Digital image correlation (DIC) based damage detection for CFRP laminates by using machine learning based image semantic segmentation

Object detection is essential to the interpretation of optical remote sensing images and can serve as a foundation for research into additional visual tasks that utilize remote sensing. However, the object detection network currently employed in optical remote sensing images underutilizes the output of the feature pyramid, so there remains potential for an improved detection. At present, a suitable balance between the detection efficiency and detection effect is difficult to attain. This paper proposes an enhanced YOLOv5 algorithm for object detection in high-resolution optical remote sensing images, utilizing multiple layers of the feature pyramid, a multi-detection-head strategy, and a hybrid attention module to improve the effect of object-detection networks for use with optical remote sensing images. According to the SIMD dataset, the mAP of the proposed method was 2.2% better than YOLOv5 and 8.48% better than YOLOX, achieving an improved balance between the detection effect and speed.

/pdf/yolo-hr-improved-yolov5-for-object-detection-in-high-bgy3zlbn.pdf

YOLO-HR: Improved YOLOv5 for Object Detection in High-Resolution Optical Remote Sensing Images

Semantic segmentation is a fundamental but challenging problem of pixel-level remote sensing (RS) data analysis. Semantic segmentation tasks based on aerial and satellite images play an important role in a wide range of applications. Recently, with the successful applications of deep learning (DL) in the computer vision (CV) field, more and more researchers have introduced and improved DL methods to the task of RS data semantic segmentation and achieved excellent results. Although there are a large number of DL methods, there remains a deficiency in the evaluation and advancement of semantic segmentation techniques for RS data. To solve the problem, this paper surveys more than 100 papers in this field in the past 5 years and elaborates in detail on the aspects of technical framework classification discussion, datasets, experimental evaluation, research challenges, and future research directions. Different from several previously published surveys, this paper first focuses on comprehensively summarizing the advantages and disadvantages of techniques and models based on the important and difficult points. This research will help beginners quickly establish research ideas and processes in this field, allowing them to focus on algorithm innovation without paying too much attention to datasets, evaluation indicators, and research frameworks.

pdf/deep-learning-based-semantic-segmentation-of-remote-sensing-12ynoaaq.pdf

Deep learning-based semantic segmentation of remote sensing images: a review

Land cover classification is a multiclass segmentation task to classify each pixel into a certain natural or human-made category of the earth’s surface, such as water, soil, natural vegetation, crops, and human infrastructure. Limited by hardware computational resources and memory capacity, most existing studies preprocessed original remote sensing images by downsampling or cropping them into small patches less than 512 × 512 pixels before sending them to a deep neural network. However, downsampling incurs a spatial detail loss, renders small segments hard to discriminate, and reverses the spatial resolution progress obtained by decades of efforts. Cropping images into small patches causes a loss of long-range context information, and restoring the predicted results to their original size brings extra latency. In response to the above weaknesses, we present an efficient lightweight semantic segmentation network termed MKANet. Aimed at the characteristics of top view high-resolution remote sensing imagery, MKANet utilizes sharing kernels to simultaneously and equally handle ground segments of inconsistent scales, and also employs a parallel and shallow architecture to boost inference speed and friendly support image patches more than 10× larger. To enhance boundary and small segment discrimination, we also propose a method that captures category impurity areas, exploits boundary information, and exerts an extra penalty on boundaries and small segment misjudgments. Both visual interpretations and quantitative metrics of extensive experiments demonstrate that MKANet obtains a state-of-the-art accuracy on two land-cover classification datasets and infers 2× faster than other competitive lightweight networks. All these merits highlight the potential of MKANet in practical applications.

/pdf/mkanet-a-lightweight-network-with-sobel-boundary-loss-for-16ufr2ai.pdf

MKANet: A Lightweight Network with Sobel Boundary Loss for Efficient Land-cover Classification of Satellite Remote Sensing Imagery

• A new building segmentation dataset for deep learning is generated. • The current building datasets have been examined in terms of quality over quantity. • Recent state-of-the-art architectures and encoders have been employed. • Cross-evaluations with different datasets were performed for generability. Automatic building segmentation from satellite images is an important task for various applications such as urban mapping, disaster management and regional planning. With the broader availability of very high-resolution satellite images, deep learning-based techniques have been broadly used for remote sensing image-related tasks. In this study, we generated a new building dataset, the Istanbul dataset, for the building segmentation task. 150 Pléiades image tiles of 1500 × 1500 pixels covering an area of 85 km 2 area of Istanbul city were used and approximately 40,000 buildings were labelled, representing different building structures and spatial distribution. We extensively investigated the ideal architecture, encoder and hyperparameter settings for building segmentation tasks using the new Istanbul dataset. More than 60 experiments were conducted by applying state-of-the-art architectures such as U-Net, Unet++, DeepLabv3+, FPN and PSPNet with different pre-trained encoders and hyperparameters. Our experiments showed that Unet++ architecture using SE-ResNeXt101 encoder pre-trained with ImageNet provides the best results with 93.8% IoU on the Istanbul dataset. In order to prove our solution's generalizability, the ideal network has also been trained separately on Inria and Massachusetts building segmentation datasets. The networks have produced IoU values of 75.39% and 92.53% on the Inria and Massachusetts datasets, respectively. The results indicate that our ideal network solution settings outperform other methods in terms of building segmentation even without any specific architectural modification. The weights files and inference notebook is available on: https://github.com/TolgaBkm/Istanbul_Dataset . 

Comparative analysis of deep learning based building extraction methods with the new VHR Istanbul dataset

Semantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with Deeplabv3+

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Semantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with Deeplabv3+