Multi-threshold remote sensing image segmentation with improved ant colony optimizer with salp foraging

Qian Yunlou,Tu Jiaqing,Luo Gang,Sha Ce,Heidari Ali Asghar,Chen Huiling 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.6

Remote sensing images can provide direct and accurate feedback on urban surface morphology and geographic conditions. They can be used as an auxiliary means to collect data for current geospatial information systems, which are also widely used in city public safety. Therefore, it is necessary to research remote sensing images. Therefore, we adopt the multi-threshold image segmentation method in this paper to segment the remote sensing images for research. We first introduce salp foraging behavior into the continuous ant colony optimization algorithm (ACOR) and construct a novel ACOR version based on salp foraging (SSACO). The original algorithm’s convergence and ability to avoid hitting local optima are enhanced by salp foraging behavior. In order to illustrate this key benefit, SSACO is first tested against 14 fundamental algorithms using 30 benchmark test functions in IEEE CEC2017. Then, SSACO is compared with 14 other algorithms. The experimental results are examined from various angles, and the findings convincingly demonstrate the main power of SSACO. We performed segmentation comparison studies based on 12 remote sensing images between SSACO segmentation techniques and several peer segmentation approaches to demonstrate the benefits of SSACO in remote sensing image segmentation. Peak signal-to-noise ratio, structural similarity index, and feature similarity index evaluation of the segmentation results demonstrated the benefits of the SSACO-based segmentation approach. SSACO is an excellent optimizer since it seeks to serve as a guide and a point of reference for using remote sensing image algorithms in urban public safety.

A Remote Sensing Industrial Solid Waste Image Segmentation Method Based on Improved Watershed Algorithm

보안공학연구지원센터(IJSIP) 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.2

Taking the high-resolution remote sensing image of industrial waste dump as the research object, this paper proposes a new image segmentation method based on marker-controlled watershed transform and region merging. The method gets the finial partition result by two-phrase segmentation on the pan-sharpened true color ALOS image. In the first phase, color gradient image of original image should be calculated and then it is modified by morphological impose minima, which should use markers extracted in two different ways. Lastly, preliminary segmentation result is obtained by watershed transform operates on the modified color gradient image. To solve the over-segmentation of industrial solid waste and other ground objects, region merging operation is performed according to the similarity measure criterion based on segmented objects’ color histogram Bhattacharyya coefficient in the second phrase, and then the final result is obtained. This method has been tested on the pan-sharpened ALOS image of 2.5 meters resolution in Shizuishan industrial zone, China. Experiment results demonstrate that this method is feasible and effective to segment the remote sensing industrial solid waste image.

MRU-Net: A remote sensing image segmentation network for enhanced edge contour Detection

Jing Han,Weiyu Wang,Yuqi Lin,Xueqiang LYU 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.12

Remote sensing image segmentation plays an important role in realizing intelligent city construction. The current mainstream segmentation networks effectively improve the segmentation effect of remote sensing images by deeply mining the rich texture and semantic features of images. But there are still some problems such as rough results of small target region segmentation and poor edge contour segmentation. To overcome these three challenges, we propose an improved semantic segmentation model, referred to as MRU-Net, which adopts the U-Net architecture as its backbone. Firstly, the convolutional layer is replaced by BasicBlock structure in U-Net network to extract features, then the activation function is replaced to reduce the computational load of model in the network. Secondly, a hybrid multi-scale recognition module is added in the encoder to improve the accuracy of image segmentation of small targets and edge parts. Finally, test on Massachusetts Buildings Dataset and WHU Dataset the experimental results show that compared with the original network the ACC、mIoU and F1 value are improved, and the imposed network shows good robustness and portability in different datasets.

1D 통합된 근접차이에 기반한 자율적인 다중분광 영상 분할

뮤잠멜 ( Khairul Muzzammil Saipullah ),윤병춘 ( Byung-choon Yun ),김덕환 ( Deok-hwan Kim ) 한국정보처리학회 2010 한국정보처리학회 학술대회논문집 Vol.17 No.2

This paper proposes a novel feature extraction method for unsupervised multispectral image segmentation based in one dimensional combined neighborhood differences (1D CND). In contrast with the original CND, which is applied with traditional image, 1D CND is computed on a single pixel with various bands. The proposed algorithm utilizes the sign of differences between bands of the pixel. The difference values are thresholded to form a binary codeword. A binomial factor is assigned to these codeword to form another unique value. These values are then grouped to construct the 1D CND feature image where is used in the unsupervised image segmentation. Various experiments using two LANDSAT multispectral images have been performed to evaluate the segmentation and classification accuracy of the proposed method. The result shows that 1D CND feature outperforms the spectral feature, with average classification accuracy of 87.55% whereas that of spectral feature is 55.81%.

