Scene Classification Is A Highly Useful Task In Remote Sensing (RS) Applications. Many Efforts Have Been Made To Improve The Accuracy Of RS Scene Classification. Scene Classification Is A Challenging Problem, Especially For Large Datasets With Tens Of Thousands Of Images With A Large Number Of Classes And Taken Under Different Circumstances. One Problem That Is Observed In Scene Classification Is The Fact That For A Given Scene, Only One Part Of It Indicates Which Class It Belongs To, Whereas The Other Parts Are Either Irrelevant Or They Actually Tend To Belong To Another Class. To Address This Issue, This Paper Proposes A Deep Attention Convolutional Neural Network (CNN) For Scene Classification In Remote Sensing. CNN Models Use Successive Convolutional Layers To Learn Feature Maps From Larger And Larger Regions (or Receptive Fields) Of The Scene. The Attention Mechanism Computes A New Feature Map As A Weighted Average Of These Original Feature Maps. In Particular, We Propose A Solution, Named EfficientNet-B3-Attn-2, Based On The Pre-trained EfficientNet-B3 CNN Enhanced With An Attention Mechanism. A Dedicated Branch Is Added To Layer 262 Of The Network, To Compute The Required Weights. These Weights Are Learned Automatically By Training The Whole CNN Model End-to-end Using The Back Propagation Algorithm. In This Way, The Network Learns To Emphasize Important Regions Of The Scene And Suppress The Regions That Are Irrelevant To The Classification. We Tested The Proposed EfficientNet-B3-Attn-2 On Six Popular Remote Sensing Datasets, Namely UC Merced, KSA, OPTIMAL-31, RSSCN7, WHU-RS19, And AID Datasets, Showing Its Strong Capabilities In Classifying RS Scenes.
Recently, The Convolutional Neural Network Has Brought Impressive Improvements For Object Detection. However, Detecting Tiny Objects In Large-scale Remote Sensing Images Still Remains Challenging. First, The Extreme Large Input Size Makes The Existing Object Detection Solutions Too Slow For Practical Use. Second, The Massive And Complex Backgrounds Cause Serious False Alarms. Moreover, The Ultra-tiny Objects Increase The Difficulty Of Accurate Detection. To Tackle These Problems, We Propose A Unified And Self-reinforced Network Called Remote Sensing Region-based Convolutional Neural Network (R2-CNN), Composing Of Backbone Tiny-Net, Intermediate Global Attention Block, And Final Classifier And Detector. Tiny-Net Is A Lightweight Residual Structure, Which Enables Fast And Powerful Features Extraction From Inputs. Global Attention Block Is Built Upon Tiny-Net To Inhibit False Positives. Classifier Is Then Used To Predict The Existence Of Target In Each Patch, And Detector Is Followed To Locate Them Accurately If Available. The Classifier And Detector Are Mutually Reinforced With End-to-end Training, Which Further Speed Up The Process And Avoid False Alarms. Effectiveness Of R2-CNN Is Validated On Hundreds Of GF-1 Images And GF-2 Images That Are 18 000×18 192 Pixels, 2.0-m Resolution, And 27 620 × 29 200 Pixels, 0.8-m Resolution, Respectively. Specifically, We Can Process A GF-1 Image In 29.4 S On Titian X Just With Single Thread. According To Our Knowledge, No Previous Solution Can Detect The Tiny Object On Such Huge Remote Sensing Images Gracefully. We Believe That It Is A Significant Step Toward Practical Real-time Remote Sensing Systems.
In This Paper, We Propose A Multiframe Marked Point Process Model Of Line Segments And Point Groups For Automatic Target Structure Extraction And Tracking In Inverse Synthetic Aperture Radar (ISAR) Image Sequences. To Deal With Scatterer Scintillations And High Speckle Noise In The ISAR Frames, We Obtain The Resulting Target Sequence By An Iterative Optimization Process, Which Simultaneously Considers The Observed Image Data And Various Prior Geometric Interaction Constraints Between The Target Appearances In The Consecutive Frames. A Detailed Quantitative Evaluation Is Performed On Eight Real ISAR Image Sequences Of Different Carrier Ships And Airplane Targets, Using A Test Database Containing 545 Manually Annotated Frames.
This Paper Proposes A Detailed Framework Of Storing Privacy Information In Surveillance Video As A Watermark. Authorized Personnel Is Not Only Removed From The Surveillance Video As In But Also Embedded Into The Video Itself, Which Can Only Be Retrieved With A Secrete Key. A Perceptual-model-based Compressed Domain Video Watermarking Scheme Is Proposed To Deal With The Huge Payload Problem In The Proposed Surveillance System. A Signature Is Also Embedded Into The Header Of The Video As In For Authentication. Simulation Results Have Shown That The Proposed Algorithm Can Embed All The Privacy Information Into The Video Without Affecting Its Visual Quality. As A Result, The Proposed Video Surveillance System Can Monitor The Unauthorized Persons In A Restricted Environment, Protect The Privacy Of The Authorized Persons But, At The Same Time, Allow The Privacy Information To Be Revealed In A Secure And Reliable Way.
With The Increasing Volume Of Video Data, How To Analyze And Browse Video In A Fast And Effective Way Has Become An Urgent Problem In Applications. This Letter Proposes A Novel Video Synopsis Method In Compressed Domain For Browsing Video Captured By Static Cameras. Synopsis Video Is A Video Abstraction, Which Displays Moving Objects From Different Periods Simultaneously On The Primary Background Contents Of Original Video. To Overcome The Low Efficiency Of Traditional Video Synopsis For Compressed Video, Our Method Presents A New Graph Cut Algorithm To Extract Objects Tubes And Meanwhile Gives A Fast Solution To Minimize Energy Function In Compressed Domain. Experimental Results In H.264 Video Have Demonstrated The High-efficiency Of This New Video Synopsis Scheme For Massive Video Browsing.
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