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Changes to Captions: An Attentive Network for Remote Sensing Change Captioning

Shizhen Chang, Pedram Ghamisi

arXiv:2304.01091v2cs.CVcs.AI

TL;DR

Remote-sensing change captioning must describe changes despite illumination, seasonal, and land-cover variation. Chg2Cap combines an attentive encoder with a transformer-based caption decoder. It outperforms comparison methods on Dubai-CC and LEVIR-CC using the reported BLEU-4 and CIDEr-D comparisons.

  • Problem

    Remote-sensing change captioning must describe changes despite illumination and seasonal variation and complex land-cover distributions.

    Source: §I · p. 2

  • Method

    Chg2Cap uses a CNN feature extractor, an attentive encoder with hierarchical self-attention and a residual block, and a caption decoder.

    Source: A. Overview · p. 4

  • Results

    On Dubai-CC, Chg2Cap surpasses CC-SVM by 4.6% on BLEU-4 and 3.9% on CIDEr-D; on LEVIR-CC, it surpasses RSICCformer by 5.3% on BLEU-4 and 5.2% on CIDEr-D.

    Source: F. Comparison to the State of the Art · p. 12

  • Takeaways & Limitations

    The authors present Chg2Cap as generating more accurate and realistic descriptions of remote-sensing changes than existing methods.

    Source: §I · p. 3

Abstract

from arXiv · show

In recent years, advanced research has focused on the direct learning and analysis of remote sensing images using natural language processing (NLP) techniques. The ability to accurately describe changes occurring in multi-temporal remote sensing images is becoming increasingly important for geospatial understanding and land planning. Unlike natural image change captioning tasks, remote sensing change captioning aims to capture the most significant changes, irrespective of various influential factors such as illumination, seasonal effects, and complex land covers. In this study, we highlight the significance of accurately describing changes in remote sensing images and present a comparison of the change captioning task for natural and synthetic images and remote sensing images. To address the challenge of generating accurate captions, we propose an attentive changes-to-captions network, called Chg2Cap for short, for bi-temporal remote sensing images. The network comprises three main components: 1) a Siamese CNN-based feature extractor to collect high-level representations for each image pair; 2) an attentive decoder that includes a hierarchical self-attention block to locate change-related features and a residual block to generate the image embedding; and 3) a transformer-based caption generator to decode the relationship between the image embedding and the word embedding into a description. The proposed Chg2Cap network is evaluated on two representative remote sensing datasets, and a comprehensive experimental analysis is provided. The code and pre-trained models will be available online at https://github.com/ShizhenChang/Chg2Cap.

I. INTRODUCTION

Remote-sensing change captioning must describe meaningful changes despite acquisition differences and complex land covers, while attending to distinctive geographic changes. The paper proposes Chg2Cap and reports stronger performance than existing change-captioning methods.

  • Change captions may support urban planning, disaster management, and environmental monitoring by improving understanding of built-environment change.Source: §I · p. 1
  • Remote-sensing change captions must remain accurate across illumination, seasonal, and acquisition shifts while distinguishing changes amid complex land-cover distributions.Source: §I · p. 2
  • Top-down remote-sensing views compress color, height, and texture into two-dimensional surface information, making object species harder to identify.Source: §I · pp. 2–3
  • Chg2Cap uses hierarchical self-attention and a residual block in an attentive encoder, alongside a transformer-based caption generator with residual connections.The encoder captures inter- and intra-information in deep features; the residual block uses a cosine mask to enhance consistency and inconsistency between retrieved feature pairs.Source: §I · p. 3
  • Extensive experiments reportedly show Chg2Cap generates more accurate, realistic change descriptions and performs best among existing change-captioning methods.Source: §I · p. 3
  • The study analyzes attention mechanisms in image-feature representation and caption generation through parametric analysis and evaluation of network settings.Source: §I · p. 3

II. RELATED WORKS

The related work reviews image-caption generation and attention-based approaches, then positions Chg2Cap as applying attention to both bitemporal image embeddings and caption generation.

