Coastal Land Cover Classification of High-Resolution Remote Sensing Images Using Attention-Driven Context Encoding Network

Abstract

Low inter-class variance and complex spatial details exist in ground objects of the coastal zone, which leads to a challenging task for coastal land cover classification (CLCC) from high-resolution remote sensing images. Recently, fully convolutional neural networks have been widely used in CLCC. However, the inherent structure of the convolutional operator limits the receptive field, resulting in capturing the local context. Additionally, complex decoders bring additional information redundancy and computational burden. Therefore, this paper proposes a novel attention-driven context encoding network to solve these problems. Among them, lightweight global feature attention modules are employed to aggregate multi-scale spatial details in the decoding stage. Meanwhile, position and channel attention modules with long-range dependencies are embedded to enhance feature representations of specific categories by capturing the multi-dimensional global context. Additionally, multiple objective functions are introduced to supervise and optimize feature information at specific scales. We apply the proposed method in CLCC tasks of two study areas and compare it with other state-of-the-art approaches. Experimental results indicate that the proposed method achieves the optimal performances in encoding long-range context and recognizing spatial details and obtains the optimum representations in evaluation indexes.

Unsupervised Domain Adaption for High-Resolution Coastal Land Cover Mapping with Category-Space Constrained Adversarial Network

“…Feature extractor: According to our multi-level adaptation framework, the Resnet-101 [44] module pre-trained on the ImageNet [45] is adopted as the backbone network that works to extract features at multiple scales. The same as several advanced reports [3,30], we substitute for the down-sampling layers in the last two residual blocks with dilated convolutional layers. This strategy led to the size of the output feature map 1/8 of the input image, aiming to retain more spatial details without changing the scale of pre-trained parameters.…”

“…Coastal land cover mapping (CLCM) provides a detailed and intuitive presentation of ground objects in the land-sea interaction zone, which is the necessary and sufficient premise for land investigation, resource development, and eco-environment protection [1][2][3]. In the past decade, the continuous evolution of space and sensor technologies has made remote sensing enter into the Big Data era [4].…”

A Multi-Level Output-Based DBN Model for Fine Classification of Complex Geo-Environments Area Using Ziyuan-3 TMS Imagery

“…Furthermore, deep learning algorithms are increasingly popular in remote sensing classification because depth and discriminating features can be extracted layer-by-layer [ 37 , 38 ]. Zhang et al [ 39 ] proposed a scale sequence joint deep learning method by incorporating a sequence of scales in a single unified modeling framework for LULC classification.…”

“…Zhang et al [ 39 ] proposed a scale sequence joint deep learning method by incorporating a sequence of scales in a single unified modeling framework for LULC classification. Chen et al [ 37 ] proposed a novel attention-driven context encoding network method for coastal land cover classification from high-resolution remote sensing images. The deep belief network (DBN), involving unsupervised learning in feature extraction, is among the most commonly utilized algorithms, with great successes achieved in image recognition, information retrieval, and natural language processing, among others [ 40 , 41 ].…”

A Review and Meta-analysis of Semantic Segmentation Models in Land Use/ Land Cover Mapping

“…Although, the exploration and study of coastal area remote sensing image segmentation remains a relatively underexplored research area, as noted by [61]. This challenge is primarily attributed to the significant complexities associated with coastal land categories, including issues such as homogeneity, multiscale features, and class imbalance, as highlighted [64].…”