Change detection with remote sensing images (RSIs) plays an important role in the community of remote sensing applications. However, when change detection is conducted with hyperspectral remote sensing images (HRSIs), how to measure the change magnitude between bitemporal HRSIs becomes challenging due to the high dimension of HRSIs. In this article, a novel Distribution Distance based on Inconsistent Adaptive Region (D2IAR) change detection approach is proposed to measure the change magnitude between bitemporal HRSIs for improving the performance of change detection with HRSIs. First, a band selection algorithm called optimal neighborhood reconstruction is employed to reduce the dimensions of HRSIs. Then, an adaptive region around each pixel is generated to explore the contextual feature around each pixel, and kernel density estimation is suggested to estimate the spectral distribution of the pixels within an adaptive region. A distribution distance is defined based on the adaptive region to measure the change magnitude between bitemporal HRSIs. Finally, the change magnitude between pairwise adaptive regions is measured by the proposed distance between the pairwise distributions. Experimental results based on four datasets and comparisons with eight methods indicated the feasibility and superiorities of the proposed D2IAR-based change detection approach with HRSIs. The improvement rates are approximately 0.13%-24.04% for overall accuracy. The code and datasets can be available at: https://github.com/ImgSciGroup/2024-HSICD.

Learning performance is unsatisfactory when training deep-learning networks without prior-knowledge guidance. In this paper, a multi-scale change detection neural network guided by a change gradient image (CGI) was proposed. First, a multi-scale information attentional module was embedded in the backbone of UNet to achieve a multi-scale information fusion task of bi-temporal images. Second, the position channel attention module was promoted to make the neural network pay more attention to the spectral and spatial information in the multi-scale fused feature map. Finally, a change gradient guide module was proposed to optimize backpropagation and overcome the negative effects of pseudo-change. Compared with seven state-of-the-art methods using three pairs of real remote sensing images, the proposed approach could smoothen the salt-and-pepper noise from the detection maps and improve the detection accuracy. The quantitative improvements are about 1.67% and 3.00% in terms of overall accuracy and Kappa coefficient, respectively, thus confirming the feasibility and superiority of the proposed approach for detecting land cover change with remotely sensed images. Code: https://github.com/ImgSciGroup/MACGGNet.git.

Land cover change detection (LCCD) using bitemporal remote sensing images is a crucial task for identifying the change areas on the Earth’s surface. However, the utilization of hyperspectral remote sensing images (HRSIs) introduces challenges as the detection performance is affected by the spectral noise and deducing change detection accuracies. In this work, we concentrated on utilizing spatial-contextual information to improve the change detection performance while using HRSIs. First, a band selection approach is used to minimize the spectral redundancy of HRSIs. Second, an iterative spatial-adaptive filter is proposed to smooth the noise of HRSIs. Thereafter, the change magnitude between bitemporal HRSIs is measured by coupling change vector analysis and the adaptive region around each pixel, resulting in a change magnitude image (CMI). Subsequently, the CMI is divided into a binary change detection map by using an Ostu threshold method. The experimental results on three pairs of real HRSIs efficiently demonstrated the feasibility and superiorities of the proposed approach compared with six state-of-art methods. For example, the improvement rates are approximately 0.43%-11.83% and 1.05%-15.41% for overall accuracy and average accuracy, respectively. The code of our proposed approach will be available at: https://github.com/ImgSciGroup/2023-HSICD.

With the fast development of remote sensing platforms and sensors technology, change detection with heterogeneous remote sensing images (Hete-CD) has become an attractive topic in recent years and plays a vital role in land cover change detection for responding to natural disaster emergencies when homogeneous images are unavailable. Although Hete-CD has been developed for about three decades, and various related methods have been developed and applied successfully in practice, a systematic and comprehensive review of the current achievements regarding Hete-CD remains lacking. Therefore, in this article, we first present an overview of Hete-CD in terms of the related literature. Second, the major techniques of Hete-CD are reviewed in terms of publicly available datasets, the taxonomy of major techniques, results, performance, and quantitative evaluation. Then, some classical methods are selected for comparison and discussion. Finally, based on the discussion and literature review, challenges, opportunities, and future directions for Hete-CD are concluded. The review aims to provide a “one-stop-shop” understanding of the problems with the categories of existing approaches, open opportunities and challenges, and potential future directions for Hete-CD.

