Detection of cities and buildings in the images

Detection of cities and buildings in the images

Project is focused on the image detection which major components are cities and buildings. Buildings and cities detection assumes occurence of the edges as implication of the Windows and walls, as well as presence of the sky. Algorithm creates the feature vector with SVM classification algorithm.

Functions used: HoughLinesP, countNonZero, Sobel, threshold, merge, cvtColor, split, CvSVM

The process

  1. Create edge image
    cv::Sobel(intput, grad_x, CV_16S, 1, 0, 3, 1, 0, cv::BORDER_DEFAULT);
    cv::Sobel(intput, grad_y, CV_16S, 0, 1, 3, 1, 0, cv::BORDER_DEFAULT);
    
  2. Find lines in the binary edge image
    cv::HoughLinesP(edgeImage, edgeLines, 1, CV_PI / 180.0, 1, 10, 0);
    
  3. Count numbers of lines in specified tilt
  4. Convert original image to HSV color space and remove saturation and value
    cv::cvtColor(src, hsv, CV_BGR2HSV);
    
  5. Process the image from top to bottom , if pixel is not blue then all pixels under him are not sky
  6. Classification with SVM
    CvSVMParams params;
    params.svm_type  = CvSVM::C_SVC;
    params.kernel_type = CvSVM::LINEAR;
    params.term_crit   = cvTermCriteria(CV_TERMCRIT_ITER, 5000, 1e-5);
    
    float OpencvSVM::predicate(std::vector<float> features)
    {
       std::vector<std::vector<float> > featuresMatrix;
       featuresMatrix.push_back(features);
       cv::Mat featuresMat = createMat(featuresMatrix);
       return SVM.predict(featuresMat);
    }
    

Example

Original image

Edge image

Highlighted image

Hue factor

Detected sky