Since remote sensing images are one of the main sources for people to obtain required information, the quality of the image becomes particularly important. Nevertheless, noise often inevitably exists in the image, and the targets are usually blurred by the acquisition of the imaging system, resulting in the degradation of quality of the images. In this paper, a novel preprocessing algorithm is proposed to simultaneously smooth noise and to enhance the edges, which can improve the visual quality of remote sensing images. It consists of an improved adaptive spatial filter, which is a weighted filter integrating functions of both noise removal and edge sharpness. Its processing parameters are flexible and adjustable relative to different images. The experimental results confirm that the proposed method outperforms the existing spatial algorithms both visually and quantitatively. It can play an important role in the remote sensing field in order to achieve more information of interested targets.

The design of compact hyperspectral cameras with high ground resolution and large field of view (FOV) is a challenging problem in the field of remote sensing. In this paper, the time-delayed integration (TDI) of the digital domain is applied to solve the issue of insufficient light energy brought by high spatial resolution, and a hyperspectral camera with linear variable filters suitable for digital domain TDI technology is further designed. The camera has a wavelength range of 450–950 nm, with an average spectral resolution of 10.2 nm. The paper also analyzed the effects of digital domain TDI on the signal–noise ratio (SNR) and the spectral resolution. During its working in orbits, we have obtained high-SNR images with a swath width of 150 km, and a ground sample distance (GSD) of 10 m @ 500 km. The design of the hyperspectral camera has an improved spatial resolution while reducing the cost.