Analysis of image enhancement methods for remote sensing

Remote sensing generally can be defined as obtaining information about an object without any physical contact. Some distortions occur in remotely sensed images from the moment of image capturing. Remotely sensed image enhancement aims to increase the information of the input image such as edge, illumination, contrast, to provide a better input for the further image processing steps and to facilitate the interpretation of the image. Several techniques have been proposed for remote sensing image enhancement and significant developments are observed in this field. Within the scope of this thesis, General Histogram Equalization (GHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Recursive Mean-Seperate Histogram Equalization (RMSHE), Adaptive Gamma Correction with Weight Distribution (AGCWD), Discrete Wavelet Transform and Singular Value Decomposition (DWT-SVD), Regularized Histogram Equalization and Discrete Cosine Transform (RHE-DCT), Bilateral Filtering (BF), and Neural Image Correction and Enhancement Routine (NICER) methods are examined. Inspired by these traditional methods, a hybrid method has been proposed that completes the deficiencies of the BF and CLAHE methods. In the proposed hybrid method, two-level BF is applied to the input image. Then, the enhaced image is consructed by applying CLAHE to the residual image. Proposed hybrid method offers better edge enhancement than the CLAHE method while increasing the low contrast enhancement in the BF method. Due to the subjective components of image enhancement, quantitative results in evaluations have to support the visual results. For this reason, visual results and quantitative results are given together in order to make a more objective discussion. It has been observed that the hybrid method demonstrates better results compare to the examined methods.