Remote Sensing Image Classification Method Based on Preferential Adaptive IntervalValue Fuzzy CMeans The heterogeneity of objects within the class and the ambiguity of objects between the classes in remote sensing images cause the uncertainty in ground objects classification. Fuzzy set theory could express the fuzziness effectively, while intervalvalue data model can reflect the uncertainty of the data. Therefore, combining the intervalvalue data model and fuzzy cmeans algorithm, a preferential adaptive intervalvalue fuzzy cmeans (PAIVFCM) algorithm is proposed in this paper. The overall interval width of the category is adjusted by normalizing mean square error in the class, the interval modeling of the data is selected by using the preferential factor dynamically, thereby increasing the intraclass compactness and the boundary separability. The experimental results show that PAIVFCM method can be effectively applied in the SPOT5 remote sensing data classification, and the overall classification accuracy and Kappa coefficients are greatly improved compared with the existing popular fuzzy classification methods.

A Singlestage Multiclass Object Detection Method for Remote Sensing Images Impressive progresses have been achieved in object detection for images by convolution neural networks. However, a robust multiclass object detection method is still one of the great challenges for remote sensing images. Due to the great diversity of scale, orientation, density and background of objects, most advanced object detection algorithms in natural scenes usually suffer a sharp decline in remote sensing images detection. To solve these problems, we proposed a Singlestage Multiclass Object Detection (SMOD) method, aiming at remote sensing images, which can be trained from scratch and detect multiclass objects quickly and precisely. The proposed method introduces a novel Feature Reuse and Attention (FRA) structure as a key module of feature extraction backbone, which combines SE Attention module and dense Feature Reuse connection. Especially, a multiclass detection structure is proposed to learn from multiscale, multilevel feature map and get effective attention representation for multiclass remote sensing object detection. SMOD can be trained from scratch without pretrained network stably and converge well simply by employing batch normalization throughout the network. Experiments show that our trainingfromscratch method can obtain better performance compared with some stateofart algorithms on public multiclass remote sensing dataset AIIA20186.