Cloud and Shadow Identification from Aerial Images
AnupaVijai1, S Padmavathi2, D Venkataraman3
1AnupaVijai, Department of Computer Science and Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.
2Padmavathi S, Department of Computer Science and Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.
3Venkataraman D, Department of Computer Science and Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.
Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 532-536 | Volume-8 Issue-7, May 2019 | Retrieval Number: F5006048619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Clouds and shadows pose severe problems in discernment of the scene and identification of objects in aerial photography. The changes in illumination,ensued by the presence of cloud and the shadow,aresome of the reasons that lead to ambiguity, while carrying out image segmentation leading to detection of targeted objects. Conventional methods are efficient in detecting thick clouds in contrastive background, but perform poorly in the perception of thin clouds, multiple clouds and their shadows. Reference images for the input are needed in most cases, and separate algorithms are pursued, to identify clouds and shadows in an image, which might not be feasible in all scenarios. Techniques used in this paperto detect cloud and shadows,obviating the need for reference images, are image enhancement, analysis of color histogram of input images, adoption of automatic thresholding and mathematical morphology on the input image. The proposed algorithm,was found to be fast,and experimented on various images that contained multiple white cloud clusters of different shapes, thickness and their shadows. The algorithmwas validated with an accuracy of 94.6% and 87.2% for identification of clouds and shadows, respectively.
Keyword: Aerial Image, Automatic Threshold, Cloud Detection, Color Histogram, Morphological Operations, Shadow Detection.
Scope of the Article: Cloud Resources Utilization in IoT.