Gong Cheng

TL;DR: A large-scale data set, termed “NWPU-RESISC45,” is proposed, which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU).

TL;DR: This paper proposes a novel and effective approach to learn a rotation-invariant CNN (RICNN) model for advancing the performance of object detection, which is achieved by introducing and learning a new rotation- Invariant layer on the basis of the existing CNN architectures.

TL;DR: This paper proposes a simple but effective method to learn discriminative CNNs (D-CNNs) to boost the performance of remote sensing image scene classification and comprehensively evaluates the proposed method on three publicly available benchmark data sets using three off-the-shelf CNN models.

TL;DR: This survey focuses on more generic object categories including, but not limited to, road, building, tree, vehicle, ship, airport, urban-area, and proposes two promising research directions, namely deep learning- based feature representation and weakly supervised learning-based geospatial object detection.

TL;DR: A comprehensive review of the recent deep learning based object detection progress in both the computer vision and earth observation communities is provided and a large-scale, publicly available benchmark for object DetectIon in Optical Remote sensing images is proposed, which is named as DIOR.