ to LAS files and use in ArcMap

laszip.exe

Py+eCognition: Set up and tips

1. Set up


2. Hexagon_esgmentation_python_cp.dcp
3. segment-anything.dcp
4. How to download the latest version of eCognition? Click this link.
5. How to install eCognition on Windows? Click this link for License Manager, click this link for Developer
6. If you do not have a valid license, please request a trial version. Trial software access is not limited to a specific time period, but export and save functions, and the workspace environment are restricted. Rulesets saved in trial software cannot be opened in a fully-licensed version of eCognition software.
7. From the Ground Up videos

ML: A simple SAM program from input image to output mask, saving as .tif format

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# https://github.com/facebookresearch/segment-anything/issues/221 import cv2, os import matplotlib.pyplot as plt sam_checkpoint='D:/PyTest/kkk/sam_vit_l_0b3195.pth' model_type="vit_l" from segment_anything import SamAutomaticMaskGenerator, sam_model_registry sam = sam_model_registry[model_type](checkpoint=sam_checkpoint) inpath='D:/PyTest/kkk/dog.jpg' img_arr=cv2.imread(inpath) img_arr=cv2.cvtColor(img_arr,cv2.COLOR_BGR2RGB) mask_generator=SamAutomaticMaskGenerator(sam) # mask_generator = SamAutomaticMaskGenerator( # model=sam, # points_per_side=32, # pred_iou_thresh=0.86, # stability_score_thresh=0.92, # crop_n_layers=1, # crop_n_points_downscale_factor=2, # # # min_mask_region_area=100, # Requires open-cv to run post-processing # ) predictor=mask_generator.generate(img_arr) # Choose the first mask # mask=predictor[0]['segmentation'] # # Remove background by turn it to white # img_arr[mask==False]=[255, 255, 255] newimg = img_arr[:, :, 0] * 0 for ii in range(len(predictor)): # print(ii) mask=predictor[ii]['segmentation'] # newimg = img_arr[:, :, 0] * 0 newimg[mask == True] = ii+1 # filename = os.path.join('D:/PyTest/kkk/export',str(ii+1)+'.tif') # cv2.imwrite(filename, newimg) # plt.imshow(img_arr) # plt.axis('off') # plt.show() filename='D:/PyTest/kkk/dog_new.tif' cv2.imwrite(filename, newimg)