International Geoscience and Remote Sensing Symposium (IGARSS)

Segmentation of images by color features: a survey

https://research.vu.nl/ws/files/88842035/shirmohammadi_et_al_author.pdf

Scenario analysis for integrated water resources management under future land use change in the Urmia Lake region, Iran

Nowadays, high resolution aerial images are widely available thanks to the diffusion of advanced technologies such as UAVs (Unmanned Aerial Vehicles) and new satellite missions. Although these developments offer new opportunities for accurate land use analysis and change detection, cloud and terrain shadows actually limit benefits and possibilities of modern sensors. Focusing on the problem of shadow detection and removal in VHR color images, the paper proposes new solutions and analyses how they can enhance common unsupervised classification procedures for identifying land use classes related to the CO 2 absorption. To this aim, an improved fully automatic procedure has been developed for detecting image shadows using exclusively RGB color information, and avoiding user interaction. Results show a significant accuracy enhancement with respect to similar methods using RGB based indexes. Furthermore, novel solutions derived from Procrustes analysis have been applied to remove shadows and restore brightness in the images. In particular, two methods implementing the so called “anisotropic Procrustes” and the “not-centered oblique Procrustes” algorithms have been developed and compared with the linear correlation correction method based on the Cholesky decomposition. To assess how shadow removal can enhance unsupervised classifications, results obtained with classical methods such as k-means, maximum likelihood, and self-organizing maps, have been compared to each other and with a supervised clustering procedure.

Shadow detection and removal in RGB VHR images for land use unsupervised classification

With the increase of the spatial resolution of aerial images, the shadow problem is more prominent. The shadows affect the applications such as object recognition, image dense matching, and object classification. Existing shadow detection methods can acquire results with high accuracy, but usually need much manual intervention and the traditional stretch models generally lead to color distortion and undercompensation. Therefore, we propose an object-oriented automatic shadow detection method without manual intervention and a shadow compensation method by regional matching. In the proposed method, pixel-based soft shadow detection, which uses Gaussian mixture model to simulate the gray distribution and refines soft shadow map with guiled filtering, is combined with image segmentation result to obtain accurate shadow regions with complete shape and no hole. Then shadow regions are compensated, with less loss of details and brightness imbalance, referring to their optimal homogeneous nonshadow region obtained by regional matching based on Bag-of-Words. The total variation model is used to decrease the noise amplified by the pixel-based stretch and boundary effect in the result. Experiments are performed on three publicly available high-resolution aerial images to demonstrate the superiority of our proposed methods. It shows that the proposed method can accurately detect shadows from urban high-resolution aerial images with an overall rate of over 88%. The compensation results display excellent visual effects compared with the state-of-the-art methods, consistent with the true color of ground objects.

Deshadowing of Urban Airborne Imagery Based on Object-Oriented Automatic Shadow Detection and Regional Matching Compensation

Many problems in image processing and computer vision arise from shadows in a single color aerial image. This article presents a new algorithm by which shadows are extracted from a single color aerial image. Apart from using the ratio value of the hue over the intensity in some state-of-the-art algorithms, this article introduces another ratio map, which is obtained by applying the saturation over the intensity. Candidate shadow and nonshadow regions are separated by applying Otus’s thresholding method. The color attenuation relationship that describes the relationship between the attenuation of each color channel is derived from the Planck’s blackbody irradiance law. For each region, the color attenuation relationship and other determination conditions are performed iteratively to segment it into smaller sub-regions and to identify whether each sub-region is a true shadow region. Compared with previous methods, the proposed algorithm presents better shadow detection accuracy in the images that contain some dark green lawn, river, or low brightness shadow regions. The experimental results demonstrate the advantage of the proposed algorithm.

https://link.springer.com/content/pdf/10.1186%2F1687-6180-2012-141.pdf

Shadow detection in color aerial images based on HSI space and color attenuation relationship

Recently, sparse representation has been successfully used in single image super-resolution reconstruction. Unlike the traditional single image super-resolution methods such as image interpolation, the super-resolution with sparse representation reconstructs image with one or several constant dictionaries learned from external databases. However, the contents can vary significantly across different patches in a single image, and the fixed dictionaries cannot suit for every patch. This paper presents a novel approach for single image super-resolution based on sparse representation, which uses group as the basic unit, and trains dictionary with external database and the input low-resolution image itself for each group to ensure that the dictionary is suitable for the patches in the group. Simultaneous sparse coding algorithm is used to accelerate the processing and improve the result. Extensive experiments on natural images show that our method achieves better results than some state-of-the-art algorithms in terms of both objective and human visual evaluations.

/pdf/group-based-single-image-super-resolution-with-online-4ij776wsxa.pdf

Wenxuan Shi

Papers

Shadow detection in color aerial images based on HSI space and color attenuation relationship

Group-based single image super-resolution with online dictionary learning