Remote Sensing Image Classification Research Articles

Event-driven spiking neural network based on membrane potential modulation for remote sensing image classification

Spiking neural network (SNN) based on sparse triggering and event-driven is a hardware-friendly model. SNN can provide an ultra-low power alternative for the deep neural network (DNN) to process remote sensing images. Brain information processing depends on the action potential of neurons. Therefore, the biological rationality of the artificial neural network (ANN) has been questioned. SNN is a more suitable model for brain information processing mechanisms. At present, the SNN obtained by ANN conversion has achieved the best performance in the current image processing tasks. However, the method based on ANN to SNN will have performance loss in the conversion process. Herein, we proposed a spiking neuron threshold-following reset (TF-reset) method and a membrane potential modulation method to reduce the loss of network conversion. We theoretically analyzed the proposed TF-reset and deduced the relationship between spike firing rate and neuron activation. In the experiment, we used an improved VGG-15 architecture combined with the method of transfer learning to apply the model to the classification task of remote sensing images. SNN-VGG-15 based on TF-reset and membrane potential modulation algorithm achieved a classification accuracy of 99.14%, 94.54%, and 95.00% on UCM, RSSCN7, and AID. Our algorithm can not only realize the lossless conversion of SNN but also outperforms the original network in classification performance on UCM and RSSCN7. In addition, our model also has advantages in energy consumption and noise robustness. The algorithm in this paper can provide a reference for the research remote sensing images procession using SNN.

TPENAS: A Two-Phase Evolutionary Neural Architecture Search for Remote Sensing Image Classification

The application of deep learning in remote sensing image classification has been paid more and more attention by industry and academia. However, manually designed remote sensing image classification models based on convolutional neural networks usually require sophisticated expert knowledge. Moreover, it is notoriously difficult to design a model with both high classification accuracy and few parameters. Recently, neural architecture search (NAS) has emerged as an effective method that can greatly reduce the heavy burden of manually designing models. However, it remains a challenge to search for a classification model with high classification accuracy and few parameters in the huge search space. To tackle this challenge, we propose TPENAS, a two-phase evolutionary neural architecture search framework, which optimizes the model using computational intelligence techniques in two search phases. In the first search phase, TPENAS searches for the optimal depth of the model. In the second search phase, TPENAS searches for the structure of the model from the perspective of the whole model. Experiments on three open benchmark datasets demonstrate that our proposed TPENAS outperforms the state-of-the-art baselines in both classification accuracy and reducing parameters.

Remote Sensing Image Classification with Few Labeled Data Using Semisupervised Learning

Synthetic aperture radar (SAR) as an imaging radar is capable of high-resolution remote sensing, independent of flight altitude, and independent of weather. Traditional SAR ship image classification tends to extract features manually. It relies too much on expert experience and is sensitive to the scale of SAR images. Recently, with the development of deep learning, deep neural networks such as convolutional neural networks are widely used to complete feature extraction and classification tasks, which improves algorithm accuracy and normalization capabilities to a large extent. However, deep learning requires a large number of labeled samples, and the vast bulk of SAR images are unlabeled. Therefore, the classification accuracy of deep neural networks is limited. To tackle the problem, we propose a semisupervised learning-based SAR image classification method considering that only few labeled images are available. The proposed method can train the classification model using both labeled and unlabeled samples. Moreover, we improve the unsupervised data augmentation (UDA) strategy by designing a symmetric function for unsupervised loss calculation. Experiments are carried out on the OpenSARShip dataset, and results show that the proposed method reaches a much higher accuracy than the original UDA.

Self-supervision assisted multimodal remote sensing image classification with coupled self-looping convolution networks

Recently, remote sensing community has seen a surge in the use of multimodal data for different tasks such as land cover classification, change detection and many more. However, handling multimodal data requires synergistically using the information from different sources. Currently, deep learning (DL) techniques are being religiously used in multimodal data fusion owing to their superior feature extraction capabilities. But, DL techniques have their share of challenges. Firstly, DL models are mostly constructed in the forward fashion limiting their feature extraction capability. Secondly, multimodal learning is generally addressed in a supervised setting, which leads to high labelled data requirement. Thirdly, the models generally handle each modality separately, thus preventing any cross-modal interaction. Hence, we propose a novel self-supervision oriented method of multimodal remote sensing data fusion. For effective cross-modal learning, our model solves a self-supervised auxiliary task to reconstruct input features of one modality from the extracted features of another modality, thus enabling more representative pre-fusion features. To counter the forward architecture, our model is composed of convolutions both in backward and forward directions, thus creating self-looping connections, leading to a self-correcting framework. To facilitate cross-modal communication, we have incorporated coupling across modality-specific extractors using shared parameters. We evaluate our approach on three remote sensing datasets, namely Houston 2013 and Houston 2018, which are HSI-LiDAR datasets and TU Berlin, which is an HSI-SAR dataset, where we achieve the respective accuracy of 93.08%, 84.59% and 73.21%, thus beating the state of the art by a minimum of 3.02%, 2.23% and 2.84%.