A. Image-to-Caption Generation

  • Image captioning methods include template-based, retrieval-based, and sequence-generation approaches; sequence methods learn relationships between image and word embeddings for flexible descriptions.Source: A. Image-to-Caption Generation · p. 3
  • Remote-sensing captioning work introduced CNN-RNN systems and datasets, while later studies developed larger datasets and change-captioning methods for multi-temporal imagery.Earlier remote-sensing change-captioning work used fusion strategies on small datasets; subsequent work introduced a large-scale dataset and transformer-based description generation.Source: A. Image-to-Caption Generation · pp. 3–4

B. Attention in Image Captioning

  • Image-captioning research uses soft, hard, adaptive, and object-region attention, with remote-sensing methods also incorporating structured, transformer, and multi-layer attention.Source: B. Attention in Image Captioning · p. 4
  • Change-captioning methods use attention to identify changes and contrast features, and Chg2Cap applies attention in both image-embedding extraction and caption generation.Source: B. Attention in Image Captioning · p. 4

III. METHODOLOGY

Chg2Cap processes bitemporal remote-sensing images through a CNN feature extractor and attentive encoder, then uses a caption decoder to predict change descriptions. Its encoder combines hierarchical self-attention with a residual block to form image embeddings.

A. Overview

  • Training pairs of bitemporal images and annotated tokens pass through a CNN extractor, attentive encoder, and decoder; validation and testing generate captions autoregressively from image pairs.The model is optimized with cross-entropy loss between predicted captions and reference tokens.Source: A. Overview · p. 4

B. Feature Extractor

  • A Siamese CNN extracts deep features from both images using shared weights, enabling their features to be compared in subsequent stages.The feature extractor adopts ResNet101 pretrained on ImageNet as its backbone.Source: B. Feature Extractor · pp. 4–5

C. Attentive Encoder

  • The HSA stack uses dual self-attention on individual image features and joint self-attention on concatenated features to retrieve hierarchical representations.Features receive learnable spatial positional embeddings, and residual connections follow the attention units.Source: C. Attentive Encoder · p. 5
  • The ResBlock computes cosine similarity between temporal features, adds it to their concatenation, and processes the result through three convolutional layers to form an image embedding.Each convolutional layer is followed by ReLU.Source: C. Attentive Encoder · pp. 5–6

D. Caption Decoder

The caption decoder uses transformer layers to relate image embeddings to word embeddings while generating change descriptions. Masked attention supports autoregressive word generation, and residual connections preserve input word information through decoding.

D. Caption Decoder

  • Each transformer decoder layer combines masked multi-head self-attention, encoder cross-attention, and a feed-forward network, with residual connections and layer normalization.An additional residual connection carries the word embedding through the decoder layer.Source: D. Caption Decoder · p. 6, Figure 4 · p. 6
  • Token embeddings receive sinusoidal positional embeddings, while a lower-triangular mask prevents attention to future output positions.The mask restricts each prediction to positions already generated.Source: D. Caption Decoder · p. 6
  • Decoder cross-attention uses the preceding masked-attention output as its query and the attentive encoder output as its keys and values.Source: D. Caption Decoder · p. 6
  • A linear layer and softmax produce word probabilities trained with cross-entropy; at test time, generation proceeds from START until END.Source: D. Caption Decoder · p. 7

IV. EXPERIMENTS AND RESULTS

The evaluation compares Chg2Cap with existing remote-sensing and natural-image change-captioning methods on two benchmark remote-sensing datasets.

IV. EXPERIMENTS AND RESULTS

  • The evaluation compares the proposed method with existing remote-sensing and natural-image change-captioning methods.Source: §IV · p. 7
  • The comparison is conducted on two benchmark remote-sensing change-captioning datasets.Source: §IV · p. 7
  • The section presents detailed comparison and evaluation results for the proposed method.Source: §IV · p. 7

1) Dubai-CC Dataset:

The experiments use Dubai-CC and LEVIR-CC, evaluate caption similarity with four standard metrics, and compare models using shared feature-extractor settings.

1) Dubai-CC Dataset:

  • Dubai-CC contains 500 bitemporal tiles with five descriptions each, derived from Landsat 7 images acquired ten years apart.The 50 × 50 tiles are split into 300 training, 50 validation, and 150 test pairs; the vocabulary has 262 words.Source: 1) Dubai-CC Dataset · p. 7

2) LEVIR-CC Dataset:

  • LEVIR-CC contains 10,077 bitemporal tiles across 20 Texas regions, with five descriptions per pair and both changed and unchanged examples.The split is 6,815 training, 1,333 validation, and 1,929 test pairs; its vocabulary contains 497 words.Source: 2) LEVIR-CC Dataset · p. 7

1) Evaluation Metrics:

  • BLEU-N, ROUGE-L, METEOR, and CIDEr-D measure generated-caption consistency with reference sentences, with higher scores indicating greater similarity and accuracy.BLEU-N is reported for N=1, 2, 3, and 4.Source: 1) Evaluation Metrics · p. 7

2) Implementation Details:

  • Comparative methods share a ResNet-101 ImageNet-pretrained backbone, while Chg2Cap uses eight attention heads and 2048-dimensional word and image embeddings.The attentive encoder feed-forward linear layer has 512 channels.Source: 2) Implementation Details · p. 7

C. Parametric Analysis

The analysis selects a shallower transformer decoder with a three-layer HSA encoder and finds accuracy gains from both the HSA and residual components.

