Conclusion

Remote sensing satellites capture data in the form of images which are processed and utilized in various applications such as land area classification, map updating, weather forecast, urban planning, etc. However, due to the constraints on the hardware of the sensors and the available transmission bandwidth of the transponder, many commercial satellites provide earth information by capturing images which have complementary characteristics. In this book, we have addressed the problem of multi-resolution image fusion or Pan-sharpening. Here, the low spatial resolution MS image and high spatial resolution Pan image are combined to obtain a single fused image which has both high spatial and spectral resolutions. We seek a fused image which has spectral resolution of the MS image and the spatial resolution of the Pan image. Although the MS and Pan images capture the same geographical area, the complementary nature of these images in terms of the spatial and spectral resolutions gives rise to variation in the two images. Because of this, when we fuse the given images by using direct pixel intensity values, the resultant fused data suffers from spatial as well as spectral distortions. Another important issue in the problem of multi-resolution image fusion is the registration of MS and Pan images. Accurate registration is a difficult task and in this book, we have not addressed it; instead we have used registered data. Here, we present the conclusions which are drawn based on the different proposed methods for Pan-sharpening/ image fusion.

We began our work by proposing two new fusion techniques based on the edge-preserving filters. The Pan image has high frequency details that can be extracted with the help of edge-preserving filter. These extracted details are injected into the upsampled MS image. In our work, we used two edge-preserving filters, namely, the guided filter and difference of Gaussians (DoGs) in order to extract the required details present in the Pan image. The extension of the guided filter in multistages is introduced which effectively extracts the details from Pan and MS images. Similarly, the concept of DoGs is also used to extract the high frequency features from the Pan image. The potential of the proposed methods were evaluated by conducting the experiments on the original as well as the degraded datasets captured using various satellites. The results were compared with state-of-the art methods.