Deep Learning Methods Used in Remote Sensing Images: A Review

Undeniably, Deep Learning (DL) has rapidly eroded traditional machine learning in Remote Sensing (RS) and geoscience domains with applications such as scene understanding, material identification, extreme weather detection, oil spill identification, among many others. Traditional machine learning algorithms are given less and less attention in the era of big data. Recently, a substantial amount of work aimed at developing image classification approaches based on the DL model's success in computer vision. The number of relevant articles has nearly doubled every year since 2015. Advances in remote sensing technology, as well as the rapidly expanding volume of publicly available satellite imagery on a worldwide scale, have opened up the possibilities for a wide range of modern applications. However, there are some challenges related to the availability of annotated data, the complex nature of data, and model parameterization, which strongly impact performance. In this article, a comprehensive review of the literature encompassing a broad spectrum of pioneer work in remote sensing image classification is presented including network architectures (vintage Convolutional Neural Network, CNN; Fully Convolutional Networks, FCN; encoder-decoder, recurrent networks; attention models, and generative adversarial models). The characteristics, capabilities, and limitations of current DL models were examined, and potential research directions were discussed.

P2FEViT: Plug-and-Play CNN Feature Embedded Hybrid Vision Transformer for Remote Sensing Image Classification

Remote sensing image classification (RSIC) is a classical and fundamental task in the intelligent interpretation of remote sensing imagery, which can provide unique labeling information for each acquired remote sensing image. Thanks to the potent global context information extraction ability of the multi-head self-attention (MSA) mechanism, visual transformer (ViT)-based architectures have shown excellent capability in natural scene image classification. However, in order to achieve powerful RSIC performance, it is insufficient to capture global spatial information alone. Specifically, for fine-grained target recognition tasks with high inter-class similarity, discriminative and effective local feature representations are key to correct classification. In addition, due to the lack of inductive biases, the powerful global spatial context representation capability of ViT requires lengthy training procedures and large-scale pre-training data volume. To solve the above problems, a hybrid architecture of convolution neural network (CNN) and ViT is proposed to improve the RSIC ability, called P2FEViT, which integrates plug-and-play CNN features with ViT. In this paper, the feature representation capabilities of CNN and ViT applying for RSIC are first analyzed. Second, aiming to integrate the advantages of CNN and ViT, a novel approach embedding CNN features into the ViT architecture is proposed, which can make the model synchronously capture and fuse global context and local multimodal information to further improve the classification capability of ViT. Third, based on the hybrid structure, only a simple cross-entropy loss is employed for model training. The model can also have rapid and comfortable convergence with relatively less training data than the original ViT. Finally, extensive experiments are conducted on the public and challenging remote sensing scene classification dataset of NWPU-RESISC45 (NWPU-R45) and the self-built fine-grained target classification dataset called BIT-AFGR50. The experimental results demonstrate that the proposed P2FEViT can effectively improve the feature description capability and obtain outstanding image classification performance, while significantly reducing the high dependence of ViT on large-scale pre-training data volume and accelerating the convergence speed. The code and self-built dataset will be released at our webpages.

A multimodal hyper-fusion transformer for remote sensing image classification

The multispectral (MS) and the panchromatic (PAN) images represent complementary and synergistic spatial spectral information, how to make optimal use of the advantages of them has become a hot research topic. This paper proposes a selectable Transformer and Gist CNN network (STGC-Net). It designs a subspace similar recombination module (SSR-Module) based on non-negative matrix factorization (NMF) and the self-attention mechanism for feature decomposition. This can alleviate the redundant information of multi-modal data and extract their own singular and common features. Considering that the MS and the PAN images exhibit different advantageous properties, a selectable self-attention spectral feature extraction module (S3FE-Module) and a multi-stream Gist spatial feature extraction module (MGSFE-Module) are proposed for the different singular features. The former can refine the Transformer’s input and simultaneously characterize the sequence information between channels for the MS image. The latter introduces the positional relationship between local features while extracting spatial features for the PAN image, thereby improving the accuracy of scene classification. Experimental results indicate that the proposed method performs better than the other methods. The relevant code of this paper is provided at: https://github.com/ru-willow/ST-GC-Net.

