Noise Reduction from L-Band ALOSPALSAR Data Set Using Spatial Domain Gaussian Low-Pass Filter

Abstract

The microwave remote sensing system is the broadest tool use to get information about the earth's objects in the form of images. These images are very much affected by various noises, which affects the precision of the images. These, in turn, affect the information present in it. To improve, the quality of satellite images, denoising of the image is an essential task. Gaussian noise majorly seen in all remotely sensed images. Natural sources cause this noise during the acquisition of the image. Many Researchers used Numerous types of filtering techniques to reduce such noises. In this paper, we have focused on lowering Gaussian noise from the microwave satellite dataset. To minimise Gaussian noise, the spatial domain Gaussian Low Pass Filter (GLPF) with different window sizes applied to a single Polarized (HH) L-band ALOS PALSAR satellite SLC level-1 Datasets. The results of filtered images are evaluated based on the respective mean, standard deviation and coefficient of variance.

Swin-Conv-Dspp and Global Local Transformer for Remote Sensing Image Semantic Segmentation

“…Atrous convolution is essentially a superposition of many highpass filters [58], which continuously enhances high-frequency information, so it tends to be better at extracting high-frequency information of features. The transformer is essentially a lowpass filter [59], which continuously strengthens the underlying semantic information of the image, so it is often better at extracting low-frequency information of features. By combining the advantages of the two to reduce the differences within classes and expand the frequency of information between classes, the negative effects of various noises in RS images are effectively suppressed.…”