高光谱成像
计算机科学
土地覆盖
人工智能
遥感
班级(哲学)
模式识别(心理学)
封面(代数)
上下文图像分类
图像(数学)
机器学习
数据挖掘
土地利用
地理
机械工程
工程类
土木工程
作者
Meng Xu,Yuanyuan Zhao,Liang Yajun,Xiaorui Ma
出处
期刊:Remote Sensing
[MDPI AG]
日期:2022-05-26
卷期号:14 (11): 2556-2556
被引量:12
摘要
By virtue of its large-covered spatial information and high-resolution spectral information, hyperspectral images make lots of mapping-based fine-grained remote sensing applications possible. However, due to the inconsistency of land-cover types between different images, most hyperspectral image classification methods keep their effectiveness by training on every image and saving all classification models and training samples, which limits the promotion of related remote sensing tasks. To deal with the aforementioned issues, this paper proposes a hyperspectral image classification method based on class-incremental learning to learn new land-cover types without forgetting the old ones, which enables the classification method to classify all land-cover types with one final model. Specially, when learning new classes, a knowledge distillation strategy is designed to recall the information of old classes by transferring knowledge to the newly trained network, and a linear correction layer is proposed to relax the heavy bias towards the new class by reapportioning information between different classes. Additionally, the proposed method introduces a channel attention mechanism to effectively utilize spatial–spectral information by a recalibration strategy. Experimental results on the three widely used hyperspectral images demonstrate that the proposed method can identify both new and old land-cover types with high accuracy, which proves the proposed method is more practical in large-coverage remote sensing tasks.
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