Visual explanations with detailed spatial information for remote sensing image classification via channel saliency

For the past few years, the barrier of explainability accompanying by deep neural networks (DNNs) has been increasingly studied. The methods based on class activation map (CAM) which interpret the model decision by mapping the output back to the input space, have achieved a notable momentum among the research. However, the CAM-based methods cannot stably produce effective explanation results on remote sensing images (RSIs), owing to the coarse location map generated by high-level features, whereas, the RSIs contain abundant detailed spatial information and multi-scale objects. To address this issue, this article proposes class activation map weighted with channel saliency and gradient (CSG-CAM) to enhance the low-level features in saliency map. To do this, we firstly introduce the idea of dynamic channel pruning and propose the channel saliency to describe the channel importance of specific layer. Then the channel saliency, instead of gradient, is considered as the neuron importance weights to calculate the saliency map on shallow layer. Furthermore, the channel saliency also participates in the neuron importance weights of final layer, jointly with a gradient weighted combination of the positive partial derivatives. Finally, the saliency map of proposed CSG-CAM is fused by the explanation heat maps from shallow and final layer of networks. The sufficient experimental results on two publicly available datasets designed for RSI scene classification and three practical networks demonstrate the effectiveness of the CSG-CAM in terms of both faithfulness and explainability.

A decoupled search deep network framework for high-resolution remote sensing image classification

ABSTRACT Currently, high-resolution remote sensing image (HRI) interpretation is often done using deep learning methods. However, most deep learning networks currently used to classify HRIs rely on manually created skeletons, which are labour- and time-intensive and unable to completely identify the intrinsic features of the target image. To solve these problems, this study proposes a deep network skeleton search method based on a three-layer search space. A decoupled search approach was designed to optimize this three-layer search space. Our results demonstrated that the network architecture based on the decoupled search of the depth network skeleton outperforms the manually created depth network currently used for HRI classification. In particular, the mean Intersection over Union (mIoU), Accuracy (Acc) and Frequency Weighted Intersection over Union (FWIoU) values of the proposed method on the first test dataset were 0.902, 0.882 and 0.913, respectively, and those of the second test dataset were 0.887, 0.893 and 0.911, respectively. This method enables the development of autonomous designs of network skeletons, namely for HRI feature extraction and classification.

Study on Remote Sensing Image Classification of Oasis Area Based on ENVI Deep Learning

Study on Remote Sensing Image Classification of Oasis Area Based on ENVI Deep Learning

DMAU-Net: An Attention-Based Multiscale Max-Pooling Dense Network for the Semantic Segmentation in VHR Remote-Sensing Images

High-resolution remote-sensing images cover more feature information, including texture, structure, shape, and other geometric details, while the relationships among target features are more complex. These factors make it more complicated for classical convolutional neural networks to obtain ideal results when performing a feature classification on remote-sensing images. To address this issue, we proposed an attention-based multiscale max-pooling dense network (DMAU-Net), which is based on U-Net for ground object classification. The network is designed with an integrated max-pooling module that incorporates dense connections in the encoder part to enhance the quality of the feature map, and thus improve the feature-extraction capability of the network. Equally, in the decoding, we introduce the Efficient Channel Attention (ECA) module, which can strengthen the effective features and suppress the irrelevant information. To validate the ground object classification performance of the multi-pooling integration network proposed in this paper, we conducted experiments on the Vaihingen and Potsdam datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). We compared DMAU-Net with other mainstream semantic segmentation models. The experimental results show that the DMAU-Net proposed in this paper effectively improves the accuracy of the feature classification of high-resolution remote-sensing images. The feature boundaries obtained by DMAU-Net are clear and regionally complete, enhancing the ability to optimize the edges of features.

