Learning Deconvolution Network for Semantic Segmentation

In recent years, building change detection methods have made great progress by introducing deep learning, but they still suffer from the problem of the extracted features not being discriminative enough, resulting in incomplete regions and irregular boundaries. To tackle this problem, we propose a dual-task constrained deep Siamese convolutional network (DTCDSCN) model, which contains three subnetworks: a change detection network and two semantic segmentation networks. DTCDSCN can accomplish both change detection and semantic segmentation at the same time, which can help to learn more discriminative object-level features and obtain a complete change detection map. Furthermore, we introduce a dual attention module (DAM) to exploit the interdependencies between channels and spatial positions, which improves the feature representation. We also improve the focal loss function to suppress the sample imbalance problem. The experimental results obtained with the WHU building data set show that the proposed method is effective for building change detection and achieves state-of-the-art performance in terms of four metrics on the WHU building data set: precision, recall, F1-score, and intersection over union.

Building Change Detection for Remote Sensing Images Using a Dual-Task Constrained Deep Siamese Convolutional Network Model

Deep learning (DL) has great influence on large parts of science and increasingly established itself as an adaptive method for new challenges in the field of Earth observation (EO). Nevertheless, the entry barriers for EO researchers are high due to the dense and rapidly developing field mainly driven by advances in computer vision (CV). To lower the barriers for researchers in EO, this review gives an overview of the evolution of DL with a focus on image segmentation and object detection in convolutional neural networks (CNN). The survey starts in 2012, when a CNN set new standards in image recognition, and lasts until late 2019. Thereby, we highlight the connections between the most important CNN architectures and cornerstones coming from CV in order to alleviate the evaluation of modern DL models. Furthermore, we briefly outline the evolution of the most popular DL frameworks and provide a summary of datasets in EO. By discussing well performing DL architectures on these datasets as well as reflecting on advances made in CV and their impact on future research in EO, we narrow the gap between the reviewed, theoretical concepts from CV and practical application in EO.

Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends

In recent years, building change detection methods have made great progress by introducing deep learning, but they still suffer from the problem of the extracted features not being discriminative enough, resulting in incomplete regions and irregular boundaries. To tackle this problem, we propose a dual task constrained deep Siamese convolutional network (DTCDSCN) model, which contains three sub-networks: a change detection network and two semantic segmentation networks. DTCDSCN can accomplish both change detection and semantic segmentation at the same time, which can help to learn more discriminative object-level features and obtain a complete change detection map. Furthermore, we introduce a dual attention module (DAM) to exploit the interdependencies between channels and spatial positions, which improves the feature representation. We also improve the focal loss function to suppress the sample imbalance problem. The experimental results obtained with the WHU building dataset show that the proposed method is effective for building change detection and achieves a state-of-the-art performance in terms of four metrics: precision, recall, F1-score, and intersection over union.

Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model

One of the most challenging research subjects in remote sensing is feature extraction, such as road features, from remote sensing images. Such an extraction influences multiple scenes, including map updating, traffic management, emergency tasks, road monitoring, and others. Therefore, a systematic review of deep learning techniques applied to common remote sensing benchmarks for road extraction is conducted in this study. The research is conducted based on four main types of deep learning methods, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models. We also compare these various deep learning models applied to remote sensing datasets to show which method performs well in extracting road parts from high-resolution remote sensing images. Moreover, we describe future research directions and research gaps. Results indicate that the largest reported performance record is related to the deconvolutional nets applied to remote sensing images, and the F1 score metric of the generative adversarial network model, DenseNet method, and FCN-32 applied to UAV and Google Earth images are high: 96.08%, 95.72%, and 94.59%, respectively.

Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review

Road extraction is a fundamental task in the field of remote sensing which has been a hot research topic in the past decade. In this paper, we propose a semantic segmentation neural network, named D-LinkNet, which adopts encoderdecoder structure, dilated convolution and pretrained encoder for road extraction task. The network is built with LinkNet architecture and has dilated convolution layers in its center part. Linknet architecture is efficient in computation and memory. Dilation convolution is a powerful tool that can enlarge the receptive field of feature points without reducing the resolution of the feature maps. In the CVPR DeepGlobe 2018 Road Extraction Challenge, our best IoU scores on the validation set and the test set are 0.6466 and 0.6342 respectively.

/pdf/d-linknet-linknet-with-pretrained-encoder-and-dilated-2u5z39uctw.pdf

D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction

Satellite image semantic segmentation, including extracting road, detecting building, and identifying land cover types, is essential for sustainable development, agriculture, forestry, urban planning, and climate change research. Nevertheless, it is still unclear how to develop a refined semantic segmentation model in an efficient and elegant way. In this paper, we propose attention dilation-LinkNet (AD-LinkNet) neural network that adopts encoder–decoder structure, serial–parallel combination dilated convolution, channel-wise attention mechanism, and pretrained encoder for semantic segmentation. Serial–parallel combination dilated convolution enlarges receptive field as well as assemble multi-scale features for multi-scale objects, such as long-span road and small pool. The channel-wise attention mechanism is designed to advantage the context information in the satellite image. The experimental results on road extraction and surface classification data sets prove that the AD-LinkNet shows a significant effect on improving the segmentation accuracy. We defeated the D-Linknet algorithm that won the first place in the CVPR 2018 DeepGlobe road extraction competition.

/pdf/towards-accurate-high-resolution-satellite-image-semantic-4gnn1w7ciy.pdf

Towards Accurate High Resolution Satellite Image Semantic Segmentation

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D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction

Towards Accurate High Resolution Satellite Image Semantic Segmentation