The recent emergence of deep learning for characterizing complex patterns in remote sensing imagery reveals its high potential to address some classic challenges in this domain, e.g. scene classification. Typical deep learning models require extremely large datasets with rich contents to train a multilayer structure in order to capture the essential features of scenes. Compared with the benchmark datasets used in popular deep learning frameworks, however, the volumes of available remote sensing datasets are particularly limited, which have restricted deep learning methods from achieving full performance gains. In order to address this fundamental problem, this article introduces a methodology to not only enhance the volume and completeness of training data for any remote sensing datasets, but also exploit the enhanced datasets to train a deep convolutional neural network that achieves state-of-the-art scene classification performance. Specifically, we propose to enhance any original dataset by applying three operations – flip, translation, and rotation to generate augmented data – and use the augmented dataset to train and obtain a more descriptive deep model. The proposed methodology is validated in three recently released remote sensing datasets, and confirmed as an effective technique that significantly contributes to potentially revolutionary changes in remote sensing scene classification, empowered by deep learning.

Object detection plays an active role in remote sensing applications. Recently, deep convolutional neural network models have been applied to automatically extract features, generate region proposals, and predict corresponding object class. However, these models face new challenges in VHR remote sensing images due to the orientation and scale variations and the cluttered background. In this letter, we propose an end-to-end multiscale visual attention networks (MS-VANs) method. We use skip-connected encoder-decoder model to extract multiscale features from a full-size image. For feature maps in each scale, we learn a visual attention network, which is followed by a classification branch and a regression branch, so as to highlight the features from object region and suppress the cluttered background. We train the MS-VANs model by a hybrid loss function which is a weighted sum of attention loss, classification loss, and regression loss. Experiments on a combined data set consisting of Dataset for Object Detection in Aerial Images and NWPU VHR-10 show that the proposed method outperforms several state-of-the-art approaches.

This article investigates the problem of enhancing underwater visual observations for the purpose of accurate underwater object detection. Most existing underwater visual enhancement algorithms tend to follow human vision preference but do not necessarily favor the effectiveness of an object detection algorithm. We observe that it should not be the human vision preference but the object detection algorithm that knows what underwater visual enhancement configuration is most beneficial to the detection tasks. In light of this observation, we propose a reinforcement learning paradigm of configuring visual enhancement for object detection in underwater scenes. Specifically, we use underwater image features as states and object detection score increments as rewards. We set up a collection of extensible actions that consist of multiple visual enhancement algorithms. The optimal policy is learned in the form of an action sequence, which characterizes a stepwise process of visual enhancement. Experimental results validate that the sequence of visual enhancement algorithms configured with respect to the object detection algorithm is in favor of improving the detection results.

We observe that a natural image tends to exhibit similar histograms for color channels in the RGB color space and consistent statistical estimates for color channels in the Lab color space. We refer to these observations as natural color consistencies. In contrast, we discover that an underwater image does not always follow the natural color consistencies. Different color channels in an underwater image tend to give rise to very different distributions, regardless of whether the channels are in the RGB color space or Lab color space. We refer to these observations as underwater color disparities. To enhance an underwater image to make it appear more natural, it is necessary to correct its underwater color disparities to align with the natural color consistencies. To this end, we develop an adaptive attenuated channel compensation method based on optimal channel precorrection and a salient absorption map-guided fusion method for eliminating the color deviation in the RGB color space. We then develop a method to enhance the contrast of channel L and an adaptive color distribution specification method for improving the contrast and matching the color distribution in the Lab color space. Additionally, we develop an edge-enhanced mask fusion method for correcting blurry details. Our method is not a deep learning method but can effectively be applied to a single underwater image. The qualitative and quantitative empirical results validate that our method outperforms state-of-the-art underwater image enhancement methods. We release the reproducible code at https://gitee.com/wanghaoupc/Underwater Color Disparities for public evaluation.