Estimating the shrimp farm’s production and their future growth prediction by remote sensing: Case study Gulf of California

Shrimp farms are within the main aquaculture activities and In the last 30 years, shrimp farms have expanded along the Gulf of California. Because the shrimp farm’s production is related to the pond area and the extended location of shrimp farms, we employed a multi-disciplinary approach to study the process of annual shrimp farm expansion, in addition to predicting shrimp production in the short term. Our study hypothesizes that semi-intensive shrimp farm production is dependent on the pond area which can be accurately quantified by using remote sensing image classification combined with the historical production data of shrimp aquaculture. We could calculate the long-term expansion of the total pond area and forecast the total annual shrimp production in the short-term. Our results highlight the development over the last 28 years. The total surface area of the shrimp ponds grew by more than 1100%. The linear regression model between the shrimp farms area, calculated using remote sensing, and statistical data on shrimp production, was strongly positive and significant (r2 = 0.874, p =2.209e-11). Likewise, the linear regression model for the total pond area as a function of time resulted strong positive and statistically significant (r2 = 0.936, p =2.917e-15). We demonstrated that the shrimp production could be assessed based on the total pond area by year, obtained by remote sensing. We estimated the past long-term shrimp farm expansion in the study zone using remote sensing data. This methodology is valuable for monitoring food security, aquaculture management decisions, and coastal ecology.

Block-Scrambling-Based Encryption with Deep-Learning-Driven Remote Sensing Image Classification

Remote sensing is a long-distance measuring technology that obtains data about a phenomenon or an object. Remote sensing technology plays a crucial role in several domains, such as weather forecasts, resource surveys, disaster evaluation and environment protection. The application of remote-sensing images (RSIs) is extensive in some specific domains, such as national security and business secrets. Simple multimedia distribution techniques and the development of the Internet make the content security of RSIs a significant problem for both engineers and scientists. In this background, RSI classification using deep learning (DL) models becomes essential. Therefore, the current research article develops a block-scrambling-based encryption with privacy preserving optimal deep-learning-driven classification (BSBE-PPODLC) technique for the classification of RSIs. The presented BSBE-PPODLC technique follows a two-stage process, i.e., image encryption and classification. Initially, the RSI encryption process takes place based on a BSBE approach. In the second stage, the image classification process is performed, and it encompasses multiple phases, such as densely connected network (DenseNet) feature extraction, extreme gradient boosting (XGBoost) classifier and artificial gorilla troops optimizer (AGTO)-based hyperparameter tuning. The proposed BSBE-PPODLC technique was simulated using the RSI dataset, and the outcomes were assessed under different aspects. The outcomes confirmed that the presented BSBE-PPODLC approach accomplished improved performance compared to the existing models.

Example-based explainable AI and its application for remote sensing image classification

We present a method of explainable artificial intelligence (XAI), “What I Know (WIK)”, to provide additional information to verify the reliability of a deep learning model by showing an example of an instance in a training dataset that is similar to the input data to be inferred and demonstrate it in a remote sensing image classification task. One of the expected roles of XAI methods is verifying whether inferences of a trained machine learning model are valid for an application, and it is an important factor that what datasets are used for training the model as well as the model architecture. Our data-centric approach can help determine whether the training dataset is sufficient for each inference by checking the selected example data. If the selected example looks similar to the input data, we can confirm that the model was not trained on a dataset with a feature distribution far from the feature of the input data. With this method, the criteria for selecting an example are not merely data similarity with the input data but also data similarity in the context of the model task. Using a remote sensing image dataset from the Sentinel-2 satellite, the concept was successfully demonstrated with reasonably selected examples. This method can be applied to various machine-learning tasks, including classification and regression.

HyFormer: Hybrid Transformer and CNN for Pixel-Level Multispectral Image Land Cover Classification.

To effectively solve the problems that most convolutional neural networks cannot be applied to the pixelwise input in remote sensing (RS) classification and cannot adequately represent the spectral sequence information, we propose a new multispectral RS image classification framework called HyFormer based on Transformer. First, a network framework combining a fully connected layer (FC) and convolutional neural network (CNN) is designed, and the 1D pixelwise spectral sequences obtained from the fully connected layers are reshaped into a 3D spectral feature matrix for the input of CNN, which enhances the dimensionality of the features through FC as well as increasing the feature expressiveness, and can solve the problem that 2D CNN cannot achieve pixel-level classification. Secondly, the features of the three levels of CNN are extracted and combined with the linearly transformed spectral information to enhance the information expression capability, and also used as the input of the transformer encoder to improve the features of CNN using the powerful global modelling capability of the Transformer, and finally the skip connection of the adjacent encoders to enhance the fusion between different levels of information. The pixel classification results are obtained by MLP Head. In this paper, we mainly focus on the feature distribution in the eastern part of Changxing County and the central part of Nanxun District, Zhejiang Province, and conduct experiments based on Sentinel-2 multispectral RS images. The experimental results show that the overall accuracy of HyFormer for the study area classification in Changxing County is 95.37% and that of Transformer (ViT) is 94.15%. The experimental results show that the overall accuracy of HyFormer for the study area classification in Nanxun District is 95.4% and that of Transformer (ViT) is 94.69%, and the performance of HyFormer on the Sentinel-2 dataset is better than that of the Transformer.

