An N-dimensional approach towards object based classification of remotely sensed imagery
Remote sensing techniques are widely used for land cover classification and urban analysis. The availability of high resolution remote sensing imagery limits the level of classification accuracy attainable from pixel-based approach. In this paper object-based classification scheme based on a hierarchical support vector machine is introduced. By combining spatial and spectral information, the amount of overlap between classes can be decreased; thereby yielding higher classification accuracy and more accurate land cover maps. We have adopted certain automatic approaches based on the advanced techniques as Cellular automata and Genetic Algorithm for kernel and tuning parameter selection. Performance evaluation of the proposed methodology in comparison with the existing approaches is performed with reference to the Bhopal city study area.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral ClassificationLand Cover ClassificationSimilar Papers 제목 키워드 기반
Weakly-semi-supervised object detection in remotely sensed imagery
Deep learning for detecting objects in remotely sensed imagery can enable new technologies for important applications including mitigating climate change. However, these models often require large datasets labeled with b…
Objectobject-detectionObject DetectionSemi-Supervised Object DetectionFocusing on Shadows for Predicting Heightmaps from Single Remotely Sensed RGB Images with Deep Learning
Estimating the heightmaps of buildings and vegetation in single remotely sensed images is a challenging problem. Effective solutions to this problem can comprise the stepping stone for solving complex and demanding probl…
Temporal Sequence Object-based CNN (TS-OCNN) for crop classification from fine resolution remote sensing image time-series
Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution (FSR) remotely sensed imagery now offers great op…
ClassificationCrop ClassificationCrop Yield PredictionObject+2TorchGeo: Deep Learning With Geospatial Data
Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among others. Deep learning methods are partic…
Deep LearningTransfer LearningRoad Segmentation of Remotely-Sensed Images Using Deep Convolutional Neural Networks with Landscape Metrics and Conditional Random Fields
Object segmentation of remotely-sensed aerial (or very-high resolution, VHS) images and satellite (or high-resolution, HR) images, has been applied to many application domains, especially in road extraction in which the …
Earth ObservationRoad SegmentationSemantic Segmentation