Extraction of road features from UAV images using a novel level set segmentation approach

Abolfazl Abdollahi,Biswajeet Pradhan,Nagesh Shukla 서울시립대학교 도시과학연구원 2019 도시과학국제저널 Vol.23 No.3

A novel hybrid technique for road extraction from UAV imagery is presented in this paper. The suggested analysis begins with image segmentation via Trainable Weka Segmentation. This step uses an immense range of image features, such as detectors for edge detection, filters for texture, filters for noise depletion and a membrane finder. Then, a level set method is performed on the segmented images to extract road features. Next, morphological operators are applied on the images for improving extraction precision. Eventually, the road extraction precision is calculated on the basis of manually digitized road layers. Obtained results indicated that the average proportions of completeness, correctness and quality were 93.52%, 85.79% and 81.01%, respectively. Therefore, experimental results validated the superior performance of the proposed hybrid approach in road extraction from UAV images

Cluster-Based Spin Images for Characterizing Diffuse Objects in 3D Range Data

( Hee Zin Lee ),( Sang Yoon Oh ) 한국센서학회 2014 센서학회지 Vol.23 No.6

Detecting and segmenting diffuse targets in laser ranging data is a critical problem for tactical reconnaissance. In this study, we propose a new method that facilitates the characterization of diffuse irregularly shaped objects using “spin images,” i.e., local 2D histograms of laser returns oriented in 3D space, and a clustering process. The proposed “cluster-based spin imaging” method resolves the problem of using standard spin images for diffuse targets and it eliminates much of the computational complexity that characterizes the production of conventional spin images. The direct processing of pre-segmented laser points, including internal points that penetrate through a diffuse object’s topmost surfaces, avoids some of the requirements of the approach used at present for spin image generation, while it also greatly reduces the high computational time overheads incurred by searches to find correlated images. We employed 3D airborne range data over forested terrain to demonstrate the effectiveness of this method in discriminating the different geometric structures of individual tree clusters. Our experiments showed that cluster-based spin images have the potential to separate classes in terms of different ages and portions of tree crowns.

무인비행기 (UAV) 영상을 이용한 농작물 분류

박진기,박종화,Park, Jin Ki,Park, Jong Hwa 한국농공학회 2015 한국농공학회논문집 Vol.57 No.6

The Unmanned Aerial Vehicles (UAVs) have several advantages over conventional RS techniques. They can acquire high-resolution images quickly and repeatedly. And with a comparatively lower flight altitude i.e. 80~400 m, they can obtain good quality images even in cloudy weather. Therefore, they are ideal for acquiring spatial data in cases of small agricultural field with mixed crop, abundant in South Korea. This paper discuss the use of low cost UAV based remote sensing for classifying crops. The study area, Gochang is produced by several crops such as red pepper, radish, Chinese cabbage, rubus coreanus, welsh onion, bean in South Korea. This study acquired images using fixed wing UAV on September 23, 2014. An object-based technique is used for classification of crops. The results showed that scale 250, shape 0.1, color 0.9, compactness 0.5 and smoothness 0.5 were the optimum parameter values in image segmentation. As a result, the kappa coefficient was 0.82 and the overall accuracy of classification was 85.0 %. The result of the present study validate our attempts for crop classification using high resolution UAV image as well as established the possibility of using such remote sensing techniques widely to resolve the difficulty of remote sensing data acquisition in agricultural sector.

