인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Image segmentation of heterogeneous comparable objects lying beneath the earth’s surface is a fundamental but challenging research area in remote sensing. Learning approaches are used in remote sensing image segmentation to improve segmentation accuracy at the expense of time and a large amount of data, but their performance need to be finely classified due to information diversity constraints. In this work, we proposed an novel feature based fuzzy C -means-extreme learning machine (FBFCM-ELM) algorithm for remote sensing image segmentation in which the classification based on entropy, intensity, and edge features is performed in such a way that it updates the intensity value to preserve the most local characteristics in the image while still being able to clearly distinguish the image’s boundaries by assigning the pixel values of each cluster to the peak value of the cluster’s sub-histogram. Using FBFCM, features are extracted and used as reliable samples for ELM training. Undetermined segmented pixels are obtained using the trained ELM classifier. Experiments performed over number of images that confirmed the proposed method yields a better segmented RGB image, as evidenced by observable details, edges, and improved appearance that resembles the ground truth image and outperforms state-of-the-art algorithms.
#Artificial intelligence
#Pattern recognition (psychology)
#Computer science
#Histogram
#Image segmentation
#Segmentation
#Cluster analysis
#Segmentation-based object categorization
#Pixel
#Scale-space segmentation
#Region growing
#Feature (linguistics)
#Entropy (arrow of time)
#Computer vision
#Image fusion
#Ground truth
#Extreme learning machine
#Fuzzy logic
#RGB color model
#Image (mathematics)
#Artificial neural network
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