Landslide inventory mapping (LIM) is an important application in remote sensing for assisting in the relief of landslide geohazards. However, while conducting LIM tasks performing change detection analysis using bi-temporal very high-resolution (VHR) remote sensing images, due to landslide usually occurred in a mountain area, the phenological difference and outcrop rock may bring pseudo-changes to LIM results. In this paper, a novel change detection approach based on Adaptive Region Shape Similarity (ARSS) is proposed for LIM with VHR remote sensing images to improve detection performance. First, an adaptive region around each pixel is extended to explore the contextual information. Then, direction lines within an adaptive region are defined to describe the shape of the adaptive region. Finally, the pixels located on each direction line are taken into account to build the corresponding histogram. The shape similarity between the pairwise histogram curves is measured by using the Discrete Frchet Distance (DFD). Once the bi-temporal images are processed by using the abovementioned steps, a change magnitude image (CMI) is generated, while a threshold is then used to obtain a final binary change map. The proposed approach is applied to three pairs of landslide sites images acquired with aerial plane and one land use change dataset acquired by Quick Bird Satellite. Compared with ten state-of-the-art methods, the proposed approach achieved LIMs and detection results with higher accuracies and better performance.

This study presents preliminary observations of the first year of a crop monitoring experiment occurred in two soybean agriculture fields in the Arkansas delta. The project focuses on developing image processing and data integration techniques for UAV-based images to optimize advanced farm management such as soil microbial amendments. In particular, we present an effective algorithm that can use high-resolution UAV images efficiently to estimate sprout density and plant vigor/health throughout the growing season. Such plant characterization is extremely important for the identification of anomalous areas and provide easily interpretable information for a better decision making. We also present an integrative analysis of UAV-data with geophysical data and harvesting data, which shows high correlation between persistent spatial pattern of soil, plant phenology/growth, and crop yield.

Morphological attribute profiles are multilevel decompositions of images obtained with a sequence of transformations performed by connected operators. They have been extensively employed in performing multi-scale and region-based analysis in a large number of applications. One main, still unresolved, issue is the selection of filter parameters able to provide representative and non-redundant threshold decomposition of the image. This paper presents a framework for the automatic selection of filter thresholds based on Granulometric Characteristic Functions (GCFs). GCFs describe the way that non-linear morphological filters simplify a scene according to a given measure. Since attribute filters rely on a hierarchical representation of an image (e.g., the Tree of Shapes) for their implementation, GCFs can be efficiently computed by taking advantage of the tree representation. Eventually, the study of the GCFs allows the identification of a meaningful set of thresholds. Therefore, a trial and error approach is not necessary for the threshold selection, automating the process and in turn decreasing the computational time. It is shown that the redundant information is reduced within the resulting profiles (a problem of high occurrence, as regards manual selection). The proposed approach is tested on two real remote sensing data sets, and the classification results are compared with strategies present in the literature.

Satellite image classification is a key task used in remote sensing for the automatic interpretation of a large amount of information. Today there exist many types of classification algorithms using advanced image processing methods enhancing the classification accuracy rate. One of the best state-of-the-art methods which improves significantly the classification of complex scenes relies on Self-Dual Attribute Profiles (SDAPs). In this approach, the underlying representation of an image is the Tree of Shapes, which encodes the inclusion of connected components of the image. The SDAP computes for each pixel a vector of attributes providing a local multiscale representation of the information and hence leading to a fine description of the local structures of the image. Instead of performing a pixel-wise classification on features extracted from the Tree of Shapes, it is proposed to directly classify its nodes. Extending a specific interactive segmentation algorithm enables it to deal with the multi-class classification problem. The method does not involve any statistical learning and it is based entirely on morphological information related to the tree. Consequently, a very simple and effective region-based classifier relying on basic attributes is presented.

A novel unsupervised change detection approach for multi-spectral remote sensing data based on morphological transformation is presented. Profiles obtained by attribute filters can provide a rich multi-level analysis of the contextual information. The proposed method is based on the assumption that pixels belonging to changed areas exhibit profiles with significant differences due to a variation in their geometry, whereas pixels within unchanged areas result in similar profiles due to their similar spatial characteristics. The extension to the multi-spectral scenario is performed by applying the morphological analysis on the available bands that compose a given data set. In such scenario radiometric normalization results mandatory in order to minimize the effect due to different acquisition's conditions. To this purpose, IR-MAD is performed as pre-processing. In the paper, preliminary results obtained considering a multi-temporal Landsat ETM+ data set acquired over an agriculture area are shown.

In this letter, we propose a new version of the rotation forest (RoF) method for the pixelwise classification of hyperspectral images. RoF, which is an ensemble of decision tree classifiers, uses random feature selection and data transformation techniques (i.e., principal component analysis) to improve both the accuracy of base classifiers and the diversity within the ensemble. Traditional RoF performs data transformation on the training samples of each subset. In order to further improve the performance of RoF, the data transformation is separately performed on each class, extracting sets of transformation matrices that are strictly dependent on the training samples of each single class. The approach, namely, class-separation-based RoF (RoFCS), is experimentally investigated on a hyperspectral image collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. Experimental results demonstrate that the proposed methodology achieves excellent performances, in comparison with random forest and RoF classifiers.