Deep Learning in Forest Tree Species Classification Using Sentinel-2 on Google Earth Engine: A Case Study of Qingyuan County

Forest tree species information plays an important role in ecology and forest management, and deep learning has been used widely for remote sensing image classification in recent years. However, forest tree species classification using remote sensing images is still a difficult task. Since there is no benchmark dataset for forest tree species, a forest tree species dataset (FTSD) was built in this paper to fill the gap based on the Sentinel-2 images. The FTSD contained nine kinds of forest tree species in Qingyuan County with 8,815 images, each with a resolution of 64 × 64 pixels. The images were produced by combining forest management inventory data and Sentinel-2 images, which were acquired with less than 20% clouds from 1 April to 31 October, including the years 2017, 2018, 2019, 2020, and 2021. Then, the images were preprocessed and downloaded from Google Earth Engine (GEE). Four different band combinations were compared in the paper. Moreover, a Principal Component Analysis (PCA) and Normalized Difference Vegetation Index (NDVI) were also calculated using the GEE. Deep learning algorithms including DenseNet, EfficientNet, MobileNet, ResNet, and ShuffleNet were trained and validated in the FTSD. RGB images with red, green, and blue (PC1, PC2, and NDVI) obtained the highest validation accuracy in four band combinations. ResNet obtained the highest validation accuracy in all algorithms after 500 epochs were trained in the FTSD, which reached 84.91%. As a famous and widely used remote sensing classification satellite imagery dataset, NWPU RESISC-45 was also trained and validated in the paper. ResNet achieved a high validation accuracy of 87.90% after training 100 epochs in NWPU RESISC-45. The paper shows in forest tree species classification based on remote sensing images and deep learning that (1) PCA and NDVI can be combined to improve the accuracy of classification; (2) ResNet is more suitable than other deep learning algorithms including DenseNet, EfficientNet, MobileNet, and ShuffleNet in remote sensing classification; and (3) being too shallow or deep in ResNet does not perform better in the FTSD, that is, 50 layers are better than 34 and 101 layers.

Exploiting Superpixel-Based Contextual Information on Active Learning for High Spatial Resolution Remote Sensing Image Classification

Superpixel-based classification using Active Learning (AL) has shown great potential in high spatial resolution remote sensing image classification tasks. However, in existing superpixel-based classification models using AL, the expert labeling information is only used on the selected informative superpixel while its neighboring superpixels are ignored. Actually, as most superpixels are over-segmented, a ground object always contains multiple superpixels. Thus, the center superpixel tends to have the same label as its neighboring superpixels. In this paper, to make full use of the expert labeling information, a Similar Neighboring Superpixels Search and Labeling (SNSSL) method was proposed and used in the AL process. Firstly, we identify superpixels with certain categories and uncertain superpixels by supervised learning. Secondly, we use the active learning method to process those uncertain superpixels. In each round of AL, the expert labeling information is not only used to enrich the training set but also used to label the similar neighboring superpixels. Similar neighboring superpixels are determined by computing the similarity of two superpixels according to CIELAB Dominant Colors distance, Correlation distance, Angular Second Moment distance and Contrast distance. The final classification map is composed of the supervised learning classification map and the active learning with SNSSL classification map. To demonstrate the performance of the proposed SNSSL method, the experiments were conducted on images from two benchmark high spatial resolution remote sensing datasets. The experiment shows that overall accuracy, average accuracy and kappa coefficients of the classification using the SNSSL have been improved obviously compared with the classification without the SNSSL.

