Lightweight Convolutional Neural Network for High-Spatial-Resolution Remote Sensing Scenes Classification

Luo, Wang; Yang, Wenqing; Yu, Xian; Wang, Ye; Tan, Kai

doi:10.1109/wcsp49889.2020.9299854

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Ample CNN variants have been studied to solve the task of land cover classification by taking high-resolution remote sensing images as input, such as these serial networks of Sherrah et al [27] , Maggiori et al [28] , Luo et al [29] , Huang et al [30] etc. However, there are still several issues remained to be addressed, one of them is fragmented segmentation caused by scale variance of objects in high-resolution remote sensing images as Fig.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Feature Reconstruction and Interclass Attention Weighting for Land Cover Classification

Zhan,

Xiong,

Huang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Land cover classification has the goal to attribute each pixel of high-resolution remote sensing image with planimetric category labels (such as vegetation, building, water etc.). In recent years, many serial deep learning architectures (features are delivered through a single path such as in ResNet, MobileNet, Segformer etc.) based on Convolutional Neural Networks (CNN) and attention mechanisms have been widely explored in land cover classification. However, high-resolution remote sensing images typically have abundant textual details, variable scales in objects, large intra-class variance and similar inter-class correlation, which bring challenges to land cover classification. In this work, we present two pluggable modules to further boost serial learning architecture: first, to cope with ambiguous boundaries caused by lost details and fragmented segmentation stemmed from scale variances, a combination of spatial attention and channel attention is proposed for Multi-Scale Feature Reconstruction (MSFR); Second, to mitigate the classification error caused by intra-class variance and inter-class correlation, we explore an Inter-Class Attention Weighting (ICAW) module, which builds feature vectors for each category, and applies a multi-head attention model to capture self-attention dependence among different categories. The experimental results demonstrate that the proposed modules are feasible to existing serial learning architectures and can improve OA (Overall Accuracy) by 5.64% on the ISPRS Vaihingen 2D dataset (using ResNet50 as backbone), in particular, the OA values are 80.68% and 86.32% before and after using the proposed modules, respectively. In addition, compared to other state-of-art models, our method can achieve similar or even better classification results, yet offer superior inference performance. Code is available at: https://github.com/StraySparks/MSFR-and-ICAW.

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Section: Introductionmentioning

confidence: 99%