Attribute profiles (APs) have increasingly been receiving more attention over the last years, as they are able to extract and model spatial information that is useful for the analysis of remote sensing images of very high spatial resolution (VHR). However, one of the major issues in employing APs is the choice of a proper range of thresholds, able to provide a representative and non-redundant multi-level image decomposition. This paper presents a novel method for the automatic selection of adequate thresholds to compute the AP. A new concept of cumulative function, which can be seen as an extension of the basic notion of granulometry, is introduced. In particular, different information on the spatial context is achieved according to the measure used for computing the cumulative function, which is computed on the AP composed by considering all possible values of the attribute. The proposed approach aims at selecting the set of thresholds that provides the best approximation of the resulting cumulative function based on the chosen measure. Experimental analysis carried out on a very high resolution image shows the effectiveness of the presented strategy in providing a set of thresholds able to retain the salient spatial structures in the scene.

With respect to the exponential increase in the number of available remote sensors in recent years, the possibility of having different types of data captured over the same scene, has resulted in many research works related to the joint use of passive and active sensors for the accurate classification of different materials. However, until now, there is a small number of research works related to the integration of highly valuable information obtained from the joint use of LiDAR and hyperspectral data. This paper proposes an efficient classification approach in terms of accuracies and demanded CPU processing time for integrating big data sets (e.g., LiDAR and hyperspectral) to provide land cover mapping capabilities at a range of spatial scales. In addition, the proposed approach is fully automatic and is able to efficiently handle big data containing a huge number of features with very limited number of training samples in few seconds.

This is a selection of results of the North State project, that demonstrate how innovative methods applied to the new Sentinel data streams can be combined with models to monitor carbon and water fluxes for pan-boreal Europe.

The availability of hyperspectral images with improved spectral and spatial resolutions provides the opportunity to obtain accurate land-cover classification. In this paper, a novel methodology that combines spectral and spatial information for supervised hyperspectral image classification is proposed. A feature reduction strategy based on independent component analysis is the main core of the spectral analysis, where the exploitation of prior information coupled to the evaluation of the reconstruction error assures the identification of the best class-informative subset of independent components. Reduced attribute profiles (APs), which are designed to address well-known issues related to information redundancy that affect the common morphological APs, are then employed for the modeling and fusion of the contextual information. Four real hyperspectral data sets, which are characterized by different spectral and spatial resolutions with a variety of scene typologies (urban, agriculture areas), have been used for assessing the accuracy and generalization capabilities of the proposed methodology. The obtained results demonstrate the classification effectiveness of the proposed approach in all different scene typologies, with respect to other state-of-the-art techniques.

This article proposes a feature reduction technique for hyperspec-tral images using Independent Component Analysis (ICA). The proposed technique aims at extracting the best subset of class-informative independent components (ICs) for hyperspectral supervised classification. The selection of the most representative components is assured by the minimization of the reconstruction error, which is computed on the training samples used for the supervised classification. The searching strategy is optimized by exploiting a genetic algorithm-based approach where the fitness function is the classification accuracy obtained by using a support vector machine (SVM) classifier. The obtained results show the effectiveness of the proposed approach in providing class-informative components to improve the classification accuracy.

A new approach to change detection in very high resolution remote sensing images based on morphological attribute profiles (APs) is presented. A multiresolution contextual transformation performed by APs allows the extraction of geometrical features related to the structures within the scene at different scales. The temporal changes are detected by comparing the geometrical features extracted from the image of each date. The experiments performed on panchromatic QuickBird images related to an urban area show the effectiveness of the proposed technique in detecting changes on the basis of the spatial morphology by preserving geometrical detail.

In change detection analysis, the computation of the no-change distribution is affected when changed pixels are in large number in the scene. Because of this, the performance of several techniques are compromised. In this paper we compare two well known automatic change detection techniques (ITPCA and IRMAD) by performing an initial elimination of the strong changes in order to minimize the contribution of the changed pixels to the radiometric normalization computation. These two techniques are ineffective in correctly estimating the distribution of the no-change pixels when this kind of scenario is encountered. The strong changes are identified by building an initial change mask (ICM), which is based on the statistical analysis of the given data set. In this paper we show two simple algorithms for building the ICM. From the experiments on a data set characterized by a high amount of changes due to the agriculture activity, the improvement in quality of the map of changes obtained by the proposed approach with respect to the ones obtained without using the ICM has been observed.