GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection

Change detection (CD) is an important application of remote sensing, which provides timely change information about large-scale Earth surface. With the emergence of hyperspectral imagery, CD technology has been greatly promoted, as hyperspectral data with the highspectral resolution are capable of detecting finer changes than using the traditional multispectral imagery. Nevertheless, the high dimension of hyperspectral data makes it difficult to implement traditional CD algorithms. Besides, endmember abundance information at subpixel level is often not fully utilized. In order to better handle high dimension problem and explore abundance information, this paper presents a General End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image change detection (HSI-CD). The main contributions of this work are threefold: 1) Mixed-affinity matrix that integrates subpixel representation is introduced to mine more cross-channel gradient features and fuse multi-source information; 2) 2-D CNN is designed to learn the discriminative features effectively from multi-source data at a higher level and enhance the generalization ability of the proposed CD algorithm; 3) A new HSI-CD data set is designed for the objective comparison of different methods. Experimental results on real hyperspectral data sets demonstrate the proposed method outperforms most of the state-of-the-arts

ES2Net: An Efficient Spectral-Spatial Network for Hyperspectral Image Change Detection

Hyperspectral image change detection (HSI-CD) aims to identify the differences in bitemporal HSIs. To mitigate spectral redundancy and improve the discriminativeness of changing features, some methods introduced band selection technology to select bands conducive for CD. However, these methods are limited by the inability to end-to-end training with the deep learning-based feature extractor and lack considering the complex nonlinear relationship among bands. In this paper, we propose an end-to-end efficient spectral-spatial change detection network (ES2Net) to address these issues. Specifically, we devised a learnable band selection module to automatically select bands conducive to CD. It can be jointly optimized with a feature extraction network and capture the complex nonlinear relationships among bands. Moreover, considering the large spatial feature distribution differences among different bands, we design the cluster-wise spatial attention mechanism that assigns a spatial attention factor to each individual band to individually improve the feature discriminativeness for each band. Experiments on three widely used HSI-CD datasets demonstrate the effectiveness and superiority of this method compared with other state-of-the-art methods

A Study on Change Detection in Hyperspectral Image

Change detection is the procedure of obtaining changes between two Hyperspectral pictures of same topographical zone taken at two unique times. It conveys the essential and important change data of a scene. Due to a breakthrough in Hyperspectral remote sensing Hyperspectral remote sensors can capable of producing narrow spectral resolution images. These high resolution spectral and spatial hyperspectral images can find small variations in images. This work describes an efficient algorithm for detecting changes in Hyperspectral images by using spectral signatures of Hyperspectral images. The objective is developing of a proficient algorithm that can show even small variations in Hyperspectral images. It reviews Hierarchical method for finding changes in Hyperspectral images by comparing spectral homogeneity between spectral change vectors. For any scenery locating and also exploration regarding adjust delivers treasured data regarding achievable changes. Hyperspectral satellite detectors get effectiveness throughout gathering data with large spectral rings. These types of detectors typically deal with spatially and also spectrally high definition graphics and this can be used by adjust discovery. This particular function is actually elaborated and also applied your adjust discovery procedure by simply controlling Hyperspectral graphics. The main aim with this thesis is actually studying and also constructing of Hyperspectral adjust discovery algorithms This kind of analysed approach is really applied to assess Hyperspectral picture image resolution files along with the approach analysed in this particular thesis is really change breakthrough making use of Hierarchical method of spectral change vectors and also making use of principal ingredient examination and also k-means clustering. This particular document offers applying and also verify of trends Hyperspectral image

CBANet: an end-to-end cross band 2-D attention network for hyperspectral change detection in remote sensing.

As a fundamental task in remote sensing observation of the earth, change detection using hyperspectral images (HSI) features high accuracy due to the combination of the rich spectral and spatial information, especially for identifying land-cover variations in bi-temporal HSIs. Relying on the image difference, existing HSI change detection methods fail to preserve the spectral characteristics and suffer from high data dimensionality, making them extremely challenging to deal with changing areas of various sizes. To tackle these challenges, we propose a cross-band 2-D self-attention Network (CBANet) for end-to-end HSI change detection. By embedding a cross-band feature extraction module into a 2-D spatial-spectral self-attention module, CBANet is highly capable of extracting the spectral difference of matching pixels by considering the correlation between adjacent pixels. The CBANet has shown three key advantages: 1) less parameters and high efficiency; 2) high efficacy of extracting representative spectral information from bi-temporal images; and 3) high stability and accuracy for identifying both sparse sporadic changing pixels and large changing areas whilst preserving the edges. Comprehensive experiments on three publicly available datasets have fully validated the efficacy and efficiency of the proposed methodology

Challenges and Opportunities of Multimodality and Data Fusion in Remote Sensing

International audience—Remote sensing is one of the most common ways to extract relevant information about the Earth and our environment. Remote sensing acquisitions can be done by both active (synthetic aperture radar, LiDAR) and passive (optical and thermal range, multispectral and hyperspectral) devices. According to the sensor, a variety of information about the Earth's surface can be obtained. The data acquired by these sensors can provide information about the structure (optical, synthetic aperture radar), elevation (LiDAR) and material content (multi and hyperspectral) of the objects in the image. Once considered together their comple-mentarity can be helpful for characterizing land use (urban analysis, precision agriculture), damage detection (e.g., in natural disasters such as floods, hurricanes, earthquakes, oil-spills in seas), and give insights to potential exploitation of resources (oil fields, minerals). In addition, repeated acquisitions of a scene at different times allows one to monitor natural resources and environmental variables (vegetation phenology, snow cover), anthropological effects (urban sprawl, deforestation), climate changes (desertification, coastal erosion) among others. In this paper, we sketch the current opportunities and challenges related to the exploitation of multimodal data for Earth observation. This is done by leveraging the outcomes of the Data Fusion contests, organized by the IEEE Geoscience and Remote Sensing Society since 2006. We will report on the outcomes of these contests, presenting the multimodal sets of data made available to the community each year, the targeted applications and an analysis of the submitted methods and results: How was multimodality considered and integrated in the processing chain? What were the improvements/new opportunities offered by the fusion? What were the objectives to be addressed and the reported solutions? And from this, what will be the next challenges

Application of Multi-Sensor Fusion Technology in Target Detection and Recognition

Application of multi-sensor fusion technology has drawn a lot of industrial and academic interest in recent years. The multi-sensor fusion methods are widely used in many applications, such as autonomous systems, remote sensing, video surveillance, and the military. These methods can obtain the complementary properties of targets by considering multiple sensors. On the other hand, they can achieve a detailed environment description and accurate detection of interest targets based on the information from different sensors.This book collects novel developments in the field of multi-sensor, multi-source, and multi-process information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Published papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems

Deep learning-based change detection in remote sensing images:a review

Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods