Abstract
Modern convolutional neural networks (CNNs) are often trained on pre-set data sets with a fixed size. As for the large-scale applications of satellite images, for example, global or regional mappings, these images are collected incrementally by multiple stages in general. In other words, the sizes of training datasets might be increased for the tasks of mapping rather than be fixed beforehand. In this paper, we present a novel algorithm, called GeoBoost, for the incremental-learning tasks of semantic segmentation via convolutional neural networks. Specifically, the GeoBoost algorithm is trained in an end-to-end manner on the newly available data, and it does not decrease the performance of previously trained models. The effectiveness of the GeoBoost algorithm is verified on the large-scale data set of DREAM-B. This method avoids the need for training on the enlarged data set from scratch and would become more effective along with more available data.
Highlights
In recent years, the size of satellite image data sets has been considerably enlarged compared with a decade ago
We propose a novel approach, GeoBoost, for geographically incremental learning, which is trained in an end-to-end way
It enables the models of satellite images to learn continually based on geographical information of data without forgetting the previous knowledge
Summary
The size of satellite image data sets has been considerably enlarged compared with a decade ago. The widely used data set, WHU-RS [1], is built through three versions. This data set was expanded from 12 classes of aerial scenes [1] to [2] and [3] classes, and the number of samples in each class was increased. Most current models, which are adopted for the increasing data sets, have the weak continual-learning ability over time. This arouses the need for the models which are capable of continually fitting the growing data sets
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