Object detection in high resolution satellite images has recently become a major concern in new geospatial information methods. The higher spatial resolution with spectral information provides better detection results. Therefore, increasing the image resolution prior to the object detection is important. For this purpose, pansharpening, which uses complementary information from MS and PAN images, is gaining popularity as it helps to increase spatial resolution while preserving the spectral information. This study proposes a detailed enhanced scheme for pansharpening to improve the detection results. Several deep learning models are trained on raw dataset, as well as on the detail enhanced pansharpened images. It is shown that the training stage using proposed detail enhanced scheme provides better detection results compared to classical pansharpening or raw data based training for different deep networks.

An efficient image enhancement method should improve the contrast in the image while keeping the edge and color information. Since existing approaches seem not to fulfill all these demands, a hybrid approach which will combine advantages of individual approaches is proposed in this work. The multiscale bilateral filter is replaced by an iterative joint version where the output is used as guidance image for the next iterations. Then a multiscale structure is designed by the appropriate modifications of the spatial and range kernels as in the multiscale bilateral filter. A final refining is performed by the local use of the Hazy Image Model based method (HIM) on the resulting image. Visual and quantitative comparisons with conventional Discrete Wavelet Transform and Singular Value Decomposition based method (DWT-SVD), Regularized Histogram Equalization with Discrete Cosine Transform method (RHE-DCT), Bilateral Filtering based method (BF), and HIM method demonstrate the superiority of the proposed method and the resulting hybrid method for remote sensing image enhancement.

An efficient pansharpening method should inject the missing geometric information to the multispectral (MS) image while preserving its radiometric information. Widely used additive wavelet transform-based pansharpening methods extract the missing high-frequency information by decomposing the panchromatic (PAN) image and adding the detail layers to the low-resolution MS (LRM) image. However, this approach causes a redundant detail injection, leading to artifacts in the fusion result. In this letter, we propose to decompose the high-resolution-PAN image using an edge-preserving decomposition which will decrease the amount of redundant high-frequency injection. The missing high-frequency information of the LRM image is obtained by the decomposition of the PAN image using a multiscale bilateral filter. The spatial and range parameters of the bilateral filter are optimized so as to enhance spatial and spectral metrics. The fusion results are compared with the widely used additive wavelet luminance proportional (AWLP) and recently proposed improved AWLP fusion methods. The resulting images as well as evaluation metrics demonstrate that the proposed injection approach has better performance.

A new fusion method based on bilateral pyramid for multispectral and panchromatic images is presented. The fused image is obtained by two different rules: substitutive and additive methods. Bilateral pyramid is a multiscale decomposition method which decomposes an input image into a base layer representing the low frequency content and several detail layers representing the high frequency part of the image. In substitutive method, both MS and PAN images are decomposed using bilateral pyramid. The detail layers of the PAN image are added to the base layer of the MS image. In additive method, the detail layers of the PAN image are directly added to the MS image. The proposed method is compared with the widely used IHS (intensity-hue-saturation), ATWT substitutive and ATWT additive fusion methods. The resulting images as well as evaluation metrics demonstrate that the proposed algorithm has better performance.