The fusion of hyperspectral images (HSIs) and LiDAR data can improve land cover classification performance. However, existing fusion methods do not well consider the large discrepancies between two modalities (e.g., b...
详细信息
ISBN:
(纸本)9798350344868;9798350344851
The fusion of hyperspectral images (HSIs) and LiDAR data can improve land cover classification performance. However, existing fusion methods do not well consider the large discrepancies between two modalities (e.g., brightness, structure, and the possible misalignment), leading to limited improvement. In this paper, we handle this problem in frequency domain and propose a cross-modal hierarchical frequency fusion network (HFNet) for joint classification of HSI and LiDAR data. First, we extract multi-level convolutional features from both modalities. Then, we explore spatial activation maps to adaptively fuse cross-modal frequency features at each level. Since the amplitude and phase information of two modalities are separately fused, the discrepancy problem is alleviated. Finally, we concatenate the fused features at all levels to build a classification loss and an auxiliary frequency consistency loss (FCL). FCL enables the concatenated feature to predict the amplitude and phase information of the input data, which acts as a regularization term and improves the model's discrimination ability. Experimental results on two datasets show the superiority of HFNet over the state-of-the-art methods in terms of classification performance.
VHR remotesensingimages have abundant ground features and details, but it is a great challenge for machine understanding. The "same object with different spectral"problem caused by environment changes, suc...
详细信息
We propose a multimodal domain-adaptive approach for remotesensingimage segmentation, which consists of a multimodal generator network together with a discriminator and adversarial strategy. The multimodal generator...
详细信息
Synthetic Aperture Radar (SAR) images own dominant merits, such as all-weather and all-day working conditions, but it is difficult for unprofessional people to interpret SAR images. Translating SAR images into optical...
详细信息
Land Use Land Cover change is considered an exciting topic in earth sciences and information technology. The real-time multispectral satellite images captured for finding the variations in the Earth's surface can ...
详细信息
remotesensingimages are usually blurred due to platform vibrations and camera defocus, which seriously limit its application. To solve the problem of blurring remotesensingimages caused by in-orbit angular vibrati...
详细信息
In the field of remotesensing, panchromatic sharpening technology integrates spatial data from panchromatic images with spectral data from multispectral images to generate high-resolution multispectral *** mapping fr...
详细信息
As the remotesensingimage information rapidly becomes abundant, it is a challenge for the detection of tiny targets with dense distribution. Therefore, a multi-scale rotating object detection model based on the impr...
详细信息
Quick-view system plays an indispensable row in space exploration and earth observation. Currently, the remotesensing quick-view system of our country only has quick display and store abilities. The motion-blurred re...
详细信息
Storing and processingremotesensing (RS) images require large amounts of memory space and computing resources. Consequently, RS images are compressed and stored in various compression formats, such as JPEG2000. Howe...
详细信息
ISBN:
(纸本)9781510655386;9781510655379
Storing and processingremotesensing (RS) images require large amounts of memory space and computing resources. Consequently, RS images are compressed and stored in various compression formats, such as JPEG2000. However, the processing of RS images for machine interpretation and understanding still necessitates the deployment of an image decompression stage in its entirety, followed by a computationally demanding image analysis pipeline. The image analysis stage is commonly composed of machine learning techniques, such as Deep Convolutional Neural Network (DCNN) models. Classification of remotesensingimages is among the most common image analysis tasks. In the scope of this paper, we propose a sub-band image based classification method for the remotesensing Scene Classification (RSSC) task in the JPEG2000 compressed domain. The proposed approach exploits the already available sub-band image coefficients to classify RS images without needing for full decompression. Our study shows that our method increases the high frequency information in the LL sub-band and allows the image to contain more detail, leading to improved classifier performance while taking advantage of the partial decompression method.
暂无评论