A Class Distance Penalty Deep Learning Method for Post-disaster Building Damage Assessment

Fang Jung Tsai,Szu-Yun Lin 대한토목학회 2024 KSCE Journal of Civil Engineering Vol.28 No.5

Automatic building damage assessment can significantly aid rescue operations, attributed to booming deep learning and remote sensing technologies. However, the class imbalance of the dataset often skews prediction models towards the majority class in the segmentation of damaged buildings. This issue is further exacerbated when damaged buildings are categorized into multiple scales, intensifying biases within the models. Hence, this research adopts an algorithm-level method to improve the reliability of post-disaster damage assessment. It proposes a novel loss function named Ordinal Class Distance Penalty Loss (OCDPL), considering the ordinal relationship between classes and penalizing the misclassifications according to the class error distance. Two hyperparameters are also introduced to enable the model to fine-tune the contribution of ordinal relationships on the loss function. The satellite images of hurricane disasters in the xBD dataset were adopted as the case study. The results show that the proposed approach can improve F1 scores and Mean Absolute Error of overall damage level classes. Notably, the findings underscore the value of leveraging information on ordinal classes to facilitate the learning of minority classes and diminish class error distances. This aspect holds particular significance for emergency responses to widespread and severe disasters.

영상 기반 Semantic Segmentation 알고리즘을 이용한 도로 추출

오행열,전승배,김건,정명훈 한국측량학회 2022 한국측량학회지 Vol.40 No.3

Cities are becoming more complex due to rapid industrialization and population growth in modern times. In particular, urban areas are rapidly changing due to housing site development, reconstruction, and demolition. Thus accurate road information is necessary for various purposes, such as High Definition Map for autonomous car driving. In the case of the Republic of Korea, accurate spatial information can be generated by making a map through the existing map production process. However, targeting a large area is limited due to time and money. Road, one of the map elements, is a hub and essential means of transportation that provides many different resources for human civilization. Therefore, it is essential to update road information accurately and quickly. This study uses Semantic Segmentation algorithms Such as LinkNet, D-LinkNet, and NL-LinkNet to extract roads from drone images and then apply hyperparameter optimization to models with the highest performance. As a result, the LinkNet model using pre-trained ResNet-34 as the encoder achieved 85.125 mIoU. Subsequent studies should focus on comparing the results of this study with those of studies using state-of-the-art object detection algorithms or semi-supervised learning-based Semantic Segmentation techniques. The results of this study can be applied to improve the speed of the existing map update process. 현대에는 급속한 산업화와 인구 증가로 인해 도시들이 더욱 복잡해지고 있다. 특히 도심은 택지개발, 재건축, 철거 등으로 인해 빠르게 변화하는 지역에 해당한다. 따라서 자율주행에 필요한 정밀도로지도와 같은 다양한 목적을 위해 빠른 정보 갱신이 필요하다. 우리나라의 경우 기존 지도 제작 과정을 통해 지도를 제작하면 정확한 공간정보를 생성할 수 있으나 대상 지역이 넓은 경우 시간과 비용이 많이 든다는 한계가 있다. 지도 요소 중 하나인 도로는 인류 문명을 위한 많은 다양한 자원을 제공하는 중추이자 필수적인 수단에 해당한다. 따라서 도로 정보를 정확하고 신속하게 갱신하는 것이 중요하다. 이 목표를 달성하기 위해 본 연구는 Semantic Segmentation 알고리즘인 LinkNet, D-LinkNet 및 NL-LinkNet을 사용하여 광주광역시 도시철도 2호선 공사 현장을 촬영한 드론 정사영상에서 도로를 추출한 다음 성능이 가장 높은 모델에 하이퍼 파라미터 최적화를 적용하였다. 그 결과, 사전 훈련된 ResNet-34를 Encoder로 사용한 LinkNet 모델이 85.125 mIoU를 달성했다. 향후 연구 방향으로 최신 Semantic Segmentation 알고리즘 또는 준지도 학습 기반 Semantic Segmentation 기법을 사용하는 연구의 결과와의 비교 분석이 수행될 것이다. 본 연구의 결과는 기존 지도 갱신 프로세스의 속도를 개선하는 데 도움을 줄 수 있을 것으로 예상된다.

A Novel Sea-Land Segmentation Algorithm Based on Local Binary Patterns for Ship Detection

Yu Xia,Shouhong Wan,Peiquan Jin,Lihua Yue 보안공학연구지원센터(IJSIP) 2014 International Journal of Signal Processing, Image Vol.7 No.3