A Dynamic Effective Class Balanced Approach for Remote Sensing Imagery Semantic Segmentation of Imbalanced Data

Abstract

The wide application and rapid development of satellite remote sensing technology have put higher requirements on remote sensing image segmentation methods. Because of its characteristics of large image size, large data volume, and complex segmentation background, not only are the traditional image segmentation methods difficult to apply effectively, but the image segmentation methods based on deep learning are faced with the problem of extremely unbalanced data between categories. In order to solve this problem, first of all, according to the existing effective sample theory, the effective sample calculation method in the context of semantic segmentation is firstly proposed in the highly unbalanced dataset. Then, a dynamic weighting method based on the effective sample concept is proposed, which can be applied to the semantic segmentation of remote sensing images. Finally, the applicability of this method to different loss functions and different network structures is verified on the self-built Landsat8-OLI remote sensing image-based tri-classified forest fire burning area dataset and the LoveDA dataset, which is for land-cover semantic segmentation. It has been concluded that this weighting algorithm can enhance the minimal-class segmentation accuracy while ensuring that the overall segmentation performance in multi-class segmentation tasks is verified in two different semantic segmentation tasks, including the land use and land cover (LULC) and the forest fire burning area segmentation In addition, this proposed method significantly improves the recall of forest fire burning area segmentation by as much as about 30%, which is of great reference value for forest fire research based on remote sensing images.

A Forest Fire Prediction Method for Lightning Stroke Based on Remote Sensing Data

“…Although the dataset used in this study includes over 450,000 records of lightning detection and location data from 2005 to 2009, lightning-caused forest fires were limited to only 99 instances. The data imbalance exists in many directions of fire research, such as fire identification [52,53]. The severe imbalance in the data challenges the construction of predictive models.…”

SLMFNet: Enhancing land cover classification of remote sensing images through selective attentions and multi-level feature fusion

“…Similarly, [ 53 ] introduced a hybrid multiple attention network for aerial image semantic segmentation, enabling the network to adaptively learn spatial, channel, and class correlations to enhance the distinguishability of learned representations. Concerning the imbalanced distributions of RSIs, Zhou et al [ 12 ] presented a novel dynamic weighting method based on effective sample calculation for semantic segmentation in remote sensing, significantly improving minimal-class accuracy and recall in imbalanced datasets, as demonstrated in diverse applications like forest fire area segmentation and land-cover semantic segmentation using the Landsat8-OLI and LoveDA datasets. Likewise, Li et al [ 54 ] proposed a novel SSCNet that integrates spectral and spatial information using a joint spectral–spatial attention module (JSSA), significantly enhancing semantic segmentation in RSIs, as demonstrated by superior performance on ISPRS Potsdam and LoveDA datasets and validated through comprehensive ablation studies.…”

“…Semantic segmentation inherently involves three sub-problems: object recognition, localization, and boundary delineation [11][12][13]. Effectively addressing all these sub-tasks is essential for creating a robust network.…”

Gradient Structure Information-Guided Attention Generative Adversarial Networks for Remote Sensing Image Generation

“…In recent years, some mitigation approaches have been proposed at the algorithm level for issue 2 [14,15]. Zhou et al [16] proposed a dynamic balancing weighting method based on the number of effective samples for remote sensing image segmentation tasks with data imbalance. CBCL [11] dynamically constructs a class-balanced memory queue during the training of object detection models by memorizing training samples to alleviate class imbalance.…”