Deep Network Architectures as Feature Extractors for Multi-Label Classification of Remote Sensing Images

Data in the form of images are now generated at an unprecedented rate. A case in point is remote sensing images (RSI), now available in large-scale RSI archives, which have attracted a considerable amount of research on image classification within the remote sensing community. The basic task of single-target multi-class image classification considers the case where each image is assigned exactly one label from a predefined finite set of class labels. Recently, however, image annotations have become increasingly complex, with images labeled with several labels (instead of just one). In other words, the goal is to assign multiple semantic categories to an image, based on its high-level context. The corresponding machine learning tasks is called multi-label classification (MLC). The classification of RSI is currently predominantly addressed by deep neural network (DNN) approaches, especially convolutional neural networks (CNNs), which can be utilized as feature extractors as well as end-to-end methods. After only considering single-target classification for a long period, DNNs have recently emerged that address the task of MLC. On the other hand, trees and tree ensembles for MLC have a long tradition and are the best-performing class of MLC methods, but need predefined feature representations to operate on. In this work, we explore different strategies for model training based on the transfer learning paradigm, where we utilize different families of (pre-trained) CNN architectures, such as VGG, EfficientNet, and ResNet. The architectures are trained in an end-to-end manner and used in two different modes of operation, namely, as standalone models that directly perform the MLC task, and as feature extractors. In the latter case, the learned representations are used with tree ensemble methods for MLC, such as random forests and extremely randomized trees. We conduct an extensive experimental analysis of methods over several publicly available RSI datasets and evaluate their effectiveness in terms of standard MLC measures. Of these, ranking-based evaluation measures are most relevant, especially ranking loss. The results show that, for addressing the RSI-MLC task, it is favorable to use lightweight network architectures, such as EfficientNet-B2, which is the best performing end-to-end approach, as well as a feature extractor. Furthermore, in the datasets with a limited number of images, using traditional tree ensembles for MLC can yield better performance compared to end-to-end deep approaches.

Dynamic Loss Reweighting Method Based on Cumulative Classification Scores for Long-Tailed Remote Sensing Image Classification

Convolutional neural networks have been widely used in remote sensing classification and achieved quite good results. Most of these methods are based on datasets with relatively balanced samples, but such ideal datasets are rare in applications. Long-tailed datasets are very common in practice, and the number of samples among categories in most datasets is often severely uneven and leads to bad results, especially in the category with a small sample number. To address this problem, a novel remote sensing image classification method based on loss reweighting for long-tailed data is proposed in this paper to improve the classification accuracy of samples from the tail categories. Firstly, abandoning the general weighting approach, the cumulative classification scores are proposed to construct category weights instead of the number of samples from each category. The cumulative classification score can effectively combine the number of samples and the difficulty of classification. Then, the imbalanced information of samples from each category contained in the relationships between the rows and columns of the cumulative classification score matrix is effectively extracted and used to construct the required classification weights for samples from different categories. Finally, the traditional cross-entropy loss function is improved and combined with the category weights generated in the previous step to construct a new loss reweighting mechanism for long-tailed data. Extensive experiments with different balance ratios are conducted on several public datasets, such as HistAerial, SIRI-WHU, NWPU-RESISC45, PatternNet, and AID, to verify the effectiveness of the proposed method. Compared with other similar methods, our method achieved higher classification accuracy and stronger robustness.

Evaluation and Prediction of Landscape Ecological Security Based on a CA-Markov Model in Overlapped Area of Crop and Coal Production

Overlapped areas of crop and coal production are responsible for national food security and mineral resource supply. However, long-term coal mining and urban and rural construction have continuously impacted the structure and functions of the original agricultural landscape ecosystem in the area and brought serious ecological security problems. On the basis of the results of remote sensing image classification of the Zhaogu mining area, this study explores the spatio-temporal variation characteristics of the landscape pattern and the evolution of ecological security and predicts the landscape ecological security status in 2029. Research results show that the evolution of the landscape pattern in the study area manifests obvious stage characteristics: from 2004 to 2014, the landscape pattern developed in the direction of fragmentation, irregularity, heterogeneity, and low connectivity; after 2014, the landscape pattern showed continuity, regularization, and high connectivity trends. By comparing the landscape ecological security of the study area from 2004 to 2019, a temporal change characteristic of first deteriorating and then gradually improving can be observed. By analyzing the changes in the distribution of the security status in the study area on a spatial scale, it can be found that the proportion of unsafe areas is significantly reduced in 2019 due to the influence of land remediation and reclamation. By constructing a CA-Markov prediction model with both spatial and quantitative advantages, the prediction results show that the degree of landscape fragmentation in the study area will be reduced, and the connectivity will be enhanced between 2019 and 2029. The shape of landscape patches tends to be regular, and the landscape heterogeneity will be enhanced. Overall, the landscape ecological security situation will continue to improve. The results could provide reference for ecological protection and related land reclamation planning of the mine area.