Images gathered from different satellites are vastly available these days due to the fast
development of remote sensing (RS) technology. These images significantly enhance the …

An increasing availability of remote sensing data in the era of geo big-data makes producing
well-represented, reliable training data to be more challenging and requires an excessive …

Change detection (CD) aims to identify surface changes from bitemporal images. In recent
years, deep learning (DL)-based methods have made substantial breakthroughs in the field …

Given two multitemporal aerial images, semantic change detection (SCD) aims to locate the
land-cover variations and identify their change types with pixelwise boundaries. This …

Convolutional neural networks (CNNs) with excellent spatial feature extraction abilities have
become popular in remote sensing (RS) image change detection (CD). However, CNNs …

The past years have witnessed great progress on remote sensing (RS) image interpretation
and its wide applications. With RS images becoming more accessible than ever before …

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 …

Hyperspectral images (HSIs) contain rich spectral signatures that reveal more image details
and, thus, enable the detection of less noticeable changes on the ground. However, HSI …

Deep learning methods, especially convolutional neural network (CNN)-based methods,
have shown promising performance for hyperspectral image (HSI) change detection (CD). It …

Recent change detection (CD) methods focus on the extraction of deep change semantic
features. However, existing methods overlook the fine-grained features and have the poor …