C. Parametric Analysis

  • A three-layer HSA encoder and one-layer transformer decoder are the empirically selected Chg2Cap depths, with shallower decoders giving more balanced performance.With decoder depth fixed at one, encoder depth three performs better; excessive decoder complexity may adversely affect word generation.Source: C. Parametric Analysis · p. 8

D. Ablation Studies

  • The ablation study evaluates the HSA and residual blocks in Chg2Cap's attentive encoder on Dubai-CC and LEVIR-CC.The study assesses the effectiveness of both components.Source: D. Ablation Studies · p. 8

1) Effects of the HSA Block:

  • Combining positional embedding, dual self-attention, and joint self-attention gives the HSA block's optimal performance across both datasets, with inference delay under 5 milliseconds per frame.The HSA block improves caption accuracy across Dubai-CC and LEVIR-CC, despite a slight inference-time increase.Source: 1) Effects of the HSA Block · p. 8

2) Effects of the ResBlock:

  • Cosine similarity masking adds no network parameters and improves localization of change-related objects, while the ResBlock enhances captioning performance with a minor inference-time increase.The ResBlock comprises three 2D convolutional layers with ReLU activation.Source: 2) Effects of the ResBlock · p. 8

E. Qualitative Visualization

Qualitative examples show Chg2Cap generating plausible change captions and visual embeddings that highlight changes in Dubai-CC and LEVIR-CC scenes.

1) Caption Generation:

  • On Dubai-CC's 30 m imagery, small land covers may mislead the network, yet examples show it distinguishing changes and producing captions that appear reasonable against human annotations.The visualized embeddings generally locate and highlight changes for the decoder, and unchanged-scene examples show what the network focuses on when recognizing unchanged objects.Source: 1) Caption Generation · p. 9, Figure 5 · p. 10
  • Example predictions closely match references for grass becoming denser and a road appearing, and for a parking lot being built in open space.A pool-water-level reference is captioned as a lake-water-level rise.Source: 1) Caption Generation · p. 10
  • On higher-resolution LEVIR-CC scenes, Chg2Cap shows sensitivity to land covers including vegetation, buildings, water, and roads, with embeddings highlighting changes between image pairs.The visualized embeddings are described as potentially providing useful referential inputs to the caption generator.Source: 1) Caption Generation · pp. 9–10, Figure 6 · p. 10

2) Attention Weights:

Visualized decoder attention varies with land-cover scale and word type, focusing locally on small targets and more broadly on large or widespread categories.

2) Attention Weights:

  • For small Dubai-CC targets such as islands, parks, buildings, and roads, attention weights identify their approximate image locations.The attention maps are overlaid on the time-2 image, with corresponding words marked in blue.Source: 2) Attention Weights · p. 10, Figure 7 · p. 11
  • Attention spreads over a larger image area for large land covers such as sea and desert, reflecting spatial scale differences.The analysis reports that attention can focus on different spatial scales depending on land-cover size.Source: 2) Attention Weights · p. 10
  • In LEVIR-CC, attention focuses on corresponding change regions for object-related words but spreads more widely for terms such as residential, bareland, forest, and vegetation.The authors report that the model attends to salient image areas to generate relevant captions.Source: 2) Attention Weights · p. 10, Figure 8 · p. 11

F. Comparison to the State of the Art

Chg2Cap is compared with natural-image and remote-sensing change-captioning methods on Dubai-CC and LEVIR-CC, outperforming the compared methods on both datasets. The experiments also report gains from the attentive encoder over a baseline without it.

F. Comparison to the State of the Art

V. CONCLUSION

The conclusion presents Chg2Cap as an attentive network for remote-sensing change captioning and reports superior performance on Dubai-CC and LEVIR-CC. The authors also describe its potential applications and identify more complex tasks as a direction for future extension.

V. CONCLUSION

  • Chg2Cap combines a ResNet-101 CNN feature extractor, an attentive encoder with hierarchical self-attention and a ResBlock, and a caption decoder.The paper describes the method as using transformer models in natural language processing.Source: §V · p. 12
  • On Dubai-CC and LEVIR-CC, Chg2Cap achieves superior performance to remote-sensing and natural-image change-captioning methods.Experiments and analyses also demonstrate its effectiveness in capturing changes across buildings, vegetation, water, desert, and roads.Source: §V · p. 12
  • The authors identify extension and optimization for more complex change-captioning tasks as future work.Source: §V · p. 13

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