Hierarchical Feature Fusion Triple Network for Change Detection With Bitemporal Remote Sensing Images

• Published in: IEEE transactions on geoscience and remote sensing Vol. 63; pp. 1 - 12
• Main Authors: Lv, Zhiyong, Yang, Tianyv, Zhong, Pingdong, Sun, Weiwei, Atli Benediktsson, Jon, Li, Junhuai
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Change detection; Convolution; Convolutional neural networks; Earth surface; False alarms; Feature extraction; Imaging; Kernel; Land cover; Land cover change detection (LCCD); Learning; Modules; neural network; Neural networks; Noise; Noise reduction; Performance enhancement; Reflectance; Remote sensing; remotely sensed images (RSIs); Shape; Transformers

Achieving land cover change detection (LCCD) through remotely sensed images (RSIs) is important in the observation of the changes on the Earth's surface. In such detection, spectral-reflectance noise and the uncertainty of the imaging external conditions for the bitemporal RSIs usually cause so...