Semantic segmentation of remotely sensed urban scene images is required in a wide range
of practical applications, such as land cover mapping, urban change detection …

Quantities of multi-temporal remote sensing (RS) images create favorable conditions for
exploring the urban change in the long term. However, diverse multi-source features and …

Finding robust and correct keypoints in images remains a challenge, especially when
repetitive patterns are present. In this article, we propose a universal two-step filtering …

The integration of Artificial Intelligence (AI) and Satellite Remote Sensing in Land Cover
Change Detection (LCCD) has gained increasing significance in scientific discovery and …

Traditional change detection (CD) methods operate in the simple image domain or hand-
crafted features, which has less robustness to the inconsistencies (eg, brightness and noise …

Establishing feature correspondences between different images is a key step in many
remote sensing applications. In this paper, a novel descriptor based on an extended self …

The characteristics of urban spatial structure and the objective evaluation of the
development level of urban economy have always been the concern of urban researchers …

In this paper, a novel approach for the registration of multi‐sensor remote sensing images
with substantial time differences is proposed. The proposed method consists of four main …

With advances in remote sensing, massive amounts of remotely sensed data can be
harnessed to support land use/land cover (LULC) change studies over larger scales and …

Semantic segmentation plays an important role in being able to understand the content of
remote sensing images. In recent years, deep learning methods based on Fully …