Ship detection is a challenging problem in complex optical remote-sensing images. In this letter, an effective ship detection framework in remote-sensing images based on the convolutional neural network is proposed. The framework is designed to predict bounding box of ship with orientation angle information. Note that the angle information which is added to bounding box regression makes bounding box accurately fit into the ship region. In order to make the model adaptable to the detection of multiscale ship targets, especially small-sized ships, we design the network with feature maps from the layers of different depths. The whole detection pipeline is a single network and achieves real-time detection for a 704 x 704 image with the use of Titan X GPU acceleration. Through experiments, we validate the effectiveness, robustness, and accuracy of the proposed ship detection framework in complex remote-sensing scenes.

Object detection in complex optical remote sensing images is a challenging problem due to the wide variety of scales, densities, and shapes of object instances on the earth surface. In this letter, we focus on the wide-scale variation problem of multiclass object detection and propose an effective object detection framework in remote sensing images based on YOLOv2. To make the model adaptable to multiscale object detection, we design a network that concatenates feature maps from layers of different depths and adopt a feature introducing strategy based on oriented response dilated convolution. Through this strategy, the performance for small-scale object detection is improved without losing the performance for large-scale object detection. Compared to YOLOv2, the performance of the proposed framework tested in the DOTA (a large-scale data set for object detection in aerial images) data set improves by 4.4% mean average precision without adding extra parameters. The proposed framework achieves real-time detection for $1024\times 1024$ image using Titan Xp GPU acceleration. ^{1 1}https://github.com/WenchaoliuMUC/Detection-of-Multiclass-Objects-in-Optical-Remote-Sensing-Images

In recent times, many efforts have been made to improve remote sensing image scene classification, especially using popular deep convolutional neural networks. However, most of these methods do not consider the specific scene orientation of the remote sensing images. In this letter, we propose the improved oriented response network (IORN), which is based on the ORN, to handle the orientation problem in remote sensing image scene classification. We propose average active rotating filters (A-ARFs) in the IORN. While IORNs are being trained, A-ARFs are updated by a method that is different from the ARFs of the ORN, without additional computations. This change helps IORN improve its ability to encode orientation information and speeds up optimization during training. We also propose Squeeze-ORAlign (S-ORAlign) by adding a squeeze layer to ORAlign of ORN. With the squeeze layer, S-ORAlign can address large-scale images, unlike ORAlign. An ablation study and comparison experiments are designed on a public remote sensing image scene classification data set. The experimental results demonstrate the effectiveness and better performance of the proposed model over that of other state-of-the-art models.

Traditional region-of-interest (ROI) detection methods for remote sensing images are generally formulated at pixel level and are less efficient when applied on large high-resolution images. This letter presents an accurate and efficient approach via superpixel-to-pixel saliency analysis for ROI detection. At first, the image is downsampled and segmented into superpixels by simple linear iterative clustering. Next, structure tensor and background contrast are used to yield superpixel feature maps for texture and color. After fusing the feature maps, the overall superpixel saliency map is obtained and then used to achieve the final pixel-level saliency map by superpixel-to-pixel mapping. Through experimentations, we validate the effectiveness and computational efficiency of the proposed model in comparison with state-of-the-art techniques.

Recently, extensive convolutional neural network (CNN)-based methods have been used in remote sensing applications, such as object detection and classification, and have achieved significant improvements in performance. Furthermore, there are a lot of hardware implementation demands for remote sensing real-time processing applications. However, the operation and storage processes in floating-point models hinder the deployment of networks in hardware implements with limited resource and power budgets, such as field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs). To solve this problem, this paper focuses on optimizing the hardware design of CNN with low bit-width integers by quantization. First, a symmetric quantization scheme-based hybrid-type inference method was proposed, which uses the low bit-width integer to replace floating-point precision. Then, a training approach for the quantized network is introduced to reduce accuracy degradation. Finally, a processing engine (PE) with a low bit-width is proposed to optimize the hardware design of FPGA for remote sensing image classification. Besides, a fused-layer PE is also presented for state-of-the-art CNNs equipped with Batch-Normalization and LeakyRelu. The experiments performed on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset using a graphics processing unit (GPU) demonstrate that the accuracy of 8-bit quantized model drops by about 1%, which is an acceptable accuracy loss. The accuracy result tested on FPGA is consistent with that of GPU. As for the resource consumptions of FPGA, the Look Up Table (LUT), Flip-flop (FF), Digital Signal Processor (DSP), and Block Random Access Memory (BRAM) are reduced by 46.21%, 43.84%, 45%, and 51%, respectively, compared with that of floating-point implementation.

Sea–land segmentation is a key step for some important applications of panchromatic remote sensing image processing. However, robust and effective sea–land segmentation for high-resolution panchromatic remote sensing images is still a challenging problem. This letter presents an accurate and robust approach by integrating the improved multiscale normalized cut (IMNcut) method and improved Chan–Vese model for sea–land segmentation. At first, the image is downsampled and segmented into multiple regions by the IMNcut method. Next, the homogeneous regions are merged to obtain a coarse segmentation result. Finally, gray intensity and local entropy features are integrated as discriminants of the improved Chan–Vese model, which is used to obtain the final segmentation result through a low- to high-resolution segmentation scheme. Experimental results performed on several real data sets demonstrate the effectiveness of the proposed model in terms of visual and objective evaluations.