A Novel Adaptive Deep Network for Building Footprint Segmentation

• URL: http://arxiv.org/abs/2103.00286v1
• Date: Sat, 27 Feb 2021 18:13:48 GMT
• Title: A Novel Adaptive Deep Network for Building Footprint Segmentation
• Authors: A. Ziaee, R. Dehbozorgi, M. D\"oller
• Abstract summary: We propose a novel network-based on Pix2Pix methodology to solve the problem of inaccurate boundaries obtained by converting satellite images into maps. Our framework includes two generators where the first generator extracts localization features in order to merge them with the boundary features extracted from the second generator to segment all detailed building edges. Different strategies are implemented to enhance the quality of the proposed networks' results, implying that the proposed network outperforms state-of-the-art networks in segmentation accuracy with a large margin for all evaluation metrics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Building footprint segmentations for high resolution images are increasingly demanded for many remote sensing applications. By the emerging deep learning approaches, segmentation networks have made significant advances in the semantic segmentation of objects. However, these advances and the increased access to satellite images require the generation of accurate object boundaries in satellite images. In the current paper, we propose a novel network-based on Pix2Pix methodology to solve the problem of inaccurate boundaries obtained by converting satellite images into maps using segmentation networks in order to segment building footprints. To define the new network named G2G, our framework includes two generators where the first generator extracts localization features in order to merge them with the boundary features extracted from the second generator to segment all detailed building edges. Moreover, different strategies are implemented to enhance the quality of the proposed networks' results, implying that the proposed network outperforms state-of-the-art networks in segmentation accuracy with a large margin for all evaluation metrics. The implementation is available at https://github.com/A2Amir/A-Novel-Adaptive-Deep-Network-for-Building-Footprint-Segmentation.

Related papers

• Deep Attention Unet: A Network Model with Global Feature Perception Ability [12.087640144194246]
  This paper proposes a new type of UNet image segmentation algorithm based on channel self attention mechanism and residual connection called. In my experiment, the new network model improved mIOU by 2.48% compared to traditional UNet on the FoodNet dataset.
  arXiv  Detail & Related papers  (2023-04-21T09:12:29Z)
• Bandwidth-efficient distributed neural network architectures with application to body sensor networks [73.02174868813475]
  This paper describes a conceptual design methodology to design distributed neural network architectures. We show that the proposed framework enables up to a factor 20 in bandwidth reduction with minimal loss. While the application focus of this paper is on wearable brain-computer interfaces, the proposed methodology can be applied in other sensor network-like applications as well.
  arXiv  Detail & Related papers  (2022-10-14T12:35:32Z)
• Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers [5.177947445379688]
  We propose a new segmentation model that combines convolutional neural networks with deep transformers. Our results demonstrate that the proposed methodology improves segmentation accuracy compared to state-of-the-art techniques.
  arXiv  Detail & Related papers  (2022-06-20T12:03:54Z)
• A Unified Architecture of Semantic Segmentation and Hierarchical Generative Adversarial Networks for Expression Manipulation [52.911307452212256]
  We develop a unified architecture of semantic segmentation and hierarchical GANs. A unique advantage of our framework is that on forward pass the semantic segmentation network conditions the generative model. We evaluate our method on two challenging facial expression translation benchmarks, AffectNet and RaFD, and a semantic segmentation benchmark, CelebAMask-HQ.
  arXiv  Detail & Related papers  (2021-12-08T22:06:31Z)
• Boundary-Aware Segmentation Network for Mobile and Web Applications [60.815545591314915]
  Boundary-Aware Network (BASNet) is integrated with a predict-refine architecture and a hybrid loss for highly accurate image segmentation. BASNet runs at over 70 fps on a single GPU which benefits many potential real applications. Based on BASNet, we further developed two (close to) commercial applications: AR COPY & PASTE, in which BASNet is augmented reality for "COPY" and "PASTING" real-world objects, and OBJECT CUT, which is a web-based tool for automatic object background removal.
  arXiv  Detail & Related papers  (2021-01-12T19:20:26Z)
• Interactive Video Object Segmentation Using Global and Local Transfer Modules [51.93009196085043]
  We develop a deep neural network, which consists of the annotation network (A-Net) and the transfer network (T-Net) Given user scribbles on a frame, A-Net yields a segmentation result based on the encoder-decoder architecture. We train the entire network in two stages, by emulating user scribbles and employing an auxiliary loss.
  arXiv  Detail & Related papers  (2020-07-16T06:49:07Z)
• A novel Region of Interest Extraction Layer for Instance Segmentation [3.5493798890908104]
  This paper is motivated by the need to overcome the limitations of existing RoI extractors. The proposed layer (called Generic RoI Extractor - GRoIE) introduces non-local building blocks and attention mechanisms to boost the performance. GRoIE can be integrated seamlessly with every two-stage architecture for both object detection and instance segmentation tasks.
  arXiv  Detail & Related papers  (2020-04-28T17:07:32Z)
• CRNet: Cross-Reference Networks for Few-Shot Segmentation [59.85183776573642]
  Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with only a few training images. With a cross-reference mechanism, our network can better find the co-occurrent objects in the two images. Experiments on the PASCAL VOC 2012 dataset show that our network achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-03-24T04:55:43Z)
• A novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images [30.39131853354783]
  This paper presents a novel deep neural network structure for pixel-wise sea-land segmentation, a Residual Dense U-Net (RDU-Net) RDU-Net is a combination of both down-sampling and up-sampling paths to achieve satisfactory results.
  arXiv  Detail & Related papers  (2020-03-17T16:00:59Z)
• Saliency Enhancement using Gradient Domain Edges Merging [65.90255950853674]
  We develop a method to merge the edges with the saliency maps to improve the performance of the saliency. This leads to our proposed saliency enhancement using edges (SEE) with an average improvement of at least 3.4 times higher on the DUT-OMRON dataset. The SEE algorithm is split into 2 parts, SEE-Pre for preprocessing and SEE-Post pour postprocessing.
  arXiv  Detail & Related papers  (2020-02-11T14:04:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.