Color correction for underwater images has received increasing interest, due to its critical role in facilitating available mature vision algorithms for underwater scenarios. Inspired by the stunning success of deep convolutional neural network (DCNN) techniques in many vision tasks, especially the strength in extracting features in multiple scales, we propose a deep multiscale feature fusion net based on the conditional generative adversarial network (GAN) for underwater image color correction. In our network, multiscale features are extracted first, followed by augmenting local features in each scale with global features. This design was verified to facilitate more effective and faster network learning, resulting in better performance in both color correction and detail preservation. We conducted extensive experiments and compared the results with state-of-the-art approaches quantitatively and qualitatively, showing that our method achieves significant improvements.

Owing to the relatively small size of vehicles in remote sensing images, lacking sufficient detailed appearance to distinguish vehicles from similar objects, the detection performance is still far from satisfactory compared with the detection results on everyday images. Inspired by the positive effects of super-resolution convolutional neural network (SRCNN) for object detection and the stunning success of deep CNN techniques, we apply generative adversarial network frameworks to realize simultaneous SRCNN and vehicle detection in an end-to-end manner, and the detection loss is backpropagated into the SRCNN during training to facilitate detection. In particular, our work is unsupervised and bypasses the requirement of low-/high-resolution image pairs during the training stage, achieving increased generality and applicability. Extensive experiments on representative data sets demonstrate that our method outperforms the state-of-the-art detectors. (The source code will be made available after the review process.)

This work presents FG-Net, a general deep learning framework for large-scale point cloud understanding without voxelizations, which achieves accurate and real-time performance with a single NVIDIA GTX 1080 8G GPU and an i7 CPU. First, a novel noise and outlier filtering method is designed to facilitate the subsequent high-level understanding tasks. For effective understanding purpose, we propose a novel plug-and-play module consisting of correlated feature mining and deformable convolution-based geometric-aware modeling, in which the local feature relationships and point cloud geometric structures can be fully extracted and exploited. For the efficiency issue, we put forward a new composite inverse density sampling (IDS)-based and learning-based operation and a feature pyramid-based residual learning strategy to save the computational cost and memory consumption, respectively. Compared with current methods which are only validated on limited datasets, we have done extensive experiments on eight real-world challenging benchmarks, which demonstrates that our approaches outperform state-of-the-art (SOTA) approaches in terms of accuracy, speed, and memory efficiency. Moreover, weakly supervised transfer learning is also conducted to demonstrate the generalization capacity of our method.

Visual grounding for remote sensing images (RSVG) aims to localize the referred objects in the remote sensing (RS) images according to a language expression. Existing methods tend to align visual and text features followed by concatenation and then employ a fusion Transformer to learn a token representation for final target localization. However, simple fusion Transformer structure fails to sufficiently learn the location representation of referred object from the multi-modal features. Inspired by the detection Transformer, in this paper, we propose a novel language query based Transformer framework for RSVG termed LQVG. Specifically, we adopt the extracted sentence-level text features as the queries, called language queries, to retrieve and aggregate representation information of the referred object from the multi-scale visual features in the Transformer decoder. The language queries are then converted into object embeddings for final coordinate prediction of referred object. Besides, a multi-scale cross-modal alignment module is devised before the multimodal Transformer to enhance the semantic correlation between the visual and text features, thus facilitating the cross-modal decoding process to generate more precise object representation. Moreover, a new RSVG dataset named RSVG-HR is built to evaluate the performance of the RSVG approaches on very high-resolution remote sensing images with inconspicuous objects. Experimental results on two benchmark datasets demonstrate that our proposed method significantly surpasses the comparison methods and achieves state-of-the-art performance. The dataset and code are available at https://github.com/LANMNG/LQVG.

Remote sensing image retrieval (RSIR), aiming at searching for a set of similar items to a given query image, is a very important task in remote sensing applications. Deep hashing learning as the current mainstream method has achieved satisfactory retrieval performance. On one hand, various deep neural networks are used to extract semantic features of remote sensing images. On the other hand, the hashing techniques are subsequently adopted to map the high-dimensional deep features to the low-dimensional binary codes. This kind of method attempts to learn one hash function for both the query and database samples in a symmetric way. However, with the number of database samples increasing, it is typically time-consuming to generate the hash codes of large-scale database images. In this article, we propose a novel deep hashing method, named asymmetric hash code learning (AHCL), for RSIR. The proposed AHCL generates the hash codes of query and database images in an asymmetric way. In more detail, the hash codes of query images are obtained by binarizing the output of the network, while the hash codes of database images are directly learned by solving the designed objective function. In addition, we combine the semantic information of each image and the similarity information of pairs of images as supervised information to train a deep hashing network, which improves the representation ability of deep features and hash codes. The experimental results on three public datasets demonstrate that the proposed method outperforms symmetric methods in terms of retrieval accuracy and efficiency. The source code is available at https://github.com/weiweisong415/Demo_AHCL_for_TGRS2022 .

Clouds and accompanying shadows, which exist in optical remote sensing images with high possibility, can degrade or even completely occlude certain ground-cover information in images, limiting their applicabilities for Earth observation, change detection, or land-cover classification. In this paper, we aim to deal with cloud contamination problems with the objective of generating cloud-removed remote sensing images. Inspired by low-rank representation together with sparsity constraints, we propose a coarse-to-fine framework for cloud removal in the remote sensing image sequence. Leveraging on group-sparsity constraint, we first decompose the observed cloud image sequence of the same area into the low-rank component, group-sparse outliers, and sparse noise, corresponding to cloud-free land-covers, clouds (and accompanying shadows), and noise respectively. Subsequently, a discriminative robust principal component analysis (RPCA) algorithm is utilized to assign aggressive penalizing weights to the initially detected cloud pixels to facilitate cloud removal and scene restoration. Moreover, we incorporate geometrical transformation into a low-rank model to address the misalignment of the image sequence. Significantly superior to conventional cloud-removal methods, neither cloud-free reference image(s) nor additional operations of cloud and shadow detection are required in our method. Extensive experiments on both simulated data and real data demonstrate that our method works effectively, outperforming many state-of